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Found 6 papers, 4 papers with code
TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation
In addition, most of current Transformer-based ReID methods only utilize the global feature of class tokens to achieve the holistic retrieval, ignoring the local discriminative ones.
TF-CLIP: Learning Text-free CLIP for Video-based Person Re-Identification
Technically, TMC allows the frame-level memories in a sequence to communicate with each other, and to extract temporal information based on the relations within the sequence.
Video-based Person Re-identification with Long Short-Term Representation Learning
Meanwhile, to extract short-term representations, we propose a Bi-direction Motion Estimator (BME), in which reciprocal motion information is efficiently extracted from consecutive frames.
Representation Learning
Video-Based Person Re-Identification
Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification
Further, in spatial, we propose a Complementary Content Attention (CCA) to take advantages of the coupled structure and guide independent features for spatial complementary learning.
A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification
To capture richer perceptions and extract more comprehensive video representations, in this paper we propose a novel framework named Trigeminal Transformers (TMT) for video-based person Re-ID.
Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification
Specifically, we first propose a Global-guided Correlation Estimation (GCE) to generate feature correlation maps of local features and global features, which help to localize the high- and low-correlation regions for identifying the same person.
