Search Results for author:
Found 252 papers, 119 papers with code
Spatial Latent Dirichlet Allocation
In this paper, we propose a topic model Spatial Latent Dirichlet Allocation (SLDA), which better encodes spatial structure among visual words that are essential for solving many vision problems.
Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering.
Ranked #1 on
Image Clustering
on Coil-20
(Accuracy metric)
Locally Aligned Feature Transforms across Views
In this paper, we propose a new approach for matching images observed in different camera views with complex cross-view transforms and apply it to person reidentification.
Deep Convolutional Network Cascade for Facial Point Detection
At each level, the outputs of multiple networks are fused for robust and accurate estimation.
Modeling Mutual Visibility Relationship in Pedestrian Detection
In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians.
Unsupervised Salience Learning for Person Re-identification
In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning.
Measuring Crowd Collectiveness
Collective motions are common in crowd systems and have attracted a great deal of attention in a variety of multidisciplinary fields.
Single-Pedestrian Detection Aided by Multi-pedestrian Detection
A probabilistic framework is proposed to model the relationship between the configurations estimated by singleand multi-pedestrian detectors, and to refine the single-pedestrian detection result with multi-pedestrian detection.
Deep Learning Face Representation from Predicting 10,000 Classes
When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons.
Ranked #6 on
Face Verification
on Labeled Faces in the Wild
Recover Canonical-View Faces in the Wild with Deep Neural Networks
Face images in the wild undergo large intra-personal variations, such as poses, illuminations, occlusions, and low resolutions, which cause great challenges to face-related applications.
Switchable Deep Network for Pedestrian Detection
In this paper, we propose a Switchable Deep Network (SDN) for pedestrian detection.
Learning Mid-level Filters for Person Re-identification
In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification.
L0 Regularized Stationary Time Estimation for Crowd Group Analysis
We tackle stationary crowd analysis in this paper, which is similarly important as modeling mobile groups in crowd scenes and finds many applications in surveillance.
Multi-source Deep Learning for Human Pose Estimation
Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation.
Deep Learning Face Representation from Predicting 10,000 Classes
When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons.
DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification
In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter.
Deep Learning Face Representation by Joint Identification-Verification
The learned DeepID2 features can be well generalized to new identities unseen in the training data.
Deep Learning Multi-View Representation for Face Recognition
Intriguingly, even without accessing 3D data, human not only can recognize face identity, but can also imagine face images of a person under different viewpoints given a single 2D image, making face perception in the brain robust to view changes.
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty.
Fully Convolutional Neural Networks for Crowd Segmentation
Based on FCNN, a multi-stage deep learning is proposed to integrate appearance and motion cues for crowd segmentation.
Deep Learning Face Attributes in the Wild
LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction.
Ranked #6 on
Facial Attribute Classification
on LFWA
Pedestrian Detection aided by Deep Learning Semantic Tasks
Rather than expensively annotating scene attributes, we transfer attributes information from existing scene segmentation datasets to the pedestrian dataset, by proposing a novel deep model to learn high-level features from multiple tasks and multiple data sources.
Ranked #30 on
Pedestrian Detection
on Caltech
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations
Intriguingly, even without accessing 3D data, human not only can recognize face identity, but can also imagine face images of a person under different viewpoints given a single 2D image, making face perception in the brain robust to view changes.
Deeply learned face representations are sparse, selective, and robust
(2) Its neurons in higher layers are highly selective to identities and identity-related attributes.
Ranked #1 on
Face Verification
on Oulu-CASIA
Person Re-identification by Saliency Learning
(3) saliency matching is proposed based on patch matching.
Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification
The proposed algorithms eliminate all the redundant computation in convolution and pooling on images by introducing novel d-regularly sparse kernels.
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
In this paper, we propose deformable deep convolutional neural networks for generic object detection.
DeepID3: Face Recognition with Very Deep Neural Networks
Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.
Deeply Learned Attributes for Crowded Scene Understanding
We further measure user study performance on WWW and compare this with the proposed deep models.
