Recent 3D large multimodal models (3D-LMMs) rely on a visual bottleneck to compress complex 3D scene evidence into a limited number of visual tokens compatible with large language models (LLMs). Current visual bottlenecks, however, often passively compress heterogeneous 3D evidence into a homogeneous object-centric tok...
Xiang-Qi Li, Li-Bo Huang, Jia-Rui Zhao et al.· 0 citations
Large-scale video diffusion models (V-DMs) have achieved remarkable text-to-video generation quality, yet their massive computational complexity makes deployment costly. Post-Training Quantization (PTQ) offers an appealing route to accelerate inference without retraining, but existing diffusion PTQ methods remain fragi...
Wei-Lun Feng, Chuan-Guang Yang, Haotong Qin et al.· IEEE Transactions on Pattern...· 3 citations
P predictive Prompting (PrePrompt) is proposed, a novel CIL framework that circumvents correlation-based limitations by leveraging the inherent classification ability of pre-trained models to predict task-specific prompts and decomposes CIL into a two-stage prediction process: task-specific prompt prediction followed b...
Libo Huang, Xiang-Qi Li, Jia-Rui Zhao et al.· Proceedings of the 32nd ACM...· 4 citations
Prompt-based learning has emerged as a promising paradigm for Class Incremental Learning (CIL), enabling pre-trained models to adapt efficiently to open-world scenarios. Existing methods often employ correlation-based strategies, where an image's feature serves as a query to retrieve the most relevant key prompts, with...
Libo Huang, Xiangqi Li, Jiarui Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
RSIAT significantly outperforms state-of-the-art methods in both performance and parameter efficiency, achieving superior stability–plasticity trade-offs with minimal trainable parameters.
Jiarui Zhao, Libo Huang, Xiangqi Li et al.· 0 citations
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