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cs.CV2026
Rethinking Long-Video Efficiency: A Joint Allocation Perspective on Frames, Pixels, and Front-End Latency
Sixun Dong, Wei Li, Andong Deng +4
Efficient long-video understanding with vision-language models (VLMs) is often framed as selecting informative frames or visual tokens at a fixed native resolution. We show that pe…
cs.CV2026
How Should Video LLMs Output Time? An Analysis of Efficient Temporal Grounding Paradigms
Shengji Jin, Yuanhao Zou, Victor Zhu +2
While Multimodal Large Language Models (MLLMs) have advanced Video Temporal Grounding (VTG), existing methods often couple output paradigms with different backbones, datasets, and…
cs.CV2024
EVLM: Self-Reflective Multimodal Reasoning for Cross-Dimensional Visual Editing
Umar Khalid, Kashif Munir, Hasan Iqbal +6
Editing complex visual content from ambiguous or partially specified instructions remains a core challenge in vision-language modeling. Existing models can contextualize content bu…