Understanding Pedestrian Behaviors From Stationary Crowd Groups
Pedestrian behavior modeling and analysis is important for crowd scene understanding and has various applications in video surveillance.
Learning From Massive Noisy Labeled Data for Image Classification
To demonstrate the effectiveness of our approach, we collect a large-scale real-world clothing classification dataset with both noisy and clean labels.
Saliency Detection by Multi-Context Deep Learning
Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance.
Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count.
Ranked #15 on
Crowd Counting
on WorldExpo’10
Convolutional neural networks with low-rank regularization
Recently, tensor decompositions have been used for speeding up CNNs.
Video Matting via Sparse and Low-Rank Representation
We introduce a novel method of video matting via sparse and low-rank representation.
Learning Deep Representation With Large-Scale Attributes
Experimental results show that the attributes are helpful in learning better features and improving the object detection accuracy by 2. 6% in mAP on the ILSVRC 2014 object detection dataset and 2. 4% in mAP on PASCAL VOC 2007 object detection dataset.
Deep Learning Strong Parts for Pedestrian Detection
Third, each part detector in DeepParts is a strong detector that can detect pedestrian by observing only a part of a proposal.
Multi-Task Recurrent Neural Network for Immediacy Prediction
In this paper, we propose to predict immediacy for interacting persons from still images.
Pedestrian Travel Time Estimation in Crowded Scenes
In this paper, we target on the problem of estimating the statistic of pedestrian travel time within a period from an entrance to a destination in a crowded scene.
Visual Tracking With Fully Convolutional Networks
Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet.
Sparsifying Neural Network Connections for Face Recognition
This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition.
Window-Object Relationship Guided Representation Learning for Generic Object Detections
We propose a representation learning pipeline to use the relationship as supervision for improving the learned representation in object detection.
Factors in Finetuning Deep Model for object detection
Our analysis and empirical results show that classes with more samples have higher impact on the feature learning.
Structured Feature Learning for Pose Estimation
In this paper, we propose a structured feature learning framework to reason the correlations among body joints at the feature level in human pose estimation.
Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories
Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature.
Multi-Bias Non-linear Activation in Deep Neural Networks
It provides great flexibility of selecting responses to different visual patterns in different magnitude ranges to form rich representations in higher layers.
Joint Detection and Identification Feature Learning for Person Search
Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates.
Ranked #9 on
Person Re-Identification
on CUHK03
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Temporal and contextual information of videos are not fully investigated and utilized.
Object Detection from Video Tubelets with Convolutional Neural Networks
Deep Convolution Neural Networks (CNNs) have shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation.
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification
Learning generic and robust feature representations with data from multiple domains for the same problem is of great value, especially for the problems that have multiple datasets but none of them are large enough to provide abundant data variations.
STCT: Sequentially Training Convolutional Networks for Visual Tracking
To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features.
DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations
To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks.
End-To-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation
In this paper, we propose a novel end-to-end framework for human pose estimation that combines DCNNs with the expressive deformable mixture of parts.
Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution
Our analysis and empirical results show that classes with more samples have higher impact on the feature learning.
Slicing Convolutional Neural Network for Crowd Video Understanding
Learning and capturing both appearance and dynamic representations are pivotal for crowd video understanding.
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV).
Fashion Landmark Detection in the Wild
Fashion landmark is also compared to clothing bounding boxes and human joints in two applications, fashion attribute prediction and clothes retrieval, showing that fashion landmark is a more discriminative representation to understand fashion images.
Crafting GBD-Net for Object Detection
The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
CRF-CNN: Modeling Structured Information in Human Pose Estimation
In a classical neural network, there is no message passing between neurons in the same layer.
Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.
Ranked #4 on
Video Semantic Segmentation
on Cityscapes val
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.
Ranked #3 on
Text-to-Image Generation
on Oxford 102 Flowers
(Inception score metric)
Automatic Discoveries of Physical and Semantic Concepts via Association Priors of Neuron Groups
The recent successful deep neural networks are largely trained in a supervised manner.
