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Found 8 papers, 4 papers with code
Revisit Weakly-Supervised Audio-Visual Video Parsing from the Language Perspective
Specifically, we design language prompts to describe all cases of event appearance for each video.
Visible-Infrared Person Re-Identification via Patch-Mixed Cross-Modality Learning
In this paper, we propose a Patch-Mixed Cross-Modality framework (PMCM), where two images of the same person from two modalities are split into patches and stitched into a new one for model learning.
MixSiam: A Mixture-based Approach to Self-supervised Representation Learning
In this way, the model could access more variant data samples of an instance and keep predicting invariant discriminative representations for them.
Unsupervised Person Re-identification with Stochastic Training Strategy
State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning.
Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric
In essence, current re-ID overemphasizes the importance of retrieval but underemphasizes that of verification, \textit{i. e.}, all returned images are considered as the target.
Unsupervised Person Re-identification via Softened Similarity Learning
Person re-identification (re-ID) is an important topic in computer vision.
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning
We focus on the one-shot learning for video-based person re-Identification (re-ID).
Improving Person Re-identification by Attribute and Identity Learning
Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions.
Ranked #73 on
Person Re-Identification
on DukeMTMC-reID
