5 papers
SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm
Jiandong Jin, Xiao Wang, Yin Lin +4
Current pedestrian attribute recognition (PAR) algorithms use multi-label or multi-task learning frameworks with specific classification heads. These models often struggle with imb…
Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark
Andong Lu, Zhang Zhang, Yan Huang +4
Nighttime person Re-ID (person re-identification in the nighttime) is a very important and challenging task for visual surveillance but it has not been thoroughly investigated. Und…
Multi-query Vehicle Re-identification: Viewpoint-conditioned Network, Unified Dataset and New Metric
Aihua Zheng, Chaobin Zhang, Weijun Zhang +4
Existing vehicle re-identification methods mainly rely on the single query, which has limited information for vehicle representation and thus significantly hinders the performance…
Dynamic Enhancement Network for Partial Multi-modality Person Re-identification
Aihua Zheng, Ziling He, Zi Wang +2
Many existing multi-modality studies are based on the assumption of modality integrity. However, the problem of missing arbitrary modalities is very common in real life, and this p…
RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning
Yabin Zhu, Chenglong Li, Xiao Wang +2
Existing Transformer-based RGBT tracking methods either use cross-attention to fuse the two modalities, or use self-attention and cross-attention to model both modality-specific an…