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20222024
most citedExploring Invariant Representation for Visible-Infrared Person Re-Identification

10 citations · 17 across the 7 of their papers we have counts for

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10 papers · 1 filter

cs.CV20251 cited

More Clear, More Flexible, More Precise: A Comprehensive Oriented Object Detection benchmark for UAV

Kai Ye, Haidi Tang, Bowen Liu +3

Applications of unmanned aerial vehicle (UAV) in logistics, agricultural automation, urban management, and emergency response are highly dependent on oriented object detection (OOD…

cs.CV2024

Knowing Where to Focus: Attention-Guided Alignment for Text-based Person Search

Lei Tan, Weihao Li, Pingyang Dai +3

In the realm of Text-Based Person Search (TBPS), mainstream methods aim to explore more efficient interaction frameworks between text descriptions and visual data. However, recent…

cs.CV2024

RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification

Lei Tan, Yukang Zhang, Keke Han +4

This paper makes a step towards modeling the modality discrepancy in the cross-spectral re-identification task. Based on the Lambertain model, we observe that the non-linear modali…

cs.CV2024

Feature Denoising Diffusion Model for Blind Image Quality Assessment

Xudong Li, Jingyuan Zheng, Runze Hu +8

Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically de…

cs.CV2024

Prompt Decoupling for Text-to-Image Person Re-identification

Weihao Li, Lei Tan, Pingyang Dai +1

Text-to-image person re-identification (TIReID) aims to retrieve the target person from an image gallery via a textual description query. Recently, pre-trained vision-language mode…

cs.CV2023

Spectral Aware Softmax for Visible-Infrared Person Re-Identification

Lei Tan, Pingyang Dai, Qixiang Ye +3

Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Although suffering an extra modality discrepancy, existing m…