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Speculative decoding accelerates LLM generation through lightweight drafting and parallel verification. It is particularly promising for long inputs, where KV-cache access costs can be amortized over multiple accepted tokens. Yet most drafters are trained on short sequences and degrade on long inputs, while long-contex...
Multimodal Large Language Models (MLLMs) have demonstrated strong performance in video understanding, yet efficiently processing long, high-resolution videos remains challenging. Such videos often contain substantial spatiotemporal redundancy, and processing redundant visual tokens can incur avoidable computational ove...
An AVQA-specific two-stage framework that adapts established cross-modal reconstruction and dependency-modeling principles to the supervision constraints of TM-AVQA is developed, a setting in which modality-complete, audio-missing, and visual-missing samples may occur during both training and testing.
Speech-guided referring video object segmentation aims to recover the mask tracks of objects specified by a spoken motion description. Here, speech carries a linguistic instruction rather than acoustic evidence from a sounding object, so a solution must connect speech recognition, motion-centric temporal grounding, mas...
VideoVIBE is introduced, a video-grounded benchmark that transforms human-operated webpage recordings into fine-grained diagnostic tasks and V2Lens is proposed, a training-free, evidence-grounded multi-agent system that challenges and selectively refines initial video-based diagnoses through targeted visual and source-...
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