Search Results for author: Liang Lin

Found 236 papers, 86 papers with code

OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup

1 code implementation3 Jan 2023 Zhijing Yang, Junyang Chen, Yukai Shi, Hao Li, Tianshui Chen, Liang Lin

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities.

Semantic Parsing Virtual Try-on

Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification

no code implementations2 Jan 2023 Ziyi Tang, Ruimao Zhang, Zhanglin Peng, Jinrui Chen, Liang Lin

We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages.

Representation Learning Video-Based Person Re-Identification

UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression

2 code implementations6 Dec 2022 Jiaqi Chen, Tong Li, Jinghui Qin, Pan Lu, Liang Lin, Chongyu Chen, Xiaodan Liang

Naturally, we also present a unified multi-task Geometric Transformer framework, Geoformer, to tackle calculation and proving problems simultaneously in the form of sequence generation, which finally shows the reasoning ability can be improved on both two tasks by unifying formulation.

Logical Reasoning

DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning

no code implementations21 Nov 2022 Ziyi Dong, Pengxu Wei, Liang Lin

Although there are some recent attempts to use fine-tuning or prompt-tuning methods to teach the model a new concept as a new pseudo-word from a given reference image set, these methods are not only still difficult to synthesize diverse and high-quality images without distortion and artifacts, but also suffer from low controllability.

Text to image generation Text-to-Image Generation

Structure-Preserving 3D Garment Modeling with Neural Sewing Machines

no code implementations12 Nov 2022 Xipeng Chen, Guangrun Wang, Dizhong Zhu, Xiaodan Liang, Philip H. S. Torr, Liang Lin

In this paper, we propose a novel Neural Sewing Machine (NSM), a learning-based framework for structure-preserving 3D garment modeling, which is capable of learning representations for garments with diverse shapes and topologies and is successfully applied to 3D garment reconstruction and controllable manipulation.

Representation Learning

Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning

1 code implementation12 Nov 2022 Ziyi Zhang, Weikai Chen, Hui Cheng, Zhen Li, Siyuan Li, Liang Lin, Guanbin Li

We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to the source data.

Contrastive Learning Source-Free Domain Adaptation

OhMG: Zero-shot Open-vocabulary Human Motion Generation

no code implementations28 Oct 2022 Junfan Lin, Jianlong Chang, Lingbo Liu, Guanbin Li, Liang Lin, Qi Tian, Chang-Wen Chen

After that, by formulating the generated poses from the text2pose stage as prompts, the motion generator can generate motions referring to the poses in a controllable and flexible manner.

Language Modelling

Prompt-Matched Semantic Segmentation

no code implementations22 Aug 2022 Lingbo Liu, Jianlong Chang, Bruce X. B. Yu, Liang Lin, Qi Tian, Chang-Wen Chen

Previous methods usually fine-tuned the entire networks for each specific dataset, which will be burdensome to store massive parameters of these networks.

Representation Learning Semantic Segmentation

Accelerating Numerical Solvers for Large-Scale Simulation of Dynamical System via NeurVec

1 code implementation7 Aug 2022 Zhongzhan Huang, Senwei Liang, Hong Zhang, Haizhao Yang, Liang Lin

Conventional numerical solvers used in the simulation are significantly limited by the step size for time integration, which hampers efficiency and feasibility especially when high accuracy is desired.

Robust Real-World Image Super-Resolution against Adversarial Attacks

1 code implementation31 Jul 2022 Jiutao Yue, Haofeng Li, Pengxu Wei, Guanbin Li, Liang Lin

Since the frequency masking may not only destroys the adversarial perturbations but also affects the sharp details in a clean image, we further develop an adversarial sample classifier based on the frequency domain of images to determine if applying the proposed mask module.

Image Super-Resolution

Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering

no code implementations26 Jul 2022 Yang Liu, Guanbin Li, Liang Lin

Existing visual question answering methods tend to capture the cross-modal spurious correlations, and fail to discover the true causal mechanism that facilitates reasoning truthfully based on the dominant visual evidence and the question intention.

Causal Inference Question Answering +2

The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network

no code implementations16 Jul 2022 Zhongzhan Huang, Senwei Liang, Mingfu Liang, wei he, Haizhao Yang, Liang Lin

Recently many plug-and-play self-attention modules (SAMs) are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs).

Crowd Counting

Adversarially-Aware Robust Object Detector

1 code implementation13 Jul 2022 Ziyi Dong, Pengxu Wei, Liang Lin

In this work, we empirically explore the model training for adversarial robustness in object detection, which greatly attributes to the conflict between learning clean images and adversarial images.

Adversarial Robustness object-detection +1

Discourse-Aware Graph Networks for Textual Logical Reasoning

no code implementations4 Jul 2022 Yinya Huang, Lemao Liu, Kun Xu, Meng Fang, Liang Lin, Xiaodan Liang

In this work, we propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs).

graph construction Logical Reasoning +2

Real-World Image Super-Resolution by Exclusionary Dual-Learning

1 code implementation6 Jun 2022 Hao Li, Jinghui Qin, Zhijing Yang, Pengxu Wei, Jinshan Pan, Liang Lin, Yukai Shi

Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials.

Image Restoration Image Super-Resolution

Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels

1 code implementation26 May 2022 Tao Pu, Tianshui Chen, Hefeng Wu, Yongyi Lu, Liang Lin

Despite achieving impressive progress, current multi-label image recognition (MLR) algorithms heavily depend on large-scale datasets with complete labels, making collecting large-scale datasets extremely time-consuming and labor-intensive.

