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- Challenge-Aware RGBT Tracking206
A novel challenge-aware neural network to handle the modality-shared challenges and the modalities-specific ones for RGBT tracking and proposes a guidance module to transfer discriminative features from one modality to another one, which could enhance the discrim inative ability of some weak modality.
- Multi-Adapter RGBT Tracking203
A novel Multi-Adapter convolutional Network (MANet) is proposed to jointly perform modality-shared, modalities-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking.
- Duality-Gated Mutual Condition Network for RGBT Tracking189
A novel duality-gated mutual condition network is proposed to fully exploit the discriminative information of all modalities while suppressing the effects of data noise to improve the quality of conditions and further reduce data noise.
- Transformer RGBT Tracking With Spatio-Temporal Multimodal Tokens62
A novel Transformer RGBT tracking approach, which mixes spatio-temporal multimodal tokens from the static multimodal templates and multimodal search regions in Transformer to handle target appearance changes, for robust RGBT tracking.
- Modality-missing RGBT Tracking: Invertible Prompt Learning and High-quality Benchmarks43
A novel invertible prompt learning approach is proposed, which integrates the content-preserving prompts into a well-trained tracking model to adapt to various modality-missing scenarios, for robust RGBT tracking.
- Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation32
This work proposes a novel Coupled Knowledge Distillation framework called CKD, which pursues common styles of different modalities to break modality gap, for high performance RGBT tracking, and introduces two student networks and employs the style distillation loss to make their style features consistent as much as possible.
- AFTER: Attention-Based Fusion Router for RGBT Tracking31
This work presents a novel Attention-based Fusion router called AFTER, which optimizes the fusion structure to adapt to the dynamic challenging scenarios, for robust RGBT tracking.
- Illumination Distillation Framework for Nighttime Person Re-Identification and a New Benchmark31
An Illumination Distillation Framework (IDF), which utilizes illumination enhancement and illumination distillation schemes to promote the learning of Re-ID models and builds a real-world nighttime person Re-ID dataset, named Night600, which contains 600 identities captured from different viewpoints and nighttime illumination conditions under complex outdoor environments.
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- Nighttime Person Re-Identification via Collaborative Enhancement Network With Multi-Domain Learning15
A novel Collaborative Enhancement Network called CENet is proposed, which performs the multilevel feature interactions in a parallel framework, for nighttime person ReID, thereby alleviating the severe image degradation issue caused by the nighttime scenario while avoiding the impact of relighting images.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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