This paper proposes a simple but efficient MLLM-based framework StreamingRVOS, which can extend image-level segmentation to video-level via a streaming pipeline without introducing extra parameters and achieves excellent performance in referring video segmentation.
Referring Video Object Segmentation (RVOS) aims to segment the target objects specified in human instructions. Previous approaches typically rely on explicit human instructions that contain target categories or salient appearance descriptions. These approaches tend to fail when the instructions require temporal video u...
Yanyan Shao, Shuting He, Gengze Zhou et al.· IEEE Transactions on Image P...· 0 citations
STITCH is presented, a training-free method that divides a video into semantically meaningful temporal chunks that are computed once per video and reused across tasks, suggesting that reusable temporal abstraction is a promising direction for general video understanding.
Etienne Casanova, S. Brodjian, Pietro Perona· 0 citations
This paper proposes an Intrinsic Temporal Adaptation (ITA) framework for PRVR, and introduces Affinity-Weighted Gradient Propagation to address the weakly supervised nature of PRVR, softly aggregating top-$k$ frames based on text-frame affinities and propagating learning signals to multiple query-relevant frames.
Hyun Seok Seong, Woojin Jun, Subeen Lee et al.· 0 citations
This survey covers inference-efficiency mechanisms for visual and audiovisual VideoLLMs that report concrete reductions in parameter count, FLOPs per input, latency, memory, or visual and audio token count, and organize methods by the pipeline stage at which they act.
Killian Steunou, Yannis Tevissen, M. El Yacoubi· 0 citations
Results show that segmentation-region dynamicity can be learned from propagated queries, enabling its online prediction without a dedicated motion-processing pipeline at inference, and shows that segmentation-region dynamicity can be learned from propagate queries.
Calvin Galagain, Martyna Poreba, François Goulette· 0 citations
Streaming video understanding requires Vision Language Models (VLLMs) to process growing video streams and answer user questions under tight latency constraints. Existing methods improve efficiency through token pruning and memory-bank schemes, but mainly reduce visual tokens after visual encoding. Consequently, downst...
Jing-Chi Jiang, Yi-Ran Ling, Ruo-Nan Li et al.· 0 citations
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