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20232026
most citedRPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection

4 citations · 4 across the 6 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

Scaling Representation Diversity: Modulated Attention and Reconstructive Regularization for Visual Grounding

Junyi Hu, Tian Bai, Fengyi Wu +7

Referring Expression Comprehension (REC) is commonly studied under dataset-specific fine-tuning, resulting in specialist models with limited cross-dataset generalization. In this w…

cs.CV2026

LCPNet: Latent Consistent Proximal Unfolding Network for Infrared Small Target Detection

Tianfang Zhang, Lei Li, Chang Liu +3

Infrared small target detection (IRSTD) aims to identify long distance small targets from complex infrared backgrounds, and is a fundamental task in remote sensing. Deep learning m…

cs.CV2026

ExpAlign: Expectation-Guided Vision-Language Alignment for Open-Vocabulary Grounding

Junyi Hu, Tian Bai, Fengyi Wu +3

Open-vocabulary grounding requires accurate vision-language alignment under weak supervision, yet existing methods either rely on global sentence embeddings that lack fine-grained…

cs.CV2025

RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation

Fengyi Wu, Yimian Dai, Tianfang Zhang +4

Robust principal component analysis (RPCA) decomposes an observation matrix into low-rank background and sparse object components. This capability has enabled its application in ta…

cs.CV20253 cited

PHCT: Plug-and-Play Hierarchical C2F Transformer for Multi-Scale Feature Fusion

Junyi Hu, Tian Bai, Fengyi Wu +2

Feature fusion plays a pivotal role in achieving high performance in vision models, yet existing attention-based fusion techniques often suffer from substantial computational overh…

cs.CV2024

Neural Spatial-Temporal Tensor Representation for Infrared Small Target Detection

Fengyi Wu, Simin Liu, Haoan Wang +3

Optimization-based approaches dominate infrared small target detection as they leverage infrared imagery's intrinsic low-rankness and sparsity. While effective for single-frame ima…