Search Results for author:
Found 156 papers, 52 papers with code
EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation
In this paper, we have a critical insight that improving the feed-forward network (FFN) in BERT has a higher gain than improving the multi-head attention (MHA) since the computational cost of FFN is 2$\sim$3 times larger than MHA.
M5Product: A Multi-modal Pretraining Benchmark for E-commercial Product Downstream Tasks
In this paper, we aim to advance the research of multi-modal pre-training on E-commerce and subsequently contribute a large-scale dataset, named M5Product, which consists of over 6 million multimodal pairs, covering more than 6, 000 categories and 5, 000 attributes.
Voxel Transformer for 3D Object Detection
We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds.
Ranked #1 on
3D Object Detection
on waymo vehicle
(L1 mAP metric)
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
To resolve the problems, we propose a novel second-stage module, named pyramid RoI head, to adaptively learn the features from the sparse points of interest.
Ranked #1 on
3D Object Detection
on waymo vehicle
(AP metric)
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift
We attribute this ranking correlation problem to the supernet training consistency shift, including feature shift and parameter shift.
Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19.
M3D-VTON: A Monocular-to-3D Virtual Try-On Network
Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value.
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.
WAS-VTON: Warping Architecture Search for Virtual Try-on Network
Despite recent progress on image-based virtual try-on, current methods are constraint by shared warping networks and thus fail to synthesize natural try-on results when faced with clothing categories that require different warping operations.
Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining
In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories.
Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation
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.
AutoBERT-Zero: Evolving BERT Backbone from Scratch
We optimize both the search algorithm and evaluation of candidate models to boost the efficiency of our proposed OP-NAS.
Deep Learning for Embodied Vision Navigation: A Survey
Navigation is one of the fundamental features of a autonomous robot.
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks
Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions.
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy loss, which encourages an exact token-by-token match between a target sequence with a generated sequence.
One Million Scenes for Autonomous Driving: ONCE Dataset
To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving
Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system, we present a large-scale benchmark for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data, which is the first and largest benchmark to date.
Prototypical Graph Contrastive Learning
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.
Vision-Language Navigation with Random Environmental Mixup
However, these works do not explicitly reduce the data bias across different house scenes.
Ranked #16 on
Vision and Language Navigation
on VLN Challenge
Towards Quantifiable Dialogue Coherence Evaluation
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.
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning
Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 5, 010 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems.
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search
While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning
We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS).
Ranked #1 on
Mathematical Question Answering
on Geometry3K
SOON: Scenario Oriented Object Navigation with Graph-based Exploration
In this task, an agent is required to navigate from an arbitrary position in a 3D embodied environment to localize a target following a scene description.
DAGN: Discourse-Aware Graph Network for Logical Reasoning
The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks.
Ranked #9 on
Reading Comprehension
on ReClor
Dynamic Slimmable Network
Here, we explore a dynamic network slimming regime, named Dynamic Slimmable Network (DS-Net), which aims to achieve good hardware-efficiency via dynamically adjusting filter numbers of networks at test time with respect to different inputs, while keeping filters stored statically and contiguously in hardware to prevent the extra burden.
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
In this work, we present Block-wisely Self-supervised Neural Architecture Search (BossNAS), an unsupervised NAS method that addresses the problem of inaccurate architecture rating caused by large weight-sharing space and biased supervision in previous methods.
A Data-Centric Framework for Composable NLP Workflows
Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.
SparseBERT: Rethinking the Importance Analysis in Self-attention
A surprising result is that diagonal elements in the attention map are the least important compared with other attention positions.
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search
For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.
GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer
Non-parallel text style transfer has attracted increasing research interests in recent years.
Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer
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.
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task.
Unifying Relational Sentence Generation and Retrieval for Medical Image Report Composition
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.
Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning
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.
CAT-SAC: Soft Actor-Critic with Curiosity-Aware Entropy Temperature
The curiosity is added to the target entropy to increase the entropy temperature for unfamiliar states and decrease the target entropy for familiar states.
NASOA: Towards Faster Task-oriented Online Fine-tuning
The resulting model zoo is more training efficient than SOTA NAS models, e. g. 6x faster than RegNetY-16GF, and 1. 7x faster than EfficientNetB3.
TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search
While existing NAS methods mostly design architectures on one single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task.
REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement
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.
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
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.
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition
When sampling tasks in MML-ASR, AMS adaptively determines the task sampling probability for each source language.
Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding
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.
Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation
Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention.
Ranked #12 on
Panoptic Segmentation
on COCO test-dev
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
Synchronization is a key step in data-parallel distributed machine learning (ML).
Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp
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.
Towards Robust Medical Image Segmentation on Small-Scale Data with Incomplete Labels
It is time to step back and think about the robustness of partially supervised methods and how to maximally utilize small-scale and partially labeled data for medical image segmentation tasks.
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation
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.
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
In this paper, we develop a general framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training.
Iterative Graph Self-Distillation
How to discriminatively vectorize graphs is a fundamental challenge that attracts increasing attentions in recent years.
MedDG: A Large-scale Medical Consultation Dataset for Building Medical Dialogue System
To push forward the future research on building expert-sensitive medical dialogue system, we proposes two kinds of medical dialogue tasks based on MedDG dataset.
Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems
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.
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems
Capitalized on the topic-level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation.
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.
Ranked #12 on
Lane Detection
on CULane
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search
In spite of its remarkable progress, many algorithms are restricted to particular search spaces.
Ranked #3 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (val) metric)
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation
Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure.
Bidirectional Graph Reasoning Network for Panoptic Segmentation
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.
Linguistically Driven Graph Capsule Network for Visual Question Reasoning
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.
Vision-Dialog Navigation by Exploring Cross-modal Memory
Benefiting from the collaborative learning of the L-mem and the V-mem, our CMN is able to explore the memory about the decision making of historical navigation actions which is for the current step.
Learning Reinforced Agents with Counterfactual Simulation for Medical Automatic Diagnosis
To address this problem, this paper presents a propensity-based patient simulator (PBPS), which is capable of facilitating the training of MAD agents by generating informative counterfactual answers along with the disease diagnosis.
ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection
Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain?
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
Finally, an InterDomain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally.
Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation
Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversations.
Blockwisely Supervised Neural Architecture Search with Knowledge Distillation
Moreover, we find that the knowledge of a network model lies not only in the network parameters but also in the network architecture.
SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection
In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection.
Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks
In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to take advantage of the additional training signals derived from the semantic information.
Ranked #7 on
Vision and Language Navigation
on VLN Challenge
Heterogeneous Graph Learning for Visual Commonsense Reasoning
Our HGL consists of a primal vision-to-answer heterogeneous graph (VAHG) module and a dual question-to-answer heterogeneous graph (QAHG) module to interactively refine reasoning paths for semantic agreement.
Layout-Graph Reasoning for Fashion Landmark Detection
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.
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Resembling the rapid learning capability of human, few-shot learning empowers vision systems to understand new concepts by training with few samples.
Ranked #8 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network
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?)
Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
(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
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
A broad range of cross-$m$-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs).
Fashion Editing with Adversarial Parsing Learning
Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value.
Learning Personalized Modular Network Guided by Structured Knowledge
Learning semantic configurations and activation of modules to align well with structured knowledge can be regarded as a decision-making procedure, which is solved by a new graph-based reinforcement learning algorithm.
Target-Guided Open-Domain Conversation
We study the problem of imposing conversational goals on open-domain chat agents.
Graph Transformer
Graph neural networks (GNN) have gained increasing research interests as a mean to the challenging goal of robust and universal graph learning.
Graphonomy: Universal Human Parsing via Graph Transfer Learning
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.
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
Generating long and semantic-coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accurate descriptions.
Towards Multi-pose Guided Virtual Try-on Network
Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON) can generate a new person image after fitting the desired clothes into the input image and manipulating human poses.
Ranked #1 on
Virtual Try-on
on Deep-Fashion
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
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.
Data-to-Text Generation with Style Imitation
That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.
Symbolic Graph Reasoning Meets Convolutions
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 #38 on
Semantic Segmentation
on ADE20K val
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis
Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations.
AutoLoss: Learning Discrete Schedules for Alternate Optimization
Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.
Interpretable Visual Question Answering by Reasoning on Dependency Trees
Collaborative reasoning for understanding image-question pairs is a very critical but underexplored topic in interpretable visual question answering systems.
