Extensive experiments demonstrate that the on-device latency-informed design combined with the tailored training strategy establishes a new state-of-the-art for efficient LVLM encoding, significantly outperforming existing encoder-centric baselines while operating on-device at nearly 1.7xthe speed.
Ioannis Maniadis Metaxas, Adrian Bulat, Alberto Baldrati et al.· arXiv.org· 0 citations
CoFi-Lite is proposed, a highly efficient model that decouples spectral modeling into coarse- and fine-grained streams that outperforms the ultra-lightweight baseline GTCRN while requiring only 40.26% of its computational complexity.
Leyan Yang, Dahan Wang, Xiaobin Rong et al.· IEEE Signal Processing Lette...· 0 citations
Mage-VL is presented, an efficient codec-native streaming foundation model for real-time multimodal understanding and interaction and establishes AI4AI data pipelines encompassing prompt-code joint optimization for multimodal captioning and AI-driven performance diagnosis to guide training recipes.
The “Prompt for Quantization” (P4Q) is proposed, by integrating PTQ with Parameter-Efficient Fine-Tuning (PEFT) techniques, and demonstrates that P4Q significantly enhances the performance of low-bit CLIP while reducing deployment costs.
H. Sun, Runqi Wang, Yanjing Li et al.· ACM Transactions on Multimed...· 0 citations
An attention-free and lightweight token reduction framework as a plug-and-play module for VLMs, which preserves both important and diverse tokens to produce a compact visual representation, and achieves a favorable accuracy-efficiency trade-off.
Xuanyi Hao, Zuoyuan Zhang, Zhibo Wang et al.· arXiv.org· 0 citations
This work systematically study MoE designs for vision encoder scaling and finds that fine-grained MoE topologies yield substantial gains over both dense and standard MoE counterparts, and proposes an auxiliary-loss-free balancing variant for better expert utilization, and designs a specialized MoE kernel to mitigate in...
Bonan Zhang, Shiyu Dong, Quan Hung Tran et al.· 1 citation
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