Zoom Out-and-In Network with Recursive Training for Object Proposal
In this paper, we propose a zoom-out-and-in network for generating object proposals.
Person Search with Natural Language Description
Searching persons in large-scale image databases with the query of natural language description has important applications in video surveillance.
Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification
Analysis of the learned SRN model demonstrates that it can effectively capture both semantic and spatial relations of labels for improving classification performance.
Ranked #6 on
Multi-Label Classification
on NUS-WIDE
Progressively Diffused Networks for Semantic Image Segmentation
This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemplar application.
Object Detection in Videos with Tubelet Proposal Networks
Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset.
Learning Deep Features via Congenerous Cosine Loss for Person Recognition
Person recognition aims at recognizing the same identity across time and space with complicated scenes and similar appearance.
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
In this CC-Net, the cascaded classifier at a stage is aided by the classification scores in previous stages.
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
In this paper, each visual relationship is considered as a phrase with three components.
Multi-Context Attention for Human Pose Estimation
We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on the detailed description for different body parts.
Ranked #8 on
Pose Estimation
on Leeds Sports Poses
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
This paper addresses the problem of depth estimation from a single still image.
Ranked #14 on
Depth Estimation
on NYU-Depth V2
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results.
Residual Attention Network for Image Classification
In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion.
Ranked #638 on
Image Classification
on ImageNet
Learning Deep Representations for Scene Labeling with Semantic Context Guided Supervision
The experiments show that our proposed method makes deep models learn more discriminative feature representations without increasing model size or complexity.
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions.
Learning Object Interactions and Descriptions for Semantic Image Segmentation
This work significantly increases segmentation accuracy of CNNs by learning from an Image Descriptions in the Wild (IDW) dataset.
Spindle Net: Person Re-Identification With Human Body Region Guided Feature Decomposition and Fusion
Person re-identification (ReID) is an important task in video surveillance and has various applications.
Recurrent Scale Approximation for Object Detection in CNN
To further increase efficiency and accuracy, we (a): design a scale-forecast network to globally predict potential scales in the image since there is no need to compute maps on all levels of the pyramid.
Ranked #3 on
Face Detection
on Annotated Faces in the Wild
Scene Graph Generation from Objects, Phrases and Region Captions
Object detection, scene graph generation and region captioning, which are three scene understanding tasks at different semantic levels, are tied together: scene graphs are generated on top of objects detected in an image with their pairwise relationship predicted, while region captioning gives a language description of the objects, their attributes, relations, and other context information.
Ranked #2 on
Object Detection
on Visual Genome
Learning Feature Pyramids for Human Pose Estimation
We investigate our method on two standard benchmarks for human pose estimation.
Ranked #6 on
Pose Estimation
on Leeds Sports Poses
Identity-Aware Textual-Visual Matching with Latent Co-attention
The stage-2 CNN-LSTM network refines the matching results with a latent co-attention mechanism.
Unconstrained Fashion Landmark Detection via Hierarchical Recurrent Transformer Networks
This work addresses unconstrained fashion landmark detection, where clothing bounding boxes are not provided in both training and test.
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism
The visibility map of the target is learned and used for inferring the spatial attention map.
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals
Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation.
Optimization assisted MCMC
The global optimization method first reduces a high dimensional search to an one dimensional geodesic to find a starting point close to a local mode.
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
A key observation is that it is difficult to classify anchors of different sizes with the same set of features.
Ranked #2 on
Region Proposal
on COCO test-dev
Visual Question Generation as Dual Task of Visual Question Answering
Recently visual question answering (VQA) and visual question generation (VQG) are two trending topics in the computer vision, which have been explored separately.
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems.
Ranked #2 on
Pedestrian Attribute Recognition
on RAP
Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification
In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed.
Deep Dual Learning for Semantic Image Segmentation
The estimated labelmaps that capture accurate object classes and boundaries are used as ground truths in training to boost performance.
