arXiv:2608.21710v1 Announce Type: new
Abstract: With the advance of deep neural networks, the quality of disparity maps obtained through stereo matching has steadily improved. However, existing stere...
By Bohan Li
arXiv:2507.21816v1 Announce Type: cross
Abstract: Few-shot object detection (FSOD) for optical remote sensing images aims to detect rare objects with only a few annotated bounding boxes. The limited...
By Yanxing Liu, Jiancheng Pan, Bingchen Zhang
The paper introduces RoSA, a saliency-aware fine‑tuning framework for long‑text image‑text alignment that addresses the failure mode of saliency collapse. RoSA decomposes image‑text pairs into salient and contextual views using a modality‑independent strategy that combines visual saliency estimation with LLM‑based text decomposition, and employs auxiliary objectives to align these views. The method improves long‑text retrieval performance and robustness to saliency collapse, and its auxiliary objectives can be added to existing tuning methods for consistent gains.
By Qiuyu Kong, Zanxi Ruan, Marco Cristani, Yiming Wang
arXiv:2505.16157v3 Announce Type: replace
Abstract: Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Tran...
By Yuang Ai
arXiv:2511.18031v1 Announce Type: cross
Abstract: Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that i...
By Yanxing Liu, Jiancheng Pan, Jianwei Yang, Tiancheng Chen, Peiling Zhou, Bingchen Zhang
arXiv:2609.13706v1 Announce Type: new
Abstract: Co-salient object detection (Co-SOD) requires a model to find foreground regions that are salient in individual images and supported by the image group...
By Yuan Xiang, Matteo Rossi, Yingzhou Chen