Image Classification

Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels

1 code implementation23 May 2022 Tianshui Chen, Tao Pu, Lingbo Liu, Yukai Shi, Zhijing Yang, Liang Lin

Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and thus facilitate large-scale MLR.

LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning

1 code implementation17 May 2022 Zhicheng Yang, Jinghui Qin, Jiaqi Chen, Liang Lin, Xiaodan Liang

To address this issue and make a step towards interpretable MWP solving, we first construct a high-quality MWP dataset named InterMWP which consists of 11, 495 MWPs and annotates interpretable logical formulas based on algebraic knowledge as the grounded linguistic logic of each solution equation.

Math Word Problem Solving

Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution

1 code implementation CVPR 2022 Xiaoqian Xu, Pengxu Wei, Weikai Chen, Mingzhi Mao, Liang Lin, Guanbin Li

To address this issue, we propose an unsupervised domain adaptation mechanism for real-world SR, named Dual ADversarial Adaptation (DADA), which only requires LR images in the target domain with available real paired data from a source camera.

Image Super-Resolution Unsupervised Domain Adaptation

Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism

1 code implementation CVPR 2022 BinBin Yang, Xinchi Deng, Han Shi, Changlin Li, Gengwei Zhang, Hang Xu, Shen Zhao, Liang Lin, Xiaodan Liang

To make ROSETTA automatically determine which experience is available and useful, a prototypical task correlation guided Gating Diversity Controller(GDC) is introduced to adaptively adjust the diversity of gates for the new task based on class-specific prototypes.

Continual Learning object-detection +1

Audio-Visual Contrastive Learning for Self-supervised Action Recognition

no code implementations28 Apr 2022 Haoyuan Lan, Yang Liu, Liang Lin

To learn supervised information from unlabeled videos, we propose a novel self-supervised contrastive learning module (SelfCL).

Contrastive Learning Self-Supervised Action Recognition

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

no code implementations26 Apr 2022 Yang Liu, Yushen Wei, Hong Yan, Guanbin Li, Liang Lin

Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing.

Out-of-Distribution Generalization Representation Learning +1

Semantic Representation and Dependency Learning for Multi-Label Image Recognition

no code implementations8 Apr 2022 Tao Pu, Mingzhan Sun, Hefeng Wu, Tianshui Chen, Ling Tian, Liang Lin

We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions to regularize the network training.

object-detection Object Detection

Open Set Domain Adaptation By Novel Class Discovery

no code implementations7 Mar 2022 Jingyu Zhuang, Ziliang Chen, Pengxu Wei, Guanbin Li, Liang Lin

In Open Set Domain Adaptation (OSDA), large amounts of target samples are drawn from the implicit categories that never appear in the source domain.

Domain Adaptation Novel Class Discovery

Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels

1 code implementation4 Mar 2022 Tao Pu, Tianshui Chen, Hefeng Wu, Liang Lin

However, these algorithms depend on sufficient multi-label annotations to train the models, leading to poor performance especially with low known label proportion.

Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning

1 code implementation26 Feb 2022 Pengxiang Yan, Ziyi Wu, Mengmeng Liu, Kun Zeng, Liang Lin, Guanbin Li

To relieve the burden of labor-intensive labeling, deep unsupervised SOD methods have been proposed to exploit noisy labels generated by handcrafted saliency methods.

object-detection Object Detection +2

Semantic-Aware Auto-Encoders for Self-Supervised Representation Learning

1 code implementation CVPR 2022 Guangrun Wang, Yansong Tang, Liang Lin, Philip H.S. Torr

Inspired by perceptual learning that could use cross-view learning to perceive concepts and semantics, we propose a novel AE that could learn semantic-aware representation via cross-view image reconstruction.

Image Reconstruction Representation Learning +1

Structured Semantic Transfer for Multi-Label Recognition with Partial Labels

1 code implementation21 Dec 2021 Tianshui Chen, Tao Pu, Hefeng Wu, Yuan Xie, Liang Lin

To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i. e., merely some labels are known while other labels are missing (also called unknown labels) per image.

TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning

2 code implementations7 Dec 2021 Yang Liu, Keze Wang, Lingbo Liu, Haoyuan Lan, Liang Lin

To overcome these limitations, we take advantage of the multi-scale temporal dependencies within videos and proposes a novel video self-supervised learning framework named Temporal Contrastive Graph Learning (TCGL), which jointly models the inter-snippet and intra-snippet temporal dependencies for temporal representation learning with a hybrid graph contrastive learning strategy.

Action Recognition Contrastive Learning +5

Image Comes Dancing with Collaborative Parsing-Flow Video Synthesis

no code implementations27 Oct 2021 Bowen Wu, Zhenyu Xie, Xiaodan Liang, Yubei Xiao, Haoye Dong, Liang Lin

The integration of human parsing and appearance flow effectively guides the generation of video frames with realistic appearance.

Human Parsing Video Generation

Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation

no code implementations16 Oct 2021 Yang Wu, Shirui Feng, Guanbin Li, Liang Lin

PEMR includes a "looking ahead" process, \textit{i. e.} a visual feature extractor module that estimates feasible paths for gathering 3D navigational information, which is mimicking the human sense of direction.

Common Sense Reasoning Embodied Question Answering +1

Road Network Guided Fine-Grained Urban Traffic Flow Inference

no code implementations29 Sep 2021 Lingbo Liu, Mengmeng Liu, Guanbin Li, Ziyi Wu, Liang Lin

Subsequently, we incorporate the road network feature and coarse-grained flow feature to regularize the short-range spatial distribution modeling of road-relative traffic flow.

Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning

no code implementations ICCV 2021 Junkai Huang, Chaowei Fang, Weikai Chen, Zhenhua Chai, Xiaolin Wei, Pengxu Wei, Liang Lin, Guanbin Li

Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data.

Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video

no code implementations9 Aug 2021 Jie Wu, Wei zhang, Guanbin Li, Wenhao Wu, Xiao Tan, YingYing Li, Errui Ding, Liang Lin

In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video.

Anomaly Detection

Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation

1 code implementation23 Jul 2021 Bingqian Lin, Yi Zhu, Yanxin Long, Xiaodan Liang, Qixiang Ye, Liang Lin

Specifically, we propose a Dynamic Reinforced Instruction Attacker (DR-Attacker), which learns to mislead the navigator to move to the wrong target by destroying the most instructive information in instructions at different timesteps.

Vision and Language Navigation Vision-Language Navigation

Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks

1 code implementation ACL 2021 Jinghui Qin, Xiaodan Liang, Yining Hong, Jianheng Tang, Liang Lin

Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions.

Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation

1 code implementation2 Jul 2021 Lingbo Liu, Yuying Zhu, Guanbin Li, Ziyi Wu, Lei Bai, Liang Lin

In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e. g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership.

Time Series Time Series Analysis

Prototypical Graph Contrastive Learning

1 code implementation17 Jun 2021 Shuai Lin, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric Xing, Xiaodan Liang

However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i. e., the negatives likely having the same semantic structure with the query, leading to performance degradation.

Contrastive Learning Representation Learning

Towards Quantifiable Dialogue Coherence Evaluation

1 code implementation ACL 2021 Zheng Ye, Liucun Lu, Lishan Huang, Liang Lin, Xiaodan Liang

To address these limitations, we propose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel framework aiming to train a quantifiable dialogue coherence metric that can reflect the actual human rating standards.

Coherence Evaluation Dialogue Evaluation +1

GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning

1 code implementation Findings (ACL) 2021 Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric P. Xing, Liang Lin

Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 4, 998 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems.

Question Answering

Solving Inefficiency of Self-supervised Representation Learning

1 code implementation ICCV 2021 Guangrun Wang, Keze Wang, Guangcong Wang, Philip H. S. Torr, Liang Lin

In this paper, we reveal two contradictory phenomena in contrastive learning that we call under-clustering and over-clustering problems, which are major obstacles to learning efficiency.

Contrastive Learning Representation Learning +3

Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition

no code implementations31 Mar 2021 Guangrun Wang, Liang Lin, Rongcong Chen, Guangcong Wang, Jiqi Zhang

In this work, we prove that dynamically adapting network architectures tailored for each domain task along with weight finetuning benefits in both efficiency and effectiveness, compared to the existing image recognition pipeline that only tunes the weights regardless of the architecture.

Age Estimation Image Classification +4

Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer

2 code implementations26 Jan 2021 Liang Lin, Yiming Gao, Ke Gong, Meng Wang, Xiaodan Liang

Prior highly-tuned image parsing models are usually studied in a certain domain with a specific set of semantic labels and can hardly be adapted into other scenarios (e. g., sharing discrepant label granularity) without extensive re-training.

Graph Representation Learning Human Parsing +2

Unifying Relational Sentence Generation and Retrieval for Medical Image Report Composition

no code implementations9 Jan 2021 Fuyu Wang, Xiaodan Liang, Lin Xu, Liang Lin

Beyond generating long and topic-coherent paragraphs in traditional captioning tasks, the medical image report composition task poses more task-oriented challenges by requiring both the highly-accurate medical term diagnosis and multiple heterogeneous forms of information including impression and findings.

Retrieval

Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval

no code implementations4 Jan 2021 Yang Liu, Keze Wang, Haoyuan Lan, Liang Lin

To model multi-scale temporal dependencies, our TCGL integrates the prior knowledge about the frame and snippet orders into graph structures, i. e., the intra-/inter- snippet temporal contrastive graphs.

Action Recognition Contrastive Learning +4

Towards a Reliable and Robust Dialogue System for Medical Automatic Diagnosis

no code implementations1 Jan 2021 Junfan Lin, Lin Xu, Ziliang Chen, Liang Lin

To this end, we propose a novel DSMAD agent, INS-DS (Introspective Diagnosis System) comprising of two separate yet cooperative modules, i. e., an inquiry module for proposing symptom-inquiries and an introspective module for deciding when to inform a disease.

Decision Making

Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering

no code implementations ICCV 2021 Qingxing Cao, Wentao Wan, Keze Wang, Xiaodan Liang, Liang Lin

The experimental results show that our proposed method can improve current VQA models on OOD split without losing performance on the in-domain test data.

Novel Concepts Question Answering +1

Adversarial Training using Contrastive Divergence

no code implementations1 Jan 2021 Hongjun Wang, Guanbin Li, Liang Lin

To protect the security of machine learning models against adversarial examples, adversarial training becomes the most popular and powerful strategy against various adversarial attacks by injecting adversarial examples into training data.

CAT-SAC: Soft Actor-Critic with Curiosity-Aware Entropy Temperature

no code implementations1 Jan 2021 Junfan Lin, Changxin Huang, Xiaodan Liang, Liang Lin

The curiosity is added to the target entropy to increase the entropy temperature for unfamiliar states and decrease the target entropy for familiar states.

Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning

no code implementations1 Jan 2021 Fuyu Wang, Pan Zhou, Xiaodan Liang, Liang Lin

To solve this issue, we propose a novel DynamIc Self-sUperviSed Erasure (DISUSE) which adaptively erases redundant and artifactual clues in the context and questions to learn and establish the correct corresponding pair relations between the questions and their clues.

