1 code implementation • 3 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.
no code implementations • 2 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
2 code implementations • 6 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.
no code implementations • 21 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.
no code implementations • 15 Nov 2022 • Tao Pu, Qianru Lao, Hefeng Wu, Tianshui Chen, Liang Lin
To reject noisy labels, recent works regard large loss samples as noise but ignore the semantic correlation different multi-label images.
no code implementations • 12 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.
1 code implementation • 12 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.
no code implementations • 28 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.
no code implementations • 27 Oct 2022 • Zhongzhan Huang, Senwei Liang, Mingfu Liang, Weiling He, Liang Lin
Attention networks have successfully boosted accuracy in various vision problems.
no code implementations • 22 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.
1 code implementation • 7 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.
1 code implementation • 31 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.
no code implementations • 26 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.
no code implementations • 16 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).
1 code implementation • 13 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.
no code implementations • 4 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).
1 code implementation • 6 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.
1 code implementation • 26 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.
1 code implementation • 23 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.
1 code implementation • 17 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.
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.
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.
no code implementations • 28 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).
no code implementations • 26 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
no code implementations • 8 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.
no code implementations • 7 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.
1 code implementation • 4 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.
1 code implementation • 26 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.
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.
1 code implementation • 21 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.
2 code implementations • 7 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.
no code implementations • 30 Nov 2021 • Lingbo Liu, Zewei Yang, Guanbin Li, Kuo Wang, Tianshui Chen, Liang Lin
Land remote sensing analysis is a crucial research in earth science.
no code implementations • 8 Nov 2021 • Junying Huang, Fan Chen, Keze Wang, Liang Lin, Dongyu Zhang
Aiming at recognizing the samples from novel categories with few reference samples, few-shot learning (FSL) is a challenging problem.
no code implementations • 27 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.
no code implementations • 16 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.
no code implementations • 29 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.
no code implementations • Findings (EMNLP) 2021 • Guolin Zheng, Yubei Xiao, Ke Gong, Pan Zhou, Xiaodan Liang, Liang Lin
Specifically, we unify a pre-trained acoustic model (wav2vec 2. 0) and a language model (BERT) into an end-to-end trainable framework.
1 code implementation • ICCV 2021 • Jiefeng Peng, Jiqi Zhang, Changlin Li, Guangrun Wang, Xiaodan Liang, Liang Lin
We attribute this ranking correlation problem to the supernet training consistency shift, including feature shift and parameter shift.
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.
no code implementations • 9 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.
1 code implementation • 23 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.
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.
1 code implementation • 2 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.
1 code implementation • 17 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.
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.
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.
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.
Ranked #1 on
Self-Supervised Person Re-Identification
on SYSU-30k
no code implementations • 31 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.
no code implementations • 1 Feb 2021 • Yukai Shi, Sen Zhang, Chenxing Zhou, Xiaodan Liang, Xiaojun Yang, Liang Lin
Non-parallel text style transfer has attracted increasing research interests in recent years.
2 code implementations • 26 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.
no code implementations • 9 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.
no code implementations • 4 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.
no code implementations • 1 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.
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.
no code implementations • 1 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.
no code implementations • 1 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.
no code implementations • 1 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.
1 code implementation • 29 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.
no code implementations • 24 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.
1 code implementation • 22 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.
no code implementations • 22 Dec 2020 • Yubei Xiao, Ke Gong, Pan Zhou, Guolin Zheng, Xiaodan Liang, Liang Lin
When sampling tasks in MML-ASR, AMS adaptively determines the task sampling probability for each source language.
1 code implementation • 14 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.
1 code implementation • CVPR 2021 • Lingbo Liu, Jiaqi Chen, Hefeng Wu, Guanbin Li, Chenglong Li, Liang Lin
Extensive experiments conducted on the RGBT-CC benchmark demonstrate the effectiveness of our framework for RGBT crowd counting.
1 code implementation • 30 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.
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.
no code implementations • 15 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.
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.
