An Attention-level Causal Intervention Framework (ACIM), which performs causal adjustment directly within the attention learning process to mitigate confounding effects and estimate attention-level intervention effects without requiring fully observed confounders.
Si-Qi Hao, Shuo-Hao Li, Rong-Xin Lin et al.· Big Data and Cognitive Compu...· 0 citations
With the rapid development of Generative Artificial Intelligence (GAI) technology, multimodal fake media has spread widely on the Internet, posing a serious threat to the social information ecosystem. Existing multimodal manipulation detection methods mostly adopt a holistic feature fusion strategy, which is difficult...
Yi Zhou, Hao-Yang Wan, Ke-Yu Wu et al.· 2026 12th International Conf...· 0 citations
Recognizing spatial geometric relationships between objects is crucial for machines to understand the physical world. Although introducing the depth modality can effectively mitigate the ambiguities caused by 2D visual projection, existing cross-modal fusion paradigms heavily rely on computationally expensive late-stag...
Xu Zhang, Wei Chen, Qing-Hao Meng et al.· 2026 12th International Conf...· 0 citations
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