Question Answering Self-Supervised Learning +1

AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition

1 code implementation29 Dec 2020 Tao Pu, Tianshui Chen, Yuan Xie, Hefeng Wu, Liang Lin

In this work, we explore the correlations among the action units and facial expressions, and devise an AU-Expression Knowledge Constrained Representation Learning (AUE-CRL) framework to learn the AU representations without AU annotations and adaptively use representations to facilitate facial expression recognition.

Facial Expression Recognition Representation Learning

REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement

no code implementations24 Dec 2020 Yinya Huang, Meng Fang, Xunlin Zhan, Qingxing Cao, Xiaodan Liang, Liang Lin

It is crucial since the quality of the evidence is the key to answering commonsense questions, and even determines the upper bound on the QA systems performance.

Question Answering

Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation

1 code implementation22 Dec 2020 Shuai Lin, Pan Zhou, Xiaodan Liang, Jianheng Tang, Ruihui Zhao, Ziliang Chen, Liang Lin

Besides, we develop a Graph-Evolving Meta-Learning (GEML) framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease, which effectively alleviates the needs of a large number of dialogues.

Dialogue Generation Meta-Learning

Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding

1 code implementation14 Dec 2020 Qingxing Cao, Bailin Li, Xiaodan Liang, Keze Wang, Liang Lin

Specifically, we generate the question-answer pair based on both the Visual Genome scene graph and an external knowledge base with controlled programs to disentangle the knowledge from other biases.

Question Answering Visual Question Answering

Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp

1 code implementation30 Nov 2020 Junfan Lin, Zhongzhan Huang, Keze Wang, Xiaodan Liang, Weiwei Chen, Liang Lin

Although deep reinforcement learning (RL) has been successfully applied to a variety of robotic control tasks, it's still challenging to apply it to real-world tasks, due to the poor sample efficiency.

Continuous Control

Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation

2 code implementations NeurIPS 2020 Yangxin Wu, Gengwei Zhang, Hang Xu, Xiaodan Liang, Liang Lin

In this work, we propose an efficient, cooperative and highly automated framework to simultaneously search for all main components including backbone, segmentation branches, and feature fusion module in a unified panoptic segmentation pipeline based on the prevailing one-shot Network Architecture Search (NAS) paradigm.

Instance Segmentation Panoptic Segmentation +1

A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning

no code implementations15 Oct 2020 Hongjun Wang, Guanbin Li, Xiaobai Liu, Liang Lin

Although deep convolutional neural networks (CNNs) have demonstrated remarkable performance on multiple computer vision tasks, researches on adversarial learning have shown that deep models are vulnerable to adversarial examples, which are crafted by adding visually imperceptible perturbations to the input images.

Adversarial Attack

Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems

1 code implementation EMNLP 2020 Jinghui Qin, Lihui Lin, Xiaodan Liang, Rumin Zhang, Liang Lin

A practical automatic textual math word problems (MWPs) solver should be able to solve various textual MWPs while most existing works only focused on one-unknown linear MWPs.

Math Word Problem Solving

GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems

1 code implementation EMNLP 2020 Lishan Huang, Zheng Ye, Jinghui Qin, Liang Lin, Xiaodan Liang

Capitalized on the topic-level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation.

Dialogue Evaluation

Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition

no code implementations20 Sep 2020 Tianshui Chen, Liang Lin, Riquan Chen, Xiaolu Hui, Hefeng Wu

The framework exploits prior knowledge to guide adaptive information propagation among different categories to facilitate multi-label analysis and reduce the dependency of training samples.

Few-Shot Learning

Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos

no code implementations18 Sep 2020 Jie Wu, Guanbin Li, Xiaoguang Han, Liang Lin

Temporal grounding of natural language in untrimmed videos is a fundamental yet challenging multimedia task facilitating cross-media visual content retrieval.

reinforcement-learning reinforcement Learning +2

Online Alternate Generator against Adversarial Attacks

no code implementations17 Sep 2020 Haofeng Li, Yirui Zeng, Guanbin Li, Liang Lin, Yizhou Yu

The field of computer vision has witnessed phenomenal progress in recent years partially due to the development of deep convolutional neural networks.

Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition

1 code implementation1 Sep 2020 Yang Liu, Keze Wang, Guanbin Li, Liang Lin

In this paper, we propose a novel framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) by adaptively transferring and distilling the knowledge from multiple wearable sensors.

Action Recognition Image Generation +2

Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View

no code implementations23 Aug 2020 Junpeng Tan, Yukai Shi, Zhijing Yang, Caizhen Wen, Liang Lin

To ensure that we achieve effective sparse representation and clustering performance on the original data matrix, adaptive graph regularization and unsupervised clustering constraints are also incorporated in the proposed model to preserve the internal structural features of the data.

Component Divide-and-Conquer for Real-World Image Super-Resolution

1 code implementation ECCV 2020 Pengxu Wei, Ziwei Xie, Hannan Lu, Zongyuan Zhan, Qixiang Ye, WangMeng Zuo, Liang Lin

Learning an SR model with conventional pixel-wise loss usually is easily dominated by flat regions and edges, and fails to infer realistic details of complex textures.

Image Super-Resolution

Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition

1 code implementation3 Aug 2020 Yuan Xie, Tianshui Chen, Tao Pu, Hefeng Wu, Liang Lin

However, most of these works focus on holistic feature adaptation, and they ignore local features that are more transferable across different datasets.

Facial Expression Recognition

Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning

1 code implementation3 Aug 2020 Tianshui Chen, Tao Pu, Hefeng Wu, Yuan Xie, Lingbo Liu, Liang Lin

Although each declares to achieve superior performance, fair comparisons are lacking due to the inconsistent choices of the source/target datasets and feature extractors.

