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cs.CV2025
DeepInsert: Early Layer Bypass for Efficient and Performant Multimodal Understanding
Moulik Choraria, Xinbo Wu, Akhil Bhimaraju +5
Hyperscaling of data and parameter count in LLMs is yielding diminishing improvement when weighed against training costs, underlining a growing need for more efficient finetuning a…
cs.MM2025
MAVERIX: Multimodal Audio-Visual Evaluation and Recognition IndeX
Liuyue Xie, Avik Kuthiala, George Z. Wei +12
We introduce MAVERIX (Multimodal audiovisual Evaluation and Recognition IndeX), a unified benchmark to probe the video understanding in multimodal LLMs, encompassing video, audio,…
cs.CV2025
Achieving More with Less: Additive Prompt Tuning for Rehearsal-Free Class-Incremental Learning
Haoran Chen, Ping Wang, Zihan Zhou +3
Class-incremental learning (CIL) enables models to learn new classes progressively while preserving knowledge of previously learned ones. Recent advances in this field have shifted…