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This work proposes UltraG-Agent, which combines the semantic reasoning capabilities of a VLM with the ultrasound-specific segmentation capability of UltraSAM3, and shows that UltraG-Agent substantially improves both semantic prediction and pixel-level visual grounding.
Experimental results on three large-scale fMRI datasets demonstrate that BrainCGT achieves consistently better performance than existing graph-based methods for neurological disorder classification, highlighting the importance of incorporating causal directionality into brain graph transformer architectures for robust...
Hateful memes spread abusive content through implicit interactions between images and text, posing serious threats to the safety of online communities. In recent years, multimodal large language models have been widely used for hateful meme detection and are increasingly adopted to generate explainable detection result...
This work proposes MedVCoT, which incorporates latent visual reasoning into the medical visual question answering (VQA) domain, and utilizes the specialized expertise of MedSAM to train a large vision-language model so that it can autonomously generate consistent and continuous latent visual tokens within Visual Chain-...
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