Domain Adaptation Facial Expression Recognition +2

Fine-Grained Image Captioning with Global-Local Discriminative Objective

1 code implementation21 Jul 2020 Jie Wu, Tianshui Chen, Hefeng Wu, Zhi Yang, Guangchun Luo, Liang Lin

This is primarily due to (i) the conservative characteristic of traditional training objectives that drives the model to generate correct but hardly discriminative captions for similar images and (ii) the uneven word distribution of the ground-truth captions, which encourages generating highly frequent words/phrases while suppressing the less frequent but more concrete ones.

Image Captioning Retrieval

EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning

1 code implementation ECCV 2020 Bailin Li, Bowen Wu, Jiang Su, Guangrun Wang, Liang Lin

Many algorithms try to predict model performance of the pruned sub-nets by introducing various evaluation methods.

Network Pruning

Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning

no code implementations24 Apr 2020 Zhongzhan Huang, Wenqi Shao, Xinjiang Wang, Liang Lin, Ping Luo

Channel pruning is a popular technique for compressing convolutional neural networks (CNNs), where various pruning criteria have been proposed to remove the redundant filters.

Bidirectional Graph Reasoning Network for Panoptic Segmentation

no code implementations CVPR 2020 Yangxin Wu, Gengwei Zhang, Yiming Gao, Xiajun Deng, Ke Gong, Xiaodan Liang, Liang Lin

We introduce a Bidirectional Graph Reasoning Network (BGRNet), which incorporates graph structure into the conventional panoptic segmentation network to mine the intra-modular and intermodular relations within and between foreground things and background stuff classes.

Instance Segmentation Panoptic Segmentation

Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking

1 code implementation CVPR 2020 Hongjun Wang, Guangrun Wang, Ya Li, Dongyu Zhang, Liang Lin

To examine the robustness of ReID systems is rather important because the insecurity of ReID systems may cause severe losses, e. g., the criminals may use the adversarial perturbations to cheat the CCTV systems.

Adversarial Attack Person Re-Identification

Efficient Crowd Counting via Structured Knowledge Transfer

2 code implementations23 Mar 2020 Lingbo Liu, Jiaqi Chen, Hefeng Wu, Tianshui Chen, Guanbin Li, Liang Lin

Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications.

Crowd Counting Transfer Learning

Linguistically Driven Graph Capsule Network for Visual Question Reasoning

no code implementations23 Mar 2020 Qingxing Cao, Xiaodan Liang, Keze Wang, Liang Lin

Inspired by the property of a capsule network that can carve a tree structure inside a regular convolutional neural network (CNN), we propose a hierarchical compositional reasoning model called the "Linguistically driven Graph Capsule Network", where the compositional process is guided by the linguistic parse tree.

Question Answering Visual Question Answering

Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis

1 code implementation14 Mar 2020 Junfan Lin, Keze Wang, Ziliang Chen, Xiaodan Liang, Liang Lin

To eliminate this bias and inspired by the propensity score matching technique with causal diagram, we propose a propensity-based patient simulator to effectively answer unrecorded inquiry by drawing knowledge from the other records; Bias (ii) inherently comes along with the passively collected data, and is one of the key obstacles for training the agent towards "learning how" rather than "remembering what".

Medical Diagnosis

DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution

1 code implementation25 Feb 2020 Yukai Shi, Haoyu Zhong, Zhijing Yang, Xiaojun Yang, Liang Lin

Previous image SR methods fail to exhibit similar performance on Real-SR as the image data is not aligned inherently.

Image Super-Resolution

Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread

no code implementations22 Jan 2020 Haofeng Li, Guanbin Li, BinBin Yang, Guanqi Chen, Liang Lin, Yizhou Yu

The proposed algorithm for the first time achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.

Image Classification object-detection +3

Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction

2 code implementations14 Jan 2020 Lingbo Liu, Jingwen Chen, Hefeng Wu, Jiajie Zhen, Guanbin Li, Liang Lin

To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs.

Representation Learning

An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation

no code implementations18 Dec 2019 Jihan Yang, Ruijia Xu, Ruiyu Li, Xiaojuan Qi, Xiaoyong Shen, Guanbin Li, Liang Lin

In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations.

Semantic Segmentation Unsupervised Domain Adaptation

Knowledge Graph Transfer Network for Few-Shot Recognition

1 code implementation21 Nov 2019 Riquan Chen, Tianshui Chen, Xiaolu Hui, Hefeng Wu, Guanbin Li, Liang Lin

In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN).

Few-Shot Learning Novel Concepts +1

Generalizing Energy-based Generative ConvNets from Particle Evolution Perspective

no code implementations31 Oct 2019 Yang Wu, Xu Cai, Pengxu Wei, Guanbin Li, Liang Lin

Compared with Generative Adversarial Networks (GAN), Energy-Based generative Models (EBMs) possess two appealing properties: i) they can be directly optimized without requiring an auxiliary network during the learning and synthesizing; ii) they can better approximate underlying distribution of the observed data by learning explicitly potential functions.

Layout-Graph Reasoning for Fashion Landmark Detection

no code implementations CVPR 2019 Weijiang Yu, Xiaodan Liang, Ke Gong, Chenhan Jiang, Nong Xiao, Liang Lin

Each Layout-Graph Reasoning(LGR) layer aims to map feature representations into structural graph nodes via a Map-to-Node module, performs reasoning over structural graph nodes to achieve global layout coherency via a layout-graph reasoning module, and then maps graph nodes back to enhance feature representations via a Node-to-Map module.

Graph Clustering

Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning

no code implementations28 Sep 2019 Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang, Xiaodan Liang, Liang Lin

Resembling the rapid learning capability of human, few-shot learning empowers vision systems to understand new concepts by training with few samples.