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
The versatile toolkit also fosters technique sharing across different text generation tasks.
RCAA: Relational Context-Aware Agents for Person Search
In this paper, we address this problem by training relational context-aware agents which learn the actions to localize the target person from the gallery of whole scene images.
Generative Semantic Manipulation with Mask-Contrasting GAN
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.
Adversarial Geometry-Aware Human Motion Prediction
We explore an approach to forecasting human motion in a few milliseconds given an input 3D skeleton sequence based on a recurrent encoder-decoder framework.
Jointly Deep Multi-View Learning for Clustering Analysis
To our best knowledge, this is the first work to model the multi-view clustering in a deep joint framework, which will provide a meaningful thinking in unsupervised multi-view learning.
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
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).
Instance-level Human Parsing via Part Grouping Network
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
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images
Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest.
Toward Characteristic-Preserving Image-based Virtual Try-On Network
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.
Query-Conditioned Three-Player Adversarial Network for Video Summarization
Video summarization plays an important role in video understanding by selecting key frames/shots.
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
To our knowledge, this is the first successful case of the learned driving policy through reinforcement learning in the high-fidelity simulator, which performs better-than supervised imitation learning.
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
Specifically, we propose a model that enforces our intuition that prediction masks should be domain independent.
Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful
Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models.
Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
The features make Texar particularly suitable for technique sharing and generalization across different text generation applications.
Deep Generative Models with Learnable Knowledge Constraints
The broad set of deep generative models (DGMs) has achieved remarkable advances.
Reinforcement Cutting-Agent Learning for Video Object Segmentation
In this paper, we formulate this problem as a Markov Decision Process, where agents are learned to segment object regions under a deep reinforcement learning framework.
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning.
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation
Experiments show that our approach achieves the state-of-the-art results on two medical report datasets, generating well-balanced structured sentences with robust coverage of heterogeneous medical report contents.
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching
Cellular Electron CryoTomography (CECT) is a 3D imaging technique that captures information about the structure and spatial organization of macromolecular complexes within single cells, in near-native state and at sub-molecular resolution.
Dilated Temporal Relational Adversarial Network for Generic Video Summarization
Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner.
ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation
We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair based connectivity prediction task.
Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark
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
Visual Question Reasoning on General Dependency Tree
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.
Dynamic-structured Semantic Propagation Network
Moreoever, we demonstrate a universal segmentation model that is jointly trained on diverse datasets can surpass the performance of the common fine-tuning scheme for exploiting multiple domain knowledge.
Ranked #41 on
Semantic Segmentation
on ADE20K val
Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms
Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution.
Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale.
Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption
It presents two main contributions to traditional GANs: 1) a soft gradient-sensitive objective for keeping semantic boundaries; 2) a semantic-aware discriminator for validating the fidelity of personalized adaptions with respect to each semantic region.
Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder
Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day, and the underlying fine-grained semantic and motion information (i. e., objects of interest and their key motions) in online videos has been barely touched.
Structured Generative Adversarial Networks
We study the problem of conditional generative modeling based on designated semantics or structures.
Learning to Segment Human by Watching YouTube
An intuition on human segmentation is that when a human is moving in a video, the video-context (e. g., appearance and motion clues) may potentially infer reasonable mask information for the whole human body.
Attention-Aware Face Hallucination via Deep Reinforcement Learning
Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images.
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
A common issue, however, is that objects of interest that are not involved in human actions are often absent in global action descriptions known as "missing label".
Ranked #3 on
Weakly Supervised Object Detection
on Charades
Generative Semantic Manipulation with Contrasting GAN
Generative Adversarial Networks (GANs) have recently achieved significant improvement on paired/unpaired image-to-image translation, such as photo$\rightarrow$ sketch and artist painting style transfer.
Ranked #3 on
Facial Expression Translation
on CelebA
Dual Motion GAN for Future-Flow Embedded Video Prediction
To make both synthesized future frames and flows indistinguishable from reality, a dual adversarial training method is proposed to ensure that the future-flow prediction is able to help infer realistic future-frames, while the future-frame prediction in turn leads to realistic optical flows.
