Sink-Token-aware Pruning (SToP) suppresses semantically uninformative sink tokens during visual token pruning in Video LLMs, boosting fine-grained performance even at 90% pruning rates across hallucination, reasoning, and MCQA benchmarks.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5representative citing papers
OmniGUI is the first step-level benchmark supplying interleaved image, audio, and video inputs across 709 expert episodes in 29 smartphone apps to evaluate multimodal GUI agents.
Motion-o extends VLMs with Motion Chain of Thought (MCoT) using <motion/> tags and perturbation rewards to make object trajectories explicit and supervised in video reasoning.
The MOSS module learns and combines multi-order space-time self-similarity features to enhance temporal dynamics modeling in videos across action recognition, VQA, and robotic tasks.
OmniJigsaw is a self-supervised proxy task that reconstructs shuffled audio-visual clips via joint integration, sample-level selection, and clip-level masking strategies, yielding gains on 15 video, audio, and reasoning benchmarks.
citing papers explorer
-
Motion-o: Trajectory-Grounded Video Reasoning
Motion-o extends VLMs with Motion Chain of Thought (MCoT) using <motion/> tags and perturbation rewards to make object trajectories explicit and supervised in video reasoning.
-
Exploring High-Order Self-Similarity for Video Understanding
The MOSS module learns and combines multi-order space-time self-similarity features to enhance temporal dynamics modeling in videos across action recognition, VQA, and robotic tasks.
-
OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering
OmniJigsaw is a self-supervised proxy task that reconstructs shuffled audio-visual clips via joint integration, sample-level selection, and clip-level masking strategies, yielding gains on 15 video, audio, and reasoning benchmarks.