Few-Shot Learning Few-Shot Object Detection +2

Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network

no code implementations23 Sep 2019 Qingxing Cao, Bailin Li, Xiaodan Liang, Liang Lin

Explanation and high-order reasoning capabilities are crucial for real-world visual question answering with diverse levels of inference complexity (e. g., what is the dog that is near the girl playing with?)

Question Answering Visual Question Answering

Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction

2 code implementations2 Sep 2019 Lingbo Liu, Jiajie Zhen, Guanbin Li, Geng Zhan, Zhaocheng He, Bowen Du, Liang Lin

Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference.

Representation Learning Traffic Prediction

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid

no code implementations ICCV 2019 Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang

To address this issue, we propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which learns similarities between a query and a gallery cloth by using both global and local representations in multiple scales.

Image Retrieval Retrieval

Crowd Counting with Deep Structured Scale Integration Network

no code implementations ICCV 2019 Lingbo Liu, Zhilin Qiu, Guanbin Li, Shufan Liu, Wanli Ouyang, Liang Lin

Automatic estimation of the number of people in unconstrained crowded scenes is a challenging task and one major difficulty stems from the huge scale variation of people.

Crowd Counting Representation Learning

Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition

2 code implementations ICCV 2019 Tianshui Chen, Muxin Xu, Xiaolu Hui, Hefeng Wu, Liang Lin

Recognizing multiple labels of images is a practical and challenging task, and significant progress has been made by searching semantic-aware regions and modeling label dependency.

Graph Representation Learning Multi-Label Classification

Semi-Supervised Video Salient Object Detection Using Pseudo-Labels

1 code implementation ICCV 2019 Pengxiang Yan, Guanbin Li, Yuan Xie, Zhen Li, Chuan Wang, Tianshui Chen, Liang Lin

Specifically, we present an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module.

 Ranked #1 on Video Salient Object Detection on VOS-T (using extra training data)

object-detection Salient Object Detection +2

Learning Compact Target-Oriented Feature Representations for Visual Tracking

no code implementations5 Aug 2019 Chenglong Li, Yan Huang, Liang Wang, Jin Tang, Liang Lin

Many state-of-the-art trackers usually resort to the pretrained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected.

Visual Tracking

Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks

1 code implementation CVPR 2019 Ziliang Chen, Jingyu Zhuang, Xiaodan Liang, Liang Lin

(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training.

Multi-target Domain Adaptation Unsupervised Domain Adaptation

Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching

1 code implementation8 Jul 2019 Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin

A broad range of cross-$m$-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs).

Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction

no code implementations15 May 2019 Lingbo Liu, Zhilin Qiu, Guanbin Li, Qing Wang, Wanli Ouyang, Liang Lin

Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs.

Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning

no code implementations4 May 2019 Yukai Shi, Guanbin Li, Qingxing Cao, Keze Wang, Liang Lin

Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution~(LR) input.

Face Hallucination reinforcement-learning +2

Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition

no code implementations22 Apr 2019 Guanbin Li, Xin Zhu, Yirui Zeng, Qing Wang, Liang Lin

Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various facial expressions, we organize the facial AUs in the form of structured knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a multi-scale CNN framework to propagate node information through the graph for generating enhanced AU representation.

Facial Action Unit Detection Representation Learning

Graphonomy: Universal Human Parsing via Graph Transfer Learning

1 code implementation CVPR 2019 Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin

By distilling universal semantic graph representation to each specific task, Graphonomy is able to predict all levels of parsing labels in one system without piling up the complexity.

Human Parsing Transfer Learning

Adaptively Connected Neural Networks

1 code implementation CVPR 2019 Guangrun Wang, Keze Wang, Liang Lin

This paper presents a novel adaptively connected neural network (ACNet) to improve the traditional convolutional neural networks (CNNs) {in} two aspects.

Document Classification Image Classification +1

Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation

no code implementations CVPR 2019 Xipeng Chen, Kwan-Yee Lin, Wentao Liu, Chen Qian, Xiaogang Wang, Liang Lin

Recent studies have shown remarkable advances in 3D human pose estimation from monocular images, with the help of large-scale in-door 3D datasets and sophisticated network architectures.

3D Human Pose Estimation

Knowledge-Embedded Routing Network for Scene Graph Generation

3 code implementations CVPR 2019 Tianshui Chen, Weihao Yu, Riquan Chen, Liang Lin

More specifically, we show that the statistical correlations between objects appearing in images and their relationships, can be explicitly represented by a structured knowledge graph, and a routing mechanism is learned to propagate messages through the graph to explore their interactions.

Graph Generation Scene Graph Generation

End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis

1 code implementation30 Jan 2019 Lin Xu, Qixian Zhou, Ke Gong, Xiaodan Liang, Jianheng Tang, Liang Lin

Besides the challenges for conversational dialogue systems (e. g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations.

Decision Making Dialogue Management +5

3D Human Pose Machines with Self-supervised Learning

2 code implementations arXiv.org 2019 Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, Pengxu Wei

Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests.

3D Human Pose Estimation Self-Supervised Learning

SNAS: Stochastic Neural Architecture Search

2 code implementations ICLR 2019 Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin

In experiments on CIFAR-10, SNAS takes less epochs to find a cell architecture with state-of-the-art accuracy than non-differentiable evolution-based and reinforcement-learning-based NAS, which is also transferable to ImageNet.

Neural Architecture Search reinforcement-learning +1

Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning

no code implementations10 Dec 2018 Lingbo Liu, Guanbin Li, Yuan Xie, Yizhou Yu, Qing Wang, Liang Lin

In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network~(BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings.

