Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks213 from public data
- A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance641
This paper proposes a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”, which treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near- to-distantsearch in the real world surveillance environment.
- 561
- FastReID: A Pytorch Toolbox for General Instance Re-identification465
This work presents FastReID as a widely used software system that supports single and multiple GPU servers, it can reproduce the project results very easily, and has implemented some state-of-the-art projects, including person re-id, partial re-ids, cross-domain re-ID, and vehicle re-identification.
- 443
- Monocular, One-stage, Regression of Multiple 3D People356
ROMP is the first real-time implementation of monocular multi-person 3D mesh regression, and achieves superior performance on the challenging multi- person benchmarks, including 3DPW and CMU Panoptic.
- Gait Recognition in the Wild with Dense 3D Representations and A Benchmark289
This paper proposes a novel framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the human body for gait recognition, named SMPLGait, and provides 3D SMPL models recovered from video frames which can provide dense 3D information of body shape, viewpoint, and dynamics.
- Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective246
This article comprehensively summarize the 2D and 3D representations of human body and summarizes the mainstream and milestone approaches for these human body presentations since the year 2014 under unified frameworks to provide a comprehensive and holistic 2D-to-3D perspective.
- Foreground-Aware Pyramid Reconstruction for Alignment-Free Occluded Person Re-Identification228
An alignment-free matching approach namely Foreground-aware Pyramid Reconstruction (FPR) is developed to accurately compute matching scores between occluded persons, regardless of their different scales and sizes and a occlusion-sensitive foreground probability generator is designed that focuses more on clean human body parts to robustify the similarity computation with less contamination from occlusions.
- Putting People in their Place: Monocular Regression of 3D People in Depth220
A novel method to infer the poses and depth of multiple people in a single image that out-performs existing methods on depth reasoning, child shape estimation, and robustness to occlusion.
- Group-aware Label Transfer for Domain Adaptive Person Re-identification216
A group-aware Label Transfer (GLT) algorithm is proposed, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning and can better correct the noisy pseudo label in an online fashion and narrow down the search space of the target identity.
- Human Mesh Recovery From Monocular Images via a Skeleton-Disentangled Representation201
A skeleton-disentangled based framework is proposed, which divides this task into multi-level spatial and temporal granularity in a decoupling manner, and an effective and pluggable “disentangling the skeleton from the details” (DSD) module is proposed.
- 200
- Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning171
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.
- Content-Consistent Matching for Domain Adaptive Semantic Segmentation168
This paper considers the adaptation of semantic segmentation from the synthetic source domain to the real target domain and proposes a content-consistent matching (CCM) model, which can alleviate the domain bias caused by those content-irrelevant synthetic images.
- T-C3D: Temporal Convolutional 3D Network for Real-Time Action Recognition145
A new real-time action recognition architecture, called Temporal Convolutional 3D Network (T-C3D), which learns video action representations in a hierarchical multi-granularity manner, demonstrating comparable recognition performance to the state-of-the-art methods.
- Guided Saliency Feature Learning for Person Re-identification in Crowded Scenes128
A new matching approach, called Guided Adaptive Spatial Matching (GASM), which expects that each spatial feature in the query can be the most similar spatial features of a person in a gallery to match.
- TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments124
TRACE is the first one-stage method to jointly recover and track 3D humans in global coordinates from dynamic cameras, and achieves state-of-the-art performance on tracking and HPS benchmarks.
- Cross-Modality Transformer With Modality Mining for Visible-Infrared Person Re-Identification124
This work proposes a new cross-modality transformer-based method (CMTR) for this visible-infrared person re-identification task, which can explicitly mine the information of each modality and generate better discriminative features based on it, and is the first exploration of applying pure transformer network to the cross- modality re-Identification task.
- Hierarchical Gumbel Attention Network for Text-based Person Search106
A novel hierarchical Gumbel attention network for text-based person search via Gumbels top-k re-parameterization algorithm that adaptively selects the strong semantically relevant image regions and words/phrases from images and texts for precise alignment and similarity calculation.
- 89
- MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification82
This work proposes an edge features enhancement module to enhance the modality-sharable features in each single-modality space and designs a perceptual edge features (PEF) loss after the edge fusion strategy analysis, which markedly improves the network's performance.
- 82
- Third-Eye76
Two deep learning models are leveraged, Convolutional Neural Network for images and Long Short Term Memory network for weather and air-pollution data, to build an end-to-end framework for training PM2.5 inference models.
- 75
- Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification74
A gait energy image (GEI) based Siamese neural network is proposed to automatically extract robust and discriminative spatial gait features for human identification and this framework impressively outperforms state-of-the-art methods.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.