Search Results for author:
Found 46 papers, 17 papers with code
Content-Consistent Matching for Domain Adaptive Semantic Segmentation
The target of CCM is to acquire those synthetic images that share similar distribution with the real ones in the target domain, so that the domain gap can be naturally alleviated by employing the content-consistent synthetic images for training.
Ranked #11 on
Semantic Segmentation
on GTAV-to-Cityscapes Labels
Guided Saliency Feature Learning for Person Re-identification in Crowded Scenes
More importantly, we propose a new matching approach, called Guided Adaptive Spatial Matching (GASM), which expects that each spatial feature in the query can find the most similar spatial features of a person in a gallery to match.
Parsing is All You Need for Accurate Gait Recognition in the Wild
Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset.
Bridging the Gap: Multi-Level Cross-Modality Joint Alignment for Visible-Infrared Person Re-Identification
Visible-Infrared person Re-IDentification (VI-ReID) is a challenging cross-modality image retrieval task that aims to match pedestrians' images across visible and infrared cameras.
TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments
Although the estimation of 3D human pose and shape (HPS) is rapidly progressing, current methods still cannot reliably estimate moving humans in global coordinates, which is critical for many applications.
Ranked #36 on
3D Human Pose Estimation
on 3DPW
Learning To Segment Every Referring Object Point by Point
Extensive experiments show that our model achieves 52. 06% in terms of accuracy (versus 58. 93% in fully supervised setting) on RefCOCO+@testA, when only using 1% of the mask annotations.
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.
DPIT: Dual-Pipeline Integrated Transformer for Human Pose Estimation
Human pose estimation aims to figure out the keypoints of all people in different scenes.
Ranked #25 on
Pose Estimation
on COCO test-dev
Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space
To close the frequency gap between the natural and synthetic videos, we propose a novel Frequency-based human MOtion TRansfer framework, named FreMOTR, which can effectively mitigate the spatial artifacts and the temporal inconsistency of the synthesized videos.
Gait Recognition in the Wild with Multi-hop Temporal Switch
Current methods that obtain state-of-the-art performance on in-the-lab benchmarks achieve much worse accuracy on the recently proposed in-the-wild datasets because these methods can hardly model the varied temporal dynamics of gait sequences in unconstrained scenes.
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.
REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer
Human Video Motion Transfer (HVMT) aims to, given an image of a source person, generate his/her video that imitates the motion of the driving person.
SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual Grounding
Particularly, SiRi conveys a significant principle to the research of visual grounding, i. e., a better initialized vision-language encoder would help the model converge to a better local minimum, advancing the performance accordingly.
CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks
Detection of object anomalies is crucial in industrial processes, but unsupervised anomaly detection and localization is particularly important due to the difficulty of obtaining a large number of defective samples and the unpredictable types of anomalies in real life.
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
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.
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)
MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification
The RGB-infrared cross-modality person re-identification (ReID) task aims to recognize the images of the same identity between the visible modality and the infrared modality.
Cross-Modality Person Re-identification
Person Re-Identification
CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification
The existing convolutional neural network-based methods mainly face the problem of insufficient perception of modalities' information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance.
Cross-Modality Person Re-identification
Person Re-Identification
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.
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.
Ranked #16 on
Person Re-Identification
on Market-1501
(using extra training data)
Generalizable Person Re-identification
Knowledge Distillation
+1
Learning Instance-level Spatial-Temporal Patterns for Person Re-identification
In this paper, we propose a novel Instance-level and Spatial-Temporal Disentangled Re-ID method (InSTD), to improve Re-ID accuracy.
Ranked #14 on
Person Re-Identification
on DukeMTMC-reID
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.
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.
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.
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 #44 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.
Neural Architecture Search for Joint Human Parsing and Pose Estimation
With the spirit of NAS, we propose to search for an efficient network architecture (NPPNet) to tackle two tasks at the same time.
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.
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Specifically, LAFE utilizes the region attention modules and channel attention modules to extract discriminative features and confusable features respectively.
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 #13 on
Text based Person Retrieval
on CUHK-PEDES
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.
A Real-time Action Representation with Temporal Encoding and Deep Compression
Our method achieves clear improvements on UCF101 action recognition benchmark against state-of-the-art real-time methods by 5. 4% in terms of accuracy and 2 times faster in terms of inference speed with a less than 5MB storage model.
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
Deep Recurrent Quantization for Generating Sequential Binary Codes
To the end, when the model is trained, a sequence of binary codes can be generated and the code length can be easily controlled by adjusting the number of recurrent iterations.
Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification
FPR uses the error from robust reconstruction over spatial pyramid features to measure similarities between two persons.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
PVSS: A Progressive Vehicle Search System for Video Surveillance Networks
In this paper, a Progressive Vehicle Search System, named as PVSS, is designed to solve the above problems.
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.
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.
An efficient deep learning hashing neural network for mobile visual search
Mobile visual search applications are emerging that enable users to sense their surroundings with smart phones.
Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data
Motivated by this, we study the task of action recognition in surveillance video under a more realistic \emph{generalized zero-shot setting}, where testing data contains both seen and unseen classes.
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning
Specifically, the proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information.
Multi-Task Deep Visual-Semantic Embedding for Video Thumbnail Selection
Given the tremendous growth of online videos, video thumbnail, as the common visualization form of video content, is becoming increasingly important to influence user's browsing and searching experience.