Face Alignment Face Detection +2

FRAME Revisited: An Interpretation View Based on Particle Evolution

no code implementations4 Dec 2018 Xu Cai, Yang Wu, Guanbin Li, Ziliang Chen, Liang Lin

FRAME (Filters, Random fields, And Maximum Entropy) is an energy-based descriptive model that synthesizes visual realism by capturing mutual patterns from structural input signals.

Kalman Normalization: Normalizing Internal Representations Across Network Layers

no code implementations NeurIPS 2018 Guangrun Wang, Jiefeng Peng, Ping Luo, Xinjiang Wang, Liang Lin

In this paper, we present a novel normalization method, called Kalman Normalization (KN), for improving and accelerating the training of DNNs, particularly under the context of micro-batches.

object-detection Object Detection

Symbolic Graph Reasoning Meets Convolutions

1 code implementation NeurIPS 2018 Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing

To cooperate with local convolutions, each SGR is constituted by three modules: a) a primal local-to-semantic voting module where the features of all symbolic nodes are generated by voting from local representations; b) a graph reasoning module propagates information over knowledge graph to achieve global semantic coherency; c) a dual semantic-to-local mapping module learns new associations of the evolved symbolic nodes with local representations, and accordingly enhances local features.

Image Classification Semantic Segmentation

Cross-Modal Attentional Context Learning for RGB-D Object Detection

no code implementations30 Oct 2018 Guanbin Li, Yukang Gan, Hejun Wu, Nong Xiao, Liang Lin

In this paper, we address this problem by developing a Cross-Modal Attentional Context (CMAC) learning framework, which enables the full exploitation of the context information from both RGB and depth data.

Autonomous Driving object-detection +1

Hybrid Knowledge Routed Modules for Large-scale Object Detection

1 code implementation NeurIPS 2018 Chenhan Jiang, Hang Xu, Xiangdan Liang, Liang Lin

The dominant object detection approaches treat the recognition of each region separately and overlook crucial semantic correlations between objects in one scene.

object-detection Object Detection

Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview

no code implementations10 Oct 2018 Lili Huang, Jiefeng Peng, Ruimao Zhang, Guanbin Li, Liang Lin

Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the field of computer vision.

Representation Learning Semantic Segmentation

Teaching to Teach by Structured Dark Knowledge

no code implementations27 Sep 2018 Ziliang Chen, Keze Wang, Liang Lin

We evaluate T2T across different learners, teachers, and tasks, which significantly demonstrates that structured knowledge can be inherited by the teachers to further benefit learners' training.

Interpretable Visual Question Answering by Reasoning on Dependency Trees

no code implementations6 Sep 2018 Qingxing Cao, Bailin Li, Xiaodan Liang, Liang Lin

Collaborative reasoning for understanding image-question pairs is a very critical but underexplored topic in interpretable visual question answering systems.

Question Answering Visual Question Answering

Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks

1 code implementation3 Sep 2018 Yuan Yuan, Siyuan Liu, Jiawei Zhang, Yongbing Zhang, Chao Dong, Liang Lin

We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.

Image Super-Resolution Image-to-Image Translation +2

Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement

no code implementations ECCV 2018 Yukang Gan, Xiangyu Xu, Wenxiu Sun, Liang Lin

While significant progress has been made in monocular depth estimation with Convolutional Neural Networks (CNNs) extracting absolute features, such as edges and textures, the depth constraint of neighboring pixels, namely relative features, has been mostly ignored by recent methods.

Monocular Depth Estimation Stereo Matching +1

Attentive Crowd Flow Machines

no code implementations1 Sep 2018 Lingbo Liu, Ruimao Zhang, Jiefeng Peng, Guanbin Li, Bowen Du, Liang Lin

Traffic flow prediction is crucial for urban traffic management and public safety.

Management

Generative Semantic Manipulation with Mask-Contrasting GAN

no code implementations ECCV 2018 Xiaodan Liang, Hao Zhang, Liang Lin, Eric Xing

Despite the promising results on paired/unpaired image-to-image translation achieved by Generative Adversarial Networks (GANs), prior works often only transfer the low-level information (e. g. color or texture changes), but fail to manipulate high-level semantic meanings (e. g., geometric structure or content) of different object regions.

Image-to-Image Translation

Neural Task Planning with And-Or Graph Representations

no code implementations25 Aug 2018 Tianshui Chen, Riquan Chen, Lin Nie, Xiaonan Luo, Xiaobai Liu, Liang Lin

This paper focuses on semantic task planning, i. e., predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer vision research.

Common Sense Reasoning

Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding

1 code implementation14 Aug 2018 Tianshui Chen, Wenxi Wu, Yuefang Gao, Le Dong, Xiaonan Luo, Liang Lin

In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework.

Fine-Grained Image Classification Fine-Grained Image Recognition +1

Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

no code implementations4 Aug 2018 Guanbin Li, Xiang He, Wei zhang, Huiyou Chang, Le Dong, Liang Lin

Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks.

Adaptive Temporal Encoding Network for Video Instance-level Human Parsing

1 code implementation2 Aug 2018 Qixian Zhou, Xiaodan Liang, Ke Gong, Liang Lin

Beyond the existing single-person and multiple-person human parsing tasks in static images, this paper makes the first attempt to investigate a more realistic video instance-level human parsing that simultaneously segments out each person instance and parses each instance into more fine-grained parts (e. g., head, leg, dress).

Human Parsing Semantic Segmentation +3

Instance-level Human Parsing via Part Grouping Network

1 code implementation ECCV 2018 Ke Gong, Xiaodan Liang, Yicheng Li, Yimin Chen, Ming Yang, Liang Lin

Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.