Recurrent 3D Pose Sequence Machines
3D human articulated pose recovery from monocular image sequences is very challenging due to the diverse appearances, viewpoints, occlusions, and also the human 3D pose is inherently ambiguous from the monocular imagery.
Perceptual Generative Adversarial Networks for Small Object Detection
In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection.
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
We show that Poseidon enables Caffe and TensorFlow to achieve 15. 5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification.
SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays
Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes.
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach
We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems.
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning
In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space.
Recurrent Topic-Transition GAN for Visual Paragraph Generation
The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators.
ZM-Net: Real-time Zero-shot Image Manipulation Network
We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones).
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Human parsing has recently attracted a lot of research interests due to its huge application potentials.
Ranked #10 on
Semantic Segmentation
on LIP val
Tree-Structured Reinforcement Learning for Sequential Object Localization
Therefore, Tree-RL can better cover different objects with various scales which is quite appealing in the context of object proposal.
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
To capture such global interdependency, we propose a deep Variation-structured Reinforcement Learning (VRL) framework to sequentially discover object relationships and attributes in the whole image.
Interpretable Structure-Evolving LSTM
Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization.
Toward Controlled Generation of Text
Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain.
Multi-stage Object Detection with Group Recursive Learning
Most of existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately.
Human Pose Estimation from Depth Images via Inference Embedded Multi-task Learning
In this paper, we propose a novel inference-embedded multi-task learning framework for predicting human pose from still depth images, which is implemented with a deep architecture of neural networks.
Peak-Piloted Deep Network for Facial Expression Recognition
Objective functions for training of deep networks for face-related recognition tasks, such as facial expression recognition (FER), usually consider each sample independently.
Ranked #2 on
Facial Expression Recognition
on Oulu-CASIA
Is Faster R-CNN Doing Well for Pedestrian Detection?
Detecting pedestrian has been arguably addressed as a special topic beyond general object detection.
Ranked #14 on
Pedestrian Detection
on Caltech
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling
Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts.
Deep Structured Scene Parsing by Learning with Image Descriptions
This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i. e., a semantic object hierarchy with object interaction relations) that finely accords with human perception.
Geometric Scene Parsing with Hierarchical LSTM
This paper addresses the problem of geometric scene parsing, i. e. simultaneously labeling geometric surfaces (e. g. sky, ground and vertical plane) and determining the interaction relations (e. g. layering, supporting, siding and affinity) between main regions.
Attentive Contexts for Object Detection
We provide preliminary answers to these questions through developing a novel Attention to Context Convolution Neural Network (AC-CNN) based object detection model.
Semantic Object Parsing with Graph LSTM
By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LSTM from sequential data or multi-dimensional data to general graph-structured data.
Scale-aware Pixel-wise Object Proposal Networks
In particular, in order to improve the localization accuracy, a fully convolutional network is employed which predicts locations of object proposals for each pixel.
Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection
Then the concept detector can be fine-tuned based on these new instances.
Human Parsing With Contextualized Convolutional Neural Network
In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network.
Semantic Object Parsing with Local-Global Long Short-Term Memory
The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions.
Reversible Recursive Instance-level Object Segmentation
By being reversible, the proposal refinement sub-network adaptively determines an optimal number of refinement iterations required for each proposal during both training and testing.
Scale-aware Fast R-CNN for Pedestrian Detection
Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework.
Ranked #18 on
Pedestrian Detection
on Caltech
STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation
Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as well as the image-level annotations.
Proposal-free Network for Instance-level Object Segmentation
Instance-level object segmentation is an important yet under-explored task.
Matching-CNN Meets KNN: Quasi-Parametric Human Parsing
Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image.
Deep Human Parsing with Active Template Regression
The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters.
Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model
The aim of this study is to provide an automatic computational framework to assist clinicians in diagnosing Focal Liver Lesions (FLLs) in Contrast-Enhancement Ultrasound (CEUS).
Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models
Although it has been widely discussed in video surveillance, background subtraction is still an open problem in the context of complex scenarios, e. g., dynamic backgrounds, illumination variations, and indistinct foreground objects.
Learning Latent Spatio-Temporal Compositional Model for Human Action Recognition
Action recognition is an important problem in multimedia understanding.
Computational Baby Learning
Then the concept detector can be fine-tuned based on these new instances.