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Posts
Blog Test Ai Bullet Time
Published:
title: Bullet Time of AI Video Generation Tools date: 2025-11-29 permalink: /posts/2025-11-29-blog-test-ai-bullet-time/ tags:
Emotional companionship of AI
Published:
测试的时间是2025年11月18日，因为AI日新月异，更新很快，所以，测试结果仅供参考
RAG-SEG For Medicine
Published:
Paper Link: https://arxiv.org/abs/2508.15313
RAG-SEG( First RAG, Second SEG: A Training-Free Paradigm for Camouflaged Object Detection )
Published:
Paper Link: https://arxiv.org/abs/2508.15313
publications
Unified Unsupervised Salient Object Detection via Knowledge Transfer
Published in International Joint Conference on Artificial Intelligence (IJCAI) 2024, 2024
A unified framework for unsupervised salient object detection (USOD), featuring curriculum learning-based saliency distilling and knowledge transfer across tasks.
Recommended citation: Yao Yuan, Wutao Liu, Pan Gao, Qun Dai, Jie Qin. (2024). "Unified Unsupervised Salient Object Detection via Knowledge Transfer." IJCAI 2024.
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ACGFormer: Attribute Classification Guided Transformer for Camouflaged Object Detection
Published in PRCV 2025, 2025
ACGFormer introduces an Attribute Classification Guided Transformer for camouflaged object detection, leveraging attribute-aware guidance and feature refinement to achieve state-of-the-art performance.
Recommended citation: Wutao Liu, Yao Yuan, Pan Gao, Zheng Lin, Jie Qin. (2025). "ACGFormer: Attribute Classification Guided Transformer for Camouflaged Object Detection." PRCV.
First RAG, Second SEG: A Training-Free Paradigm for Camouflaged Object Detection
Published in Conference Paper (Preprint on arXiv), 2025
RAG-SEG presents a training-free paradigm for camouflaged object detection (salient obect detection) by decoupling the task into retrieval-augmented generation of coarse masks and SAM-based refinement, eliminating conventional training while achieving competitive results on benchmark datasets with only a personal laptop.
Recommended citation: Wutao Liu, Yidan Wang, Pan Gao. (2025). "First RAG, Second SEG: A Training-Free Paradigm for Camouflaged Object Detection." Conference Paper (Preprint on arXiv).
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AlignCAT: Visual-Linguistic Alignment of Category and Attribute for Weakly Supervised Visual Grounding
Published in ACM International Conference on Multimedia (ACMMM) 2025, 2025
AlignCAT introduces a query-based semantic matching framework for weakly supervised visual grounding, employing coarse-grained category alignment and fine-grained attribute alignment to enhance visual-linguistic reasoning and achieve state-of-the-art performance on RefCOCO, RefCOCO+, and RefCOCOg.
Recommended citation: Yidan Wang, Chenyi Zhuang, Wutao Liu, Pan Gao, Nicu Sebe. (2025). "AlignCAT: Visual-Linguistic Alignment of Category and Attribute for Weakly Supervised Visual Grounding." ACMMM 2025.
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