Edge Detection Human Parsing +2

Toward Characteristic-Preserving Image-based Virtual Try-On Network

5 code implementations ECCV 2018 Bochao Wang, Huabin Zheng, Xiaodan Liang, Yimin Chen, Liang Lin, Meng Yang

Second, to alleviate boundary artifacts of warped clothes and make the results more realistic, we employ a Try-On Module that learns a composition mask to integrate the warped clothes and the rendered image to ensure smoothness.

Geometric Matching Virtual Try-on

SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification

no code implementations16 Jul 2018 Ruimao Zhang, Hongbin Sun, Jingyu Li, Yuying Ge, Liang Lin, Ping Luo, Xiaogang Wang

To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN).

Video-Based Person Re-Identification

Crowd Counting using Deep Recurrent Spatial-Aware Network

no code implementations2 Jul 2018 Lingbo Liu, Hongjun Wang, Guanbin Li, Wanli Ouyang, Liang Lin

Crowd counting from unconstrained scene images is a crucial task in many real-world applications like urban surveillance and management, but it is greatly challenged by the camera's perspective that causes huge appearance variations in people's scales and rotations.

Crowd Counting Management

Deep Reasoning with Knowledge Graph for Social Relationship Understanding

1 code implementation2 Jul 2018 Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin

And this structured knowledge can be efficiently integrated into the deep neural network architecture to promote social relationship understanding by an end-to-end trainable Graph Reasoning Model (GRM), in which a propagation mechanism is learned to propagate node message through the graph to explore the interaction between persons of interest and the contextual objects.

Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria

1 code implementation30 Jun 2018 Keze Wang, Liang Lin, Xiaopeng Yan, Ziliang Chen, Dongyu Zhang, Lei Zhang

The proposed process can be compatible with mini-batch based training (i. e., using a batch of unlabeled or partially labeled data as a one-time input) for object detection.

Active Learning object-detection +2

Interpretable Video Captioning via Trajectory Structured Localization

no code implementations CVPR 2018 Xian Wu, Guanbin Li, Qingxing Cao, Qingge Ji, Liang Lin

Automatically describing open-domain videos with natural language are attracting increasing interest in the field of artificial intelligence.

Image Captioning Video Captioning +1

DRPose3D: Depth Ranking in 3D Human Pose Estimation

no code implementations23 May 2018 Min Wang, Xipeng Chen, Wentao Liu, Chen Qian, Liang Lin, Lizhuang Ma

In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation.

3D Human Pose Estimation 3D Pose Estimation

Multi-level Wavelet-CNN for Image Restoration

5 code implementations18 May 2018 Pengju Liu, Hongzhi Zhang, Kai Zhang, Liang Lin, WangMeng Zuo

With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.

Image Denoising Image Super-Resolution +2

Learning Warped Guidance for Blind Face Restoration

1 code implementation ECCV 2018 Xiaoming Li, Ming Liu, Yuting Ye, WangMeng Zuo, Liang Lin, Ruigang Yang

For better recovery of fine facial details, we modify the problem setting by taking both the degraded observation and a high-quality guided image of the same identity as input to our guided face restoration network (GFRNet).

Blind Face Restoration

Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark

3 code implementations5 Apr 2018 Xiaodan Liang, Ke Gong, Xiaohui Shen, Liang Lin

To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality.

Human Parsing Pose Estimation +1

Visual Question Reasoning on General Dependency Tree

no code implementations CVPR 2018 Qingxing Cao, Xiaodan Liang, Bailing Li, Guanbin Li, Liang Lin

This network comprises of two collaborative modules: i) an adversarial attention module to exploit the local visual evidence for each word parsed from the question; ii) a residual composition module to compose the previously mined evidence.

Question Answering Visual Question Answering

Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection

no code implementations CVPR 2018 Keze Wang, Xiaopeng Yan, Dongyu Zhang, Lei Zhang, Liang Lin

Though quite challenging, leveraging large-scale unlabeled or partially labeled images in a cost-effective way has increasingly attracted interests for its great importance to computer vision.

Active Learning object-detection +1

Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains

1 code implementation CVPR 2018 Jiahao Pang, Wenxiu Sun, Chengxi Yang, Jimmy Ren, Ruichao Xiao, Jin Zeng, Liang Lin

By feeding real stereo pairs of different domains to stereo models pre-trained with synthetic data, we see that: i) a pre-trained model does not generalize well to the new domain, producing artifacts at boundaries and ill-posed regions; however, ii) feeding an up-sampled stereo pair leads to a disparity map with extra details.

Stereo Matching Stereo Matching Hand

Weakly Supervised Salient Object Detection Using Image Labels

no code implementations17 Mar 2018 Guanbin Li, Yuan Xie, Liang Lin

Our algorithm is based on alternately exploiting a graphical model and training a fully convolutional network for model updating.

object-detection RGB Salient Object Detection +3

Single View Stereo Matching

1 code implementation CVPR 2018 Yue Luo, Jimmy Ren, Mude Lin, Jiahao Pang, Wenxiu Sun, Hongsheng Li, Liang Lin

The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data.

Monocular Depth Estimation Stereo Matching +1

Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift

1 code implementation CVPR 2018 Ruijia Xu, Ziliang Chen, WangMeng Zuo, Junjie Yan, Liang Lin

Motivated by the theoretical results in \cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the multi-source unsupervised domain adaptation via DCTN is then performed as two alternating steps: i) It deploys multi-way adversarial learning to minimize the discrepancy between the target and each of the multiple source domains, which also obtains the source-specific perplexity scores to denote the possibilities that a target sample belongs to different source domains.

Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation