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Found 170 papers, 62 papers with code
Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition
Knowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices.
Modality-Agnostic Debiasing for Single Domain Generalization
We validate the superiority of our MAD in a variety of single-DG scenarios with different modalities, including recognition on 1D texts, 2D images, 3D point clouds, and semantic segmentation on 2D images.
Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
Firstly, we construct a pretext task, \textit{i. e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network.
Semantic-Conditional Diffusion Networks for Image Captioning
The rich semantics are further regarded as semantic prior to trigger the learning of Diffusion Transformer, which produces the output sentence in a diffusion process.
Explaining Cross-Domain Recognition with Interpretable Deep Classifier
In this paper, we ask the question: how much each sample in source domain contributes to the network's prediction on the samples from target domain.
SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement
In this paper, we propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net.
Dynamic Temporal Filtering in Video Models
The pre-determined kernel size severely limits the temporal receptive fields and the fixed weights treat each spatial location across frames equally, resulting in sub-optimal solution for long-range temporal modeling in natural scenes.
3D Cascade RCNN: High Quality Object Detection in Point Clouds
Recent progress on 2D object detection has featured Cascade RCNN, which capitalizes on a sequence of cascade detectors to progressively improve proposal quality, towards high-quality object detection.
Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization
Existing works attempt to solve the problem by explicitly imposing uncertainty on classifiers when OOD inputs are exposed to the classifier during training.
Generalized One-shot Domain Adaptation of Generative Adversarial Networks
While previous works mainly focus on style transfer, we propose a novel and concise framework to address the \textit{generalized one-shot adaptation} task for both style and entity transfer, in which a reference image and its binary entity mask are provided.
WOC: A Handy Webcam-based 3D Online Chatroom
We develop WOC, a webcam-based 3D virtual online chatroom for multi-person interaction, which captures the 3D motion of users and drives their individual 3D virtual avatars in real-time.
MAPLE: Masked Pseudo-Labeling autoEncoder for Semi-supervised Point Cloud Action Recognition
In DestFormer, the spatial and temporal dimensions of the 4D point cloud videos are decoupled to achieve efficient self-attention for learning both long-term and short-term features.
Lightweight and Progressively-Scalable Networks for Semantic Segmentation
In this paper, we thoroughly analyze the design of convolutional blocks (the type of convolutions and the number of channels in convolutions), and the ways of interactions across multiple scales, all from lightweight standpoint for semantic segmentation.
Dual Vision Transformer
Dual-ViT is henceforth able to reduce the computational complexity without compromising much accuracy.
Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning
Motivated by the wavelet theory, we construct a new Wavelet Vision Transformer (\textbf{Wave-ViT}) that formulates the invertible down-sampling with wavelet transforms and self-attention learning in a unified way.
Ranked #184 on
Image Classification
on ImageNet
Video2StyleGAN: Encoding Video in Latent Space for Manipulation
To this end, we propose a novel network to encode face videos into the latent space of StyleGAN for semantic face video manipulation.
Bi-Calibration Networks for Weakly-Supervised Video Representation Learning
The video-to-text/video-to-query projections over text prototypes/query vocabulary then start the text-to-query or query-to-text calibration to estimate the amendment to query or text.
Stand-Alone Inter-Frame Attention in Video Models
In this paper, we present a new recipe of inter-frame attention block, namely Stand-alone Inter-Frame Attention (SIFA), that novelly delves into the deformation across frames to estimate local self-attention on each spatial location.
Ranked #7 on
Action Recognition
on Something-Something V1
Comprehending and Ordering Semantics for Image Captioning
In this paper, we propose a new recipe of Transformer-style structure, namely Comprehending and Ordering Semantics Networks (COS-Net), that novelly unifies an enriched semantic comprehending and a learnable semantic ordering processes into a single architecture.
Exploring Structure-aware Transformer over Interaction Proposals for Human-Object Interaction Detection
Such design decomposes the process of HOI set prediction into two subsequent phases, i. e., an interaction proposal generation is first performed, and then followed by transforming the non-parametric interaction proposals into HOI predictions via a structure-aware Transformer.
Ranked #1 on
Human-Object Interaction Detection
on V-COCO
MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing
By deriving the novel grouped time mixing (GTM) operations, we equip the basic token-mixing MLP with the ability of temporal modeling.
Ranked #15 on
Action Recognition
on Something-Something V1
Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation
This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Interaction track targets for learning policies from pre-collected demonstration trajectories.
Structured Two-stream Attention Network for Video Question Answering
To date, visual question answering (VQA) (i. e., image QA and video QA) is still a holy grail in vision and language understanding, especially for video QA.
Gait Recognition in the Wild with Dense 3D Representations and A Benchmark
Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild.
Ranked #1 on
Gait Recognition
on Gait3D
A-ACT: Action Anticipation through Cycle Transformations
To incorporate this ability in intelligent systems a question worth pondering upon is how exactly do we anticipate?
Visualizing and Understanding Patch Interactions in Vision Transformer
Vision Transformer (ViT) has become a leading tool in various computer vision tasks, owing to its unique self-attention mechanism that learns visual representations explicitly through cross-patch information interactions.
Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework
Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video.
Freeform Body Motion Generation from Speech
Motivated by studies in linguistics, we decompose the co-speech motion into two complementary parts: pose modes and rhythmic dynamics.
Cross-modal Contrastive Distillation for Instructional Activity Anticipation
In this study, we aim to predict the plausible future action steps given an observation of the past and study the task of instructional activity anticipation.
Optimization Planning for 3D ConvNets
In this paper, we decompose the path into a series of training "states" and specify the hyper-parameters, e. g., learning rate and the length of input clips, in each state.
Representing Videos as Discriminative Sub-graphs for Action Recognition
For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition.
Motion-Focused Contrastive Learning of Video Representations
To this end, we compose a duet of exploiting the motion for data augmentation and feature learning in the regime of contrastive learning.
Smart Director: An Event-Driven Directing System for Live Broadcasting
To our best knowledge, our system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events.
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training
Vision-language pre-training has been an emerging and fast-developing research topic, which transfers multi-modal knowledge from rich-resource pre-training task to limited-resource downstream tasks.
Condensing a Sequence to One Informative Frame for Video Recognition
This paper studies a two-step alternative that first condenses the video sequence to an informative "frame" and then exploits off-the-shelf image recognition system on the synthetic frame.
Boosting Video Representation Learning with Multi-Faceted Integration
Video content is multifaceted, consisting of objects, scenes, interactions or actions.
Responsive Listening Head Generation: A Benchmark Dataset and Baseline
Automatically synthesizing listening behavior that actively responds to a talking head, is critical to applications such as digital human, virtual agents and social robots.
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration
Our work reveals a structured shortcoming of the existing mainstream self-supervised learning methods.
Putting People in their Place: Monocular Regression of 3D People in Depth
To do so, we exploit a 3D body model space that lets BEV infer shapes from infants to adults.
Ranked #1 on
3D Depth Estimation
on Relative Human
(using extra training data)
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising
BERT-type structure has led to the revolution of vision-language pre-training and the achievement of state-of-the-art results on numerous vision-language downstream tasks.
Transferrable Contrastive Learning for Visual Domain Adaptation
From this point, we present a particular paradigm of self-supervised learning tailored for domain adaptation, i. e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently.
A Style and Semantic Memory Mechanism for Domain Generalization
Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains.
Ranked #24 on
Domain Generalization
on PACS
Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning
Such noisy samples are hard to predict precise depth values, thus may obstruct the widely-used depth supervised optimization.
ViDA-MAN: Visual Dialog with Digital Humans
We demonstrate ViDA-MAN, a digital-human agent for multi-modal interaction, which offers realtime audio-visual responses to instant speech inquiries.
Directional Self-supervised Learning for Heavy Image Augmentations
Specifically, we adapt heavy augmentation policies after the views lightly augmented by standard augmentations, to generate harder view (HV).
A Baseline Framework for Part-level Action Parsing and Action Recognition
This technical report introduces our 2nd place solution to Kinetics-TPS Track on Part-level Action Parsing in ICCV DeeperAction Workshop 2021.
CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation
Unlike the mainstream clustering-based methods, our framework exploits a transformer-based feature reconstruction scheme to detect event boundary by reconstruction errors.
Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis
Deep person generation has attracted extensive research attention due to its wide applications in virtual agents, video conferencing, online shopping and art/movie production.
Memory-Augmented Non-Local Attention for Video Super-Resolution
Those methods achieve limited performance as they suffer from the challenge in spatial frame alignment and the lack of useful information from similar LR neighbor frames.
X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
Nevertheless, there has not been an open-source codebase in support of training and deploying numerous neural network models for cross-modal analytics in a unified and modular fashion.
Semi-Supervised Domain Generalizable Person Re-Identification
Instead, we aim to explore multiple labeled datasets to learn generalized domain-invariant representations for person re-id, which is expected universally effective for each new-coming re-id scenario.
Generalizable Person Re-identification
Knowledge Distillation
+1
A Low Rank Promoting Prior for Unsupervised Contrastive Learning
In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC.
Contextual Transformer Networks for Visual Recognition
Such design fully capitalizes on the contextual information among input keys to guide the learning of dynamic attention matrix and thus strengthens the capacity of visual representation.
Ranked #251 on
Image Classification
on ImageNet
Augmentation Pathways Network for Visual Recognition
Experimental results on ImageNet demonstrate the compatibility and effectiveness on a much wider range of augmentations, while consuming fewer parameters and lower computational costs at inference time.
FasterPose: A Faster Simple Baseline for Human Pose Estimation
By the experimental analysis, we find that the HR representation leads to a sharp increase of computational cost, while the accuracy improvement remains marginal compared with the low-resolution (LR) representation.
Boosting Semi-Supervised Face Recognition with Noise Robustness
This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling.
Multi-Agent Semi-Siamese Training for Long-tail and Shallow Face Learning
However, unlike the existing public face datasets, in many real-world scenarios of face recognition, the depth of training dataset is shallow, which means only two face images are available for each ID.
Action Unit Memory Network for Weakly Supervised Temporal Action Localization
In this paper, we present an Action Unit Memory Network (AUMN) for weakly supervised temporal action localization, which can mitigate the above two challenges by learning an action unit memory bank.
Ranked #5 on
Weakly Supervised Action Localization
on THUMOS14
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective
We believe this survey will provide the readers with a deep and insightful understanding of monocular human pose estimation.
Towards NIR-VIS Masked Face Recognition
In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method.
Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition
Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity.
Exploiting Relationship for Complex-scene Image Generation
Our method tends to synthesize plausible layouts and objects, respecting the interplay of multiple objects in an image.
Group-aware Label Transfer for Domain Adaptive Person Re-identification
In this paper, we propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning.
Domain Adaptive Person Re-Identification
Online Clustering
+3
Explainable Person Re-Identification with Attribute-guided Metric Distillation
In this paper, we propose a post-hoc method, named Attribute-guided Metric Distillation (AMD), to explain existing ReID models.
Ranked #42 on
Person Re-Identification
on DukeMTMC-reID
TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition
Despite significant improvement in gait recognition with deep learning, existing studies still neglect a more practical but challenging scenario -- unsupervised cross-domain gait recognition which aims to learn a model on a labeled dataset then adapts it to an unlabeled dataset.
Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network
Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding and generation remains challenging.
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification
Visible-Infrared person re-identification (VI-ReID) aims to match cross-modality pedestrian images, breaking through the limitation of single-modality person ReID in dark environment.
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing
Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently.
Ranked #3 on
Face Parsing
on CelebAMask-HQ
FaceX-Zoo: A PyTorch Toolbox for Face Recognition
For example, the production of face representation network desires a modular training scheme to consider the proper choice from various candidates of state-of-the-art backbone and training supervision subject to the real-world face recognition demand; for performance analysis and comparison, the standard and automatic evaluation with a bunch of models on multiple benchmarks will be a desired tool as well; besides, a public groundwork is welcomed for deploying the face recognition in the shape of holistic pipeline.
Synthetic Training for Monocular Human Mesh Recovery
To solve this problem, we design a multi-branch framework to disentangle the regression of different body properties, enabling us to separate each component's training in a synthetic training manner using unpaired data available.
Hierarchical Gumbel Attention Network for Text-based Person Search
This hard selection strategy is able to fuse the strong-relevant multi-modality features for alleviating the problem of matching redundancy.
Ranked #8 on
Text based Person Retrieval
on CUHK-PEDES
Joint Contrastive Learning with Infinite Possibilities
This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling.
The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances
To start with, we present an overview of the end-to-end deep face recognition.
Learning to Localize Actions from Moments
In this paper, we introduce a new design of transfer learning type to learn action localization for a large set of action categories, but only on action moments from the categories of interest and temporal annotations of untrimmed videos from a small set of action classes.
Monocular, One-stage, Regression of Multiple 3D People
Through a body-center-guided sampling process, the body mesh parameters of all people in the image are easily extracted from the Mesh Parameter map.
Ranked #1 on
3D Multi-Person Mesh Recovery
on Relative Human
(using extra training data)
Black Re-ID: A Head-shoulder Descriptor for the Challenging Problem of Person Re-Identification
Given the input person image, the ensemble method would focus on the head-shoulder feature by assigning a larger weight if the individual insides the image is in black clothing.
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives.
Pre-training for Video Captioning Challenge 2020 Summary
The Pre-training for Video Captioning Challenge 2020 Summary: results and challenge participants' technical reports.
Edge-aware Graph Representation Learning and Reasoning for Face Parsing
Specifically, we encode a facial image onto a global graph representation where a collection of pixels ("regions") with similar features are projected to each vertex.
Ranked #4 on
Face Parsing
on CelebAMask-HQ
NPCFace: Negative-Positive Collaborative Training for Large-scale Face Recognition
However, the correlation between hard positive and hard negative is overlooked, and so is the relation between the margins in positive and negative logits.
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains.
Ranked #15 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Semi-Siamese Training for Shallow Face Learning
Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data.
Loss Function Search for Face Recognition
In face recognition, designing margin-based (e. g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.
Single Shot Video Object Detector
Single shot detectors that are potentially faster and simpler than two-stage detectors tend to be more applicable to object detection in videos.
Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training
In this work, we present Auto-captions on GIF, which is a new large-scale pre-training dataset for generic video understanding.
Transferring and Regularizing Prediction for Semantic Segmentation
In the view of extremely expensive expert labeling, recent research has shown that the models trained on photo-realistic synthetic data (e. g., computer games) with computer-generated annotations can be adapted to real images.
Ranked #13 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation
A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample.
Learning a Unified Sample Weighting Network for Object Detection
To this end, we devise a general loss function to cover most region-based object detectors with various sampling strategies, and then based on it we propose a unified sample weighting network to predict a sample's task weights.
Learning the Compositional Visual Coherence for Complementary Recommendations
Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents.
FastReID: A Pytorch Toolbox for General Instance Re-identification
General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research.
Ranked #1 on
Person Re-Identification
on MSMT17-C
Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks
Inspired by the human "visual tracking" capability which leverages motion cues to distinguish the target from the background, we propose a Two-Stream Residual Convolutional Network (TS-RCN) for visual tracking, which successfully exploits both appearance and motion features for model update.
VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.
Ranked #1 on
Vehicle Re-Identification
on VehicleID
A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing
The dataset is publicly accessible to the community for boosting the advance of face parsing. 1 Second, a simple yet effective Boundary-Attention Semantic Segmentation (BASS) method is proposed for face parsing, which contains a three-branch network with elaborately developed loss functions to fully exploit the boundary information.
Ranked #8 on
Face Parsing
on LaPa
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
Ranked #1 on
Image Recognition
on ImageNet
Long Short-Term Relation Networks for Video Action Detection
It has been well recognized that modeling human-object or object-object relations would be helpful for detection task.
X-Linear Attention Networks for Image Captioning
Recent progress on fine-grained visual recognition and visual question answering has featured Bilinear Pooling, which effectively models the 2$^{nd}$ order interactions across multi-modal inputs.
Ranked #21 on
Image Captioning
on COCO Captions
Adaptive Semantic-Visual Tree for Hierarchical Embeddings
To solve this new problem, we propose a hierarchical adaptive semantic-visual tree (ASVT) to depict the architecture of merchandise categories, which evaluates semantic similarities between different semantic levels and visual similarities within the same semantic class simultaneously.
Vision and Language: from Visual Perception to Content Creation
The real-world deployment or services of vision and language are elaborated as well.
Down to the Last Detail: Virtual Try-on with Detail Carving
However, existing methods can hardly preserve the details in clothing texture and facial identity (face, hair) while fitting novel clothes and poses onto a person.
Theme-Matters: Fashion Compatibility Learning via Theme Attention
This model associates themes with the pairwise compatibility with attention, and thus compute the outfit-wise compatibility.
Zooming into Face Forensics: A Pixel-level Analysis
The stunning progress in face manipulation methods has made it possible to synthesize realistic fake face images, which poses potential threats to our society.
Mis-classified Vector Guided Softmax Loss for Face Recognition
Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination.
Scheduled Differentiable Architecture Search for Visual Recognition
Moreover, we enlarge the search space of SDAS particularly for video recognition by devising several unique operations to encode spatio-temporal dynamics and demonstrate the impact in affecting the architecture search of SDAS.
Gaussian Temporal Awareness Networks for Action Localization
Temporally localizing actions in a video is a fundamental challenge in video understanding.
Deep Metric Learning with Density Adaptivity
The problem of distance metric learning is mostly considered from the perspective of learning an embedding space, where the distances between pairs of examples are in correspondence with a similarity metric.
Hierarchy Parsing for Image Captioning
It is always well believed that parsing an image into constituent visual patterns would be helpful for understanding and representing an image.
Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation
In our framework, a spatial constraint module is designed to fit reasonable scaling and spatial layout of object pairs with considering relationship between them.
Customizable Architecture Search for Semantic Segmentation
As a result, our CAS is able to search an optimized architecture with customized constraints.
Mocycle-GAN: Unpaired Video-to-Video Translation
Unsupervised image-to-image translation is the task of translating an image from one domain to another in the absence of any paired training examples and tends to be more applicable to practical applications.
Relation Distillation Networks for Video Object Detection
In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context.
daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices
It is always well believed that Binary Neural Networks (BNNs) could drastically accelerate the inference efficiency by replacing the arithmetic operations in float-valued Deep Neural Networks (DNNs) with bit-wise operations.
Convolutional Auto-encoding of Sentence Topics for Image Paragraph Generation
A valid question is how to encapsulate such gists/topics that are worthy of mention from an image, and then describe the image from one topic to another but holistically with a coherent structure.
Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation
To tackle the unsupervised domain adaptation problem, we explore the possibilities to generate high-quality labels as proxy labels to supervise the training on target data.
Hard-Aware Fashion Attribute Classification
For "hard" attributes with insufficient training data, Deact brings more stable synthetic samples for training and further improve the performance.
Learning Spatio-Temporal Representation with Local and Global Diffusion
Diffusions effectively interact two aspects of information, i. e., localized and holistic, for more powerful way of representation learning.
Ranked #4 on
Action Recognition
on UCF101
Group Re-Identification with Multi-grained Matching and Integration
The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem. Group re-identification (Re-ID) is a very challenging task sinceit is not only adversely affected by common issues in traditionalsingle object Re-ID problems such as viewpoint and human posevariations, but it also suffers from changes in group layout andgroup membership.
A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark
Each image is provided with accurate annotation of a 11-category pixel-level label map along with coordinates of 106-point landmarks.
Predictive Ensemble Learning with Application to Scene Text Detection
Taking scene text detection as the application, where no suitable ensemble learning strategy exists, PEL can significantly improve the performance, compared to either individual state-of-the-art models, or the fusion of multiple models by non-maximum suppression.
Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning
Moreover, the inherently recurrent dependency in RNN prevents parallelization within a sequence during training and therefore limits the computations.
Transferrable Prototypical Networks for Unsupervised Domain Adaptation
Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar.
Pointing Novel Objects in Image Captioning
Image captioning has received significant attention with remarkable improvements in recent advances.
Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks
Selfie and cartoon are two popular artistic forms that are widely presented in our daily life.
Unsupervised Person Image Generation with Semantic Parsing Transformation
Firstly, a semantic generative network is proposed to transform between semantic parsing maps, in order to simplify the non-rigid deformation learning.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
VrR-VG: Refocusing Visually-Relevant Relationships
Relationships encode the interactions among individual instances, and play a critical role in deep visual scene understanding.
Improved Selective Refinement Network for Face Detection
With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years.
Multi-Granularity Reasoning for Social Relation Recognition from Images
To bridge the domain gap, we propose a Multi-Granularity Reasoning framework for social relation recognition from images.
Support Vector Guided Softmax Loss for Face Recognition
Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination.
Ranked #1 on
Face Identification
on Trillion Pairs Dataset
ScratchDet: Training Single-Shot Object Detectors from Scratch
Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence.
KTAN: Knowledge Transfer Adversarial Network
To transfer the knowledge of intermediate representations, we set high-level teacher feature maps as a target, toward which the student feature maps are trained.
Exploring Visual Relationship for Image Captioning
Technically, we build graphs over the detected objects in an image based on their spatial and semantic connections.
Deep Attention Neural Tensor Network for Visual Question Answering
First, we model one of the pairwise interaction (e. g., image and question) by bilinear features, which is further encoded with the third dimension (e. g., answer) to be a triplet by bilinear tensor product.
Recurrent Tubelet Proposal and Recognition Networks for Action Detection
The RTP initializes action proposals of the start frame through a Region Proposal Network and then estimates the movements of proposals in next frame in a recurrent manner.
Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations.
DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks
Specifically, we jointly learn a deep attention encoder, and the instance-level correspondences could be consequently discovered through attending on the learned instances.
Subspace Clustering by Block Diagonal Representation
Second, we observe that many existing methods approximate the block diagonal representation matrix by using different structure priors, e. g., sparsity and low-rankness, which are indirect.
Jointly Localizing and Describing Events for Dense Video Captioning
A valid question is how to temporally localize and then describe events, which is known as "dense video captioning."
Fully Convolutional Adaptation Networks for Semantic Segmentation
The recent advances in deep neural networks have convincingly demonstrated high capability in learning vision models on large datasets.
Deep Semantic Hashing with Generative Adversarial Networks
Specifically, a novel deep semantic hashing with GANs (DSH-GANs) is presented, which mainly consists of four components: a deep convolution neural networks (CNN) for learning image representations, an adversary stream to distinguish synthetic images from real ones, a hash stream for encoding image representations to hash codes and a classification stream.
To Create What You Tell: Generating Videos from Captions
In this paper, we present a novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions.
Memory Matching Networks for One-Shot Image Recognition
In this paper, we introduce the new ideas of augmenting Convolutional Neural Networks (CNNs) with Memory and learning to learn the network parameters for the unlabelled images on the fly in one-shot learning.
Part-Aligned Bilinear Representations for Person Re-identification
We propose a novel network that learns a part-aligned representation for person re-identification.
Ranked #4 on
Person Re-Identification
on UAV-Human
To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression
Then, a multi-modal co-attention mechanism is introduced to generate not only video attention which reflects the global video structure, but also sentence attention which highlights the crucial details for temporal localization.
DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)
Specifically, we jointly learn a deep attention encoder, and the instancelevel correspondences could be consequently discovered through attending on the learned instance pairs.
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions
Most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer.
Time Matters: Multi-scale Temporalization of Social Media Popularity
We evaluate our approach on two large-scale Flickr image datasets with over 1. 8 million photos in total, for the task of popularity prediction.
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space.
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating $3\times3\times3$ convolutions with $1\times3\times3$ convolutional filters on spatial domain (equivalent to 2D CNN) plus $3\times1\times1$ convolutions to construct temporal connections on adjacent feature maps in time.
Ranked #8 on
Action Recognition
on Sports-1M
Learning Social Image Embedding with Deep Multimodal Attention Networks
Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search.
Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition
Two losses are proposed to guide the multi-task learning of channel grouping and part classification, which encourages MA-CNN to generate more discriminative parts from feature channels and learn better fine-grained features from parts in a mutual reinforced way.
Ranked #22 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Image Recognition
+2
Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge
Although convolutional neural networks (CNNs) have achieved promising results in learning such concepts, it remains an open question as to how to effectively use CNNs for abnormal event detection, mainly due to the environment-dependent nature of the anomaly detection.
Automatic Dataset Augmentation
Large scale image dataset and deep convolutional neural network (DCNN) are two primary driving forces for the rapid progress made in generic object recognition tasks in recent years.
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects
Image captioning often requires a large set of training image-sentence pairs.
Multi-Level Attention Networks for Visual Question Answering
To solve the challenges, we propose a multi-level attention network for visual question answering that can simultaneously reduce the semantic gap by semantic attention and benefit fine-grained spatial inference by visual attention.
Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition
The learning at each scale consists of a classification sub-network and an attention proposal sub-network (APN).
Fine-Grained Image Classification
Fine-Grained Image Recognition
+1
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model
In this paper, we present Fisher Vector encoding with Variational Auto-Encoder (FV-VAE), a novel deep architecture that quantizes the local activations of convolutional layer in a deep generative model, by training them in an end-to-end manner.
Video Captioning with Transferred Semantic Attributes
Automatically generating natural language descriptions of videos plays a fundamental challenge for computer vision community.
Boosting Image Captioning with Attributes
Automatically describing an image with a natural language has been an emerging challenge in both fields of computer vision and natural language processing.
Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network
Second, by using sGRU as basic units, the BMRNN is trained to align the local storylines into the global sequential timeline.
Highlight Detection With Pairwise Deep Ranking for First-Person Video Summarization
The emergence of wearable devices such as portable cameras and smart glasses makes it possible to record life logging first-person videos.
You Lead, We Exceed: Labor-Free Video Concept Learning by Jointly Exploiting Web Videos and Images
The Web images are then filtered by the learnt network and the selected images are additionally fed into the network to enhance the architecture and further trim the videos.
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
In this paper we present MSR-VTT (standing for "ABC-Video to Text") which is a new large-scale video benchmark for video understanding, especially the emerging task of translating video to text.
A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes
These semantic regions can be used to recognize pre-defined activities in crowd scenes.