Search Results for author:
Found 16 papers, 10 papers with code
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.
Recent Advances in OOD Detection: Problems and Approaches
Out-of-distribution (OOD) detection aims to detect test samples outside the training category space, which is an essential component in building reliable machine learning systems.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
Learning Feature Recovery Transformer for Occluded Person Re-identification
To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity.
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 #2 on
Gait Recognition
on Gait3D
Mimic Embedding via Adaptive Aggregation: Learning Generalizable Person Re-identification
Meanwhile, META considers the relevance of an unseen target sample and source domains via normalization statistics and develops an aggregation module to adaptively integrate multiple experts for mimicking unseen target domain.
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 #2 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
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.
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.
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
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.
Recognizing Partial Biometric Patterns
Biometric recognition on partial captured targets is challenging, where only several partial observations of objects are available for matching.
SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress.
Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention
Video-based person re-identification (ReID) is a challenging problem, where some video tracks of people across non-overlapping cameras are available for matching.
Dynamic Feature Learning for Partial Face Recognition
Partial face recognition (PFR) in unconstrained environment is a very important task, especially in video surveillance, mobile devices, etc.
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach
Experimental results on two partial person datasets demonstrate the efficiency and effectiveness of the proposed method in comparison with several state-of-the-art partial person re-id approaches.