Ranked #9 on
Math Word Problem Solving
on Math23K
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.
no code implementations • 25 Sep 2020 • Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, WangMeng Zuo, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Tangxin Xie, Liang Cao, Yan Zou, Yi Shen, Jialiang Zhang, Yu Jia, Kaihua Cheng, Chenhuan Wu, Yue Lin, Cen Liu, Yunbo Peng, Xueyi Zou, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Tongtong Zhao, Shanshan Zhao, Yoseob Han, Byung-Hoon Kim, JaeHyun Baek, HaoNing Wu, Dejia Xu, Bo Zhou, Wei Guan, Xiaobo Li, Chen Ye, Hao Li, Yukai Shi, Zhijing Yang, Xiaojun Yang, Haoyu Zhong, Xin Li, Xin Jin, Yaojun Wu, Yingxue Pang, Sen Liu, Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Marie-Paule Cani, Wan-Chi Siu, Yuanbo Zhou, Rao Muhammad Umer, Christian Micheloni, Xiaofeng Cong, Rajat Gupta, Keon-Hee Ahn, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee, Feras Almasri, Thomas Vandamme, Olivier Debeir
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
no code implementations • 20 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.
no code implementations • 18 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.
1 code implementation • ECCV 2020 • Ganlong Zhao, Guanbin Li, Ruijia Xu, Liang Lin
Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance.
no code implementations • 17 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.
1 code implementation • 1 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.
no code implementations • 23 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.
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.
1 code implementation • 3 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.
1 code implementation • 3 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.
1 code implementation • 21 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.
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.
Ranked #5 on
Network Pruning
on ImageNet
no code implementations • 3 May 2020 • Kai Zhang, Shuhang Gu, Radu Timofte, Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo, Younghyun Jo, Sejong Yang, Seon Joo Kim, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Jing Liu, Kwangjin Yoon, Taegyun Jeon, Kazutoshi Akita, Takeru Ooba, Norimichi Ukita, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Dongliang He, Wenhao Wu, Yukang Ding, Chao Li, Fu Li, Shilei Wen, Jianwei Li, Fuzhi Yang, Huan Yang, Jianlong Fu, Byung-Hoon Kim, JaeHyun Baek, Jong Chul Ye, Yuchen Fan, Thomas S. Huang, Junyeop Lee, Bokyeung Lee, Jungki Min, Gwantae Kim, Kanghyu Lee, Jaihyun Park, Mykola Mykhailych, Haoyu Zhong, Yukai Shi, Xiaojun Yang, Zhijing Yang, Liang Lin, Tongtong Zhao, Jinjia Peng, Huibing Wang, Zhi Jin, Jiahao Wu, Yifu Chen, Chenming Shang, Huanrong Zhang, Jeongki Min, Hrishikesh P. S, Densen Puthussery, Jiji C. V
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
no code implementations • 24 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.
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.
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.
2 code implementations • 23 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.
no code implementations • 23 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.
1 code implementation • 14 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".
1 code implementation • 25 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.
no code implementations • 22 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.
1 code implementation • 18 Jan 2020 • Jie Wu, Guanbin Li, Si Liu, Liang Lin
Temporally language grounding in untrimmed videos is a newly-raised task in video understanding.
2 code implementations • 14 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.
no code implementations • 18 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.
1 code implementation • 29 Nov 2019 • Changlin Li, Jiefeng Peng, Liuchun Yuan, Guangrun Wang, Xiaodan Liang, Liang Lin, Xiaojun Chang
Moreover, we find that the knowledge of a network model lies not only in the network parameters but also in the network architecture.
Ranked #1 on
Neural Architecture Search
on CIFAR-100
1 code implementation • 21 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).
no code implementations • 31 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.
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.
no code implementations • 28 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.
Ranked #15 on
Few-Shot Object Detection
on MS-COCO (30-shot)
no code implementations • 23 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?)
2 code implementations • 2 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.
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.
Ranked #4 on
Image Retrieval
on DeepFashion - Consumer-to-shop
(Rank-1 metric)
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.
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.
Ranked #7 on
Multi-Label Classification
on PASCAL VOC 2007
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)
no code implementations • 5 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.
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.
Ranked #2 on
Multi-target Domain Adaptation
on Office-Home
Multi-target Domain Adaptation
Unsupervised Domain Adaptation
1 code implementation • 8 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).
no code implementations • 15 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.
no code implementations • 4 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.
no code implementations • 22 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.
