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Chenglong Li

5 papers indexed

arxivcs.CV2026-07-22

DRGBT-1K: A Large-scale High-quality Benchmark for Dynamic RGBT Tracking

Zhaodong Ding, Chenglong Li, Zeyu Ding, Futian Wang, Jin Tang

Dynamic RGBT (DRGBT) tracking aims to continuously localize a target when the available sensing modalities and observation platforms vary over time. Compared with conventional RGBT tracking with fixed RGBT inputs and a fixed observation platform, DRGBT tracking is more consistent…

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arxivcs.CV2026-07-21

Cross-Modal UAV Object Tracking: State-Aware Representation Learning and A Unified Benchmark

Yun Xiao, Zhihong Hong, Jiandong Jin, Chenglong Li, Jin Tang, Amir Hussain

Unmanned Aerial Vehicle (UAV) object tracking has emerged as a popular research field with broad practical applications. Modern UAVs are increasingly equipped with both visible light and thermal infrared sensors. However, due to constraints in communication bandwidth, computation…

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arxiveess.SP2026-07-16

Learning-Driven Channel Representation for Wireless Localization: From Channel Observations to Location Inference

Hongyu Xie, Chenglong Li, Xinming Huang, Emmeric Tanghe, Wout Joseph, Shaojie Ni, et al.

Wireless observations capture radio signal responses formed through interactions with propagation environments and spatial geometry. In integrated sensing and communication, such observations have become an important basis for high-accuracy localization beyond conventional channe…

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arxivcs.CV2026-07-09

Dual-Correlation Hypergraph Network for Unaligned RGBT Video Object Detection and A Large-scale Benchmark

Qishun Wang, Yapeng Li, Bin Luo, Zhengzheng Tu, Chenglong Li

RGB-Thermal (RGBT) Video Object Detection (VOD) has gained significant traction due to its ability to overcome the limitations of conventional RGB-based VOD under challenging conditions. However, spatial misalignment commonly exists between RGBT image pairs. To address this, we p…

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arxivcs.CV2026-07-09

Mixture of Enhanced-View Experts for Multi-Query Vehicle ReID and A Large-Scale Benchmark

Aihua Zheng, Jie Zhen, Chenglong Li, Jiaxiang Wang, Jin Tang

Multi-query vehicle ReID aims to leverage complementary information from diverse views for robust feature learning. However, current methods suffer from simplistic feature fusion and thus easily ignores some important view information and cross-view relationships. To handle these…

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