Spontaneous Symmetry Breaking in Neural Networks
We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory.
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.
Ranked #5 on
Text-to-Image Generation
on Oxford 102 Flowers
Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering
Existing VQA methods mainly adopt the visual attention mechanism to associate the input question with corresponding image regions for effective question answering.
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored.
Ranked #50 on
Lane Detection
on CULane
Spontaneous Symmetry Breaking in Deep Neural Networks
In the layer decoupling limit applicable to residual networks (He et al., 2015), we show that the remnant symmetries that survive the non-linear layers are spontaneously broken based on empirical results.
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection.
Decoupling the Layers in Residual Networks
We propose a Warped Residual Network (WarpNet) using a parallelizable warp operator for forward and backward propagation to distant layers that trains faster than the original residual neural network.
Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep Networks
Depth cues have been proved very useful in various computer vision and robotic tasks.
Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data
The aim of image captioning is to generate captions by machine to describe image contents.
Context Encoding for Semantic Segmentation
In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps.
Ranked #7 on
Semantic Segmentation
on PASCAL VOC 2012 test
3D Human Pose Estimation in the Wild by Adversarial Learning
Instead of defining hard-coded rules to constrain the pose estimation results, we design a novel multi-source discriminator to distinguish the predicted 3D poses from the ground-truth, which helps to enforce the pose estimator to generate anthropometrically valid poses even with images in the wild.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification
As a result, the network learns latent representations of the face, torso and other body parts using the best available image patches from the entire video sequence.
Exploring Disentangled Feature Representation Beyond Face Identification
This paper proposes learning disentangled but complementary face features with minimal supervision by face identification.
Learnable Histogram: Statistical Context Features for Deep Neural Networks
Statistical features, such as histogram, Bag-of-Words (BoW) and Fisher Vector, were commonly used with hand-crafted features in conventional classification methods, but attract less attention since the popularity of deep learning methods.
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images.
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing
Depth estimation and scene parsing are two particularly important tasks in visual scene understanding.
Ranked #15 on
Depth Estimation
on NYU-Depth V2
Lehmer Transform and its Theoretical Properties
We propose a new class of transforms that we call {\it Lehmer Transform} which is motivated by the {\it Lehmer mean function}.
Eliminating Background-Bias for Robust Person Re-Identification
State-of-the-art methods mainly utilize deep learning based approaches for learning visual features for describing person appearances.
Group Consistent Similarity Learning via Deep CRF for Person Re-Identification
Extensive experiments demonstrate the effectiveness of our model that combines DNN and CRF for learning robust multi-scale local similarities.
FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis
Existing methods typically formulate GAN as a two-player game, where a discriminator distinguishes face images from the real and synthesized domains, while a generator reduces its discriminativeness by synthesizing a face of photo-realistic quality.
Video Person Re-Identification With Competitive Snippet-Similarity Aggregation and Co-Attentive Snippet Embedding
The attention weights are obtained based on a query feature, which is learned from the whole probe snippet by an LSTM network, making the resulting embeddings less affected by noisy frames.
Ranked #4 on
Person Re-Identification
on PRID2011
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking
In addition, inter-object relations are mostly modeled in a symmetric way, which we argue is not an optimal setting.
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation
Generating scene graph to describe all the relations inside an image gains increasing interests these years.
Ranked #1 on
Scene Graph Generation
on VRD
Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition
We show that by encouraging deep message propagation and interactions between local object features and global predicate features, one can achieve compelling performance in recognizing complex relationships without using any linguistic priors.
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
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).
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech.
Person Re-identification with Deep Similarity-Guided Graph Neural Network
However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs.
Ranked #2 on
Person Re-Identification
on CUHK03
End-to-End Deep Kronecker-Product Matching for Person Re-identification
Person re-identification aims to robustly measure similarities between person images.
Deep Group-shuffling Random Walk for Person Re-identification
Person re-identification aims at finding a person of interest in an image gallery by comparing the probe image of this person with all the gallery images.
Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association
Person re-identification is an important task that requires learning discriminative visual features for distinguishing different person identities.
Ranked #22 on
Text based Person Retrieval
on CUHK-PEDES
Question-Guided Hybrid Convolution for Visual Question Answering
Most state-of-the-art VQA methods fuse the high-level textual and visual features from the neural network and abandon the visual spatial information when learning multi-modal features. To address these problems, question-guided kernels generated from the input question are designed to convolute with visual features for capturing the textual and visual relationship in the early stage.
Ranked #14 on
Visual Question Answering (VQA)
on CLEVR
Neural Network Encapsulation
Motivated by the routing to make higher capsule have agreement with lower capsule, we extend the mechanism as a compensation for the rapid loss of information in nearby layers.
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
Monocular depth estimation aims at estimating a pixelwise depth map for a single image, which has wide applications in scene understanding and autonomous driving.
HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion
Dense depth cues are important and have wide applications in various computer vision tasks.
Ranked #10 on
Depth Completion
on KITTI Depth Completion
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.
Learning to Group and Label Fine-Grained Shape Components
We propose to generate part hypotheses from the components based on a hierarchical grouping strategy, and perform labeling on those part groups instead of directly on the components.
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
Our proposed FD-GAN achieves state-of-the-art performance on three person reID datasets, which demonstrates that the effectiveness and robust feature distilling capability of the proposed FD-GAN.
Ranked #3 on
Person Re-Identification
on CUHK03
Gradient Harmonized Single-stage Detector
Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training, i. e. the huge difference in quantity between positive and negative examples as well as between easy and hard examples.
Ranked #165 on
Object Detection
on COCO test-dev
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
In this paper, we propose PointRCNN for 3D object detection from raw point cloud.
Ranked #2 on
Object Detection
on KITTI Cars Moderate
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering
It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering.
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images
A strong baseline is proposed, called Match R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner.
Improving Referring Expression Grounding with Cross-modal Attention-guided Erasing
Referring expression grounding aims at locating certain objects or persons in an image with a referring expression, where the key challenge is to comprehend and align various types of information from visual and textual domain, such as visual attributes, location and interactions with surrounding regions.
Unsupervised Bi-directional Flow-based Video Generation from one Snapshot
Imagining multiple consecutive frames given one single snapshot is challenging, since it is difficult to simultaneously predict diverse motions from a single image and faithfully generate novel frames without visual distortions.
Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize
Unsupervised cross-spectral stereo matching aims at recovering disparity given cross-spectral image pairs without any supervision in the form of ground truth disparity or depth.
SSN: Learning Sparse Switchable Normalization via SparsestMax
Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax.
Group-wise Correlation Stereo Network
Previous works built cost volumes with cross-correlation or concatenation of left and right features across all disparity levels, and then a 2D or 3D convolutional neural network is utilized to regress the disparity maps.
Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes
For the task of mobility analysis of 3D shapes, we propose joint analysis for simultaneous motion part segmentation and motion attribute estimation, taking a single 3D model as input.
Video Generation from Single Semantic Label Map
This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.
Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation
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.
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving.
Ranked #18 on
Vehicle Pose Estimation
on KITTI Cars Hard
Feature Intertwiner for Object Detection
We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher behavior and thus pushing towards a more compact class centroid in the feature space.
Ranked #134 on
Object Detection
on COCO test-dev
Context and Attribute Grounded Dense Captioning
Dense captioning aims at simultaneously localizing semantic regions and describing these regions-of-interest (ROIs) with short phrases or sentences in natural language.
Ranked #3 on
Dense Captioning
on Visual Genome
Semantics Disentangling for Text-to-Image Generation
Synthesizing photo-realistic images from text descriptions is a challenging problem.
Conditional Adversarial Generative Flow for Controllable Image Synthesis
Flow-based generative models show great potential in image synthesis due to its reversible pipeline and exact log-likelihood target, yet it suffers from weak ability for conditional image synthesis, especially for multi-label or unaware conditions.