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.
1 code implementation • 8 Apr 2019 • Guangrun Wang, Guangcong Wang, Xujie Zhang, Jian-Huang Lai, Zhengtao Yu, Liang Lin
Learning a Re-ID model with bag-level annotation is called the weakly supervised Re-ID problem.
Ranked #2 on
Person Re-Identification
on SYSU-30k
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.
Ranked #1 on
Document Classification
on Cora
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.
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.
Ranked #7 on
Scene Graph Generation
on Visual Genome
1 code implementation • 30 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.
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.
Ranked #218 on
3D Human Pose Estimation
on Human3.6M
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.
Ranked #23 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
no code implementations • ICLR 2019 • Sirui Xie, Junning Huang, Lanxin Lei, Chunxiao Liu, Zheng Ma, Wei zhang, Liang Lin
Reinforcement learning agents need exploratory behaviors to escape from local optima.
no code implementations • 10 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.
no code implementations • 4 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.
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.
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.
Ranked #79 on
Semantic Segmentation
on ADE20K val
3 code implementations • ICCV 2019 • Ruijia Xu, Guanbin Li, Jihan Yang, Liang Lin
Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions.
Ranked #6 on
Domain Adaptation
on ImageCLEF-DA
no code implementations • 30 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.
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.
no code implementations • 10 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.
no code implementations • 3 Oct 2018 • Andrey Ignatov, Radu Timofte, Thang Van Vu, Tung Minh Luu, Trung X. Pham, Cao Van Nguyen, Yongwoo Kim, Jae-Seok Choi, Munchurl Kim, Jie Huang, Jiewen Ran, Chen Xing, Xingguang Zhou, Pengfei Zhu, Mingrui Geng, Yawei Li, Eirikur Agustsson, Shuhang Gu, Luc van Gool, Etienne de Stoutz, Nikolay Kobyshev, Kehui Nie, Yan Zhao, Gen Li, Tong Tong, Qinquan Gao, Liu Hanwen, Pablo Navarrete Michelini, Zhu Dan, Hu Fengshuo, Zheng Hui, Xiumei Wang, Lirui Deng, Rang Meng, Jinghui Qin, Yukai Shi, Wushao Wen, Liang Lin, Ruicheng Feng, Shixiang Wu, Chao Dong, Yu Qiao, Subeesh Vasu, Nimisha Thekke Madam, Praveen Kandula, A. N. Rajagopalan, Jie Liu, Cheolkon Jung
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
no code implementations • 27 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.
no code implementations • 6 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.
1 code implementation • 3 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.
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.
no code implementations • 1 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.
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.
no code implementations • 25 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.
1 code implementation • 14 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.
Ranked #47 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Image Recognition
+1
no code implementations • 4 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.
1 code implementation • 2 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).
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.
Ranked #3 on
Human Part Segmentation
on CIHP
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.
no code implementations • 16 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).
no code implementations • 2 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.
1 code implementation • 2 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.
no code implementations • 2 Jul 2018 • Tianshui Chen, Liang Lin, Riquan Chen, Yang Wu, Xiaonan Luo
Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions.
Fine-Grained Image Classification
Fine-Grained Image Recognition
+1
1 code implementation • 30 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.
no code implementations • CVPR 2018 • Guanbin Li, Yuan Xie, Tianhao Wei, Keze Wang, Liang Lin
Image saliency detection has recently witnessed significant progress due to deep convolutional neural networks.
Ranked #2 on
Video Salient Object Detection
on DAVSOD-Difficult20
(using extra training data)
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.
no code implementations • 23 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.
5 code implementations • 18 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.
Ranked #1 on
Grayscale Image Denoising
on Set12 sigma25
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).
Ranked #1 on
Image Super-Resolution
on WebFace - 8x upscaling
2 code implementations • CVPR 2018 • Ke Yu, Chao Dong, Liang Lin, Chen Change Loy
We investigate a novel approach for image restoration by reinforcement learning.
3 code implementations • 5 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.
Ranked #7 on
Semantic Segmentation
on LIP val
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.
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.
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.
no code implementations • 17 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.
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.
Ranked #22 on
Monocular Depth Estimation
on KITTI Eigen split
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