Disentangling Pose from Appearance in Monochrome Hand Images
Hand pose estimation from the monocular 2D image is challenging due to the variation in lighting, appearance, and background.
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
Our results show that training deep neural networks with the AdaCos loss is stable and able to achieve high face recognition accuracy.
Ranked #6 on
Face Verification
on MegaFace
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph
Therefore, to generate the images with preferred objects and rich interactions, we propose a semi-parametric method, PasteGAN, for generating the image from the scene graph and the image crops, where spatial arrangements of the objects and their pair-wise relationships are defined by the scene graph and the object appearances are determined by the given object crops.
P2SGrad: Refined Gradients for Optimizing Deep Face Models
Cosine-based softmax losses significantly improve the performance of deep face recognition networks.
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Few-shot learning is an important area of research.
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications.
Deep Self-Learning From Noisy Labels
Unlike previous works constrained by many conditions, making them infeasible to real noisy cases, this work presents a novel deep self-learning framework to train a robust network on the real noisy datasets without extra supervision.
Multi-modality Latent Interaction Network for Visual Question Answering
The proposed module learns the cross-modality relationships between latent visual and language summarizations, which summarize visual regions and question into a small number of latent representations to avoid modeling uninformative individual region-word relations.
Interpolated Convolutional Networks for 3D Point Cloud Understanding
Our InterpConv is shown to be permutation and sparsity invariant, and can directly handle irregular inputs.
Ranked #27 on
3D Part Segmentation
on ShapeNet-Part
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once
Recently, generation-based methods have received much attention since they directly use feed-forward networks to generate the adversarial samples, which avoid the time-consuming iterative attacking procedure in optimization-based and gradient-based methods.
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
ResNeXt, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers.
CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval
Text-image cross-modal retrieval is a challenging task in the field of language and vision.
Ranked #9 on
Image Retrieval
on Flickr30K 1K test
Channel Equilibrium Networks
However, over-sparse CNNs have many collapsed channels (i. e. many channels with undesired zero values), impeding their learning ability.
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
Semantic image synthesis aims at generating photorealistic images from semantic layouts.
Vision-Infused Deep Audio Inpainting
Extensive experiments demonstrate that our framework is capable of inpainting realistic and varying audio segments with or without visual contexts.
Search to Distill: Pearls are Everywhere but not the Eyes
Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into the parameters of a student model with a pre-defined architecture.
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.
Single Image Dehazing Using Ranking Convolutional Neural Network
To obtain effective features for single image dehazing, this paper presents a novel Ranking Convolutional Neural Network (Ranking-CNN).
Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation
Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images.
Channel Equilibrium Networks for Learning Deep Representation
Unlike prior arts that simply removed the inhibited channels, we propose to "wake them up" during training by designing a novel neural building block, termed Channel Equilibrium (CE) block, which enables channels at the same layer to contribute equally to the learned representation.
Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
To tackle the challenges, we propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term.
Ranked #4 on
Unsupervised Domain Adaptation
on Market to MSMT
Adapting Object Detectors with Conditional Domain Normalization
Then this domain-vector is used to encode the features from another domain through a conditional normalization, resulting in different domains' features carrying the same domain attribute.
Ranked #1 on
Unsupervised Domain Adaptation
on SIM10K to BDD100K
Revisiting the Sibling Head in Object Detector
The ``shared head for classification and localization'' (sibling head), firstly denominated in Fast RCNN~\cite{girshick2015fast}, has been leading the fashion of the object detection community in the past five years.
Ranked #67 on
Object Detection
on COCO test-dev
1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation
Given such good instance bounding box, we further design a simple instance-level semantic segmentation pipeline and achieve the 1st place on the segmentation challenge.
KPNet: Towards Minimal Face Detector
The small receptive field and capacity of minimal neural networks limit their performance when using them to be the backbone of detectors.
Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images
Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods.
Cascaded Refinement Network for Point Cloud Completion
Point clouds are often sparse and incomplete.
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching
Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias.
Adaptive Momentum Coefficient for Neural Network Optimization
We propose a novel and efficient momentum-based first-order algorithm for optimizing neural networks which uses an adaptive coefficient for the momentum term.
3D Human Mesh Regression with Dense Correspondence
This paper proposes a model-free 3D human mesh estimation framework, named DecoMR, which explicitly establishes the dense correspondence between the mesh and the local image features in the UV space (i. e. a 2D space used for texture mapping of 3D mesh).
Ranked #1 on
3D Human Reconstruction
on Surreal
PIE-NET: Parametric Inference of Point Cloud Edges
We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data.
Sep-Stereo: Visually Guided Stereophonic Audio Generation by Associating Source Separation
Stereophonic audio is an indispensable ingredient to enhance human auditory experience.
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
At the core of our method, gradient regularization plays two key roles: (1) enforces the gradient of contrastive loss not to increase the supervised training loss on the source domain, which maintains the discriminative power of learned features; (2) regularizes the gradient update on the new domain not to increase the classification loss on the old target domains, which enables the model to adapt to an in-coming target domain while preserving the performance of previously observed domains.
Point Cloud Completion by Learning Shape Priors
Then we learn a mapping to transfer the point features from partial points to that of the complete points by optimizing feature alignment losses.
Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
We propose a novel algorithm, named Open-Edit, which is the first attempt on open-domain image manipulation with open-vocabulary instructions.
Deformable DETR: Deformable Transformers for End-to-End Object Detection
DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance.
Ranked #6 on
2D Object Detection
on SARDet-100K
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
In this paper, we propose to automate the design of metric-specific loss functions by searching differentiable surrogate losses for each metric.
Cascaded Refinement Network for Point Cloud Completion with Self-supervision
This is to mitigate the dependence of existing approaches on large amounts of ground truth training data that are often difficult to obtain in real-world applications.
End-to-End Object Detection with Adaptive Clustering Transformer
In this paper, a novel variant of transformer named Adaptive Clustering Transformer(ACT) has been proposed to reduce the computation cost for high-resolution input.
A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection
With the proposed holistically-guided decoder, we implement the EfficientFCN architecture for semantic segmentation and HGD-FPN for object detection and instance segmentation.
Learning With Privileged Tasks
Concretely, we introduce a privileged parameter so that the optimization direction does not necessarily follow the gradient from the privileged tasks, but concentrates more on the target tasks.
STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement
STAR is a general architecture that can be easily adapted to different image enhancement tasks.
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction
In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner.
Fast Convergence of DETR with Spatially Modulated Co-Attention
The recently proposed Detection Transformer (DETR) model successfully applies Transformer to objects detection and achieves comparable performance with two-stage object detection frameworks, such as Faster-RCNN.
PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
3D object detection is receiving increasing attention from both industry and academia thanks to its wide applications in various fields.
Ranked #2 on
3D Object Detection
on KITTI Cars Easy val
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network
Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse.
Learning Fine-Grained Segmentation of 3D Shapes without Part Labels
Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags.
Fixing the Teacher-Student Knowledge Discrepancy in Distillation
To solve this problem, in this paper, we propose a novel student-dependent distillation method, knowledge consistent distillation, which makes teacher's knowledge more consistent with the student and provides the best suitable knowledge to different student networks for distillation.
Semantic Scene Completion via Integrating Instances and Scene in-the-Loop
The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene.
Ranked #1 on
3D Semantic Scene Completion
on NYUv2
Visually Informed Binaural Audio Generation without Binaural Audios
Moreover, combined with binaural recordings, our method is able to further boost the performance of binaural audio generation under supervised settings.
Decoupled Spatial-Temporal Transformer for Video Inpainting
Seamless combination of these two novel designs forms a better spatial-temporal attention scheme and our proposed model achieves better performance than state-of-the-art video inpainting approaches with significant boosted efficiency.
Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation
While speech content information can be defined by learning the intrinsic synchronization between audio-visual modalities, we identify that a pose code will be complementarily learned in a modulated convolution-based reconstruction framework.
