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cs.CV2026
ANCHOR: Audio-Visually Grounded Chain-of-Thought Reasoning Benchmark
Joel Julin, Souraja Kundu, Liza Dahiya +6
Multimodal large language models (MLLMs) perform well on audio-visual question answering, but correct answers do not necessarily imply that their stated reasoning is grounded in re…
cs.CV2026
Learning Compact Video Representations for Efficient Long-form Video Understanding in Large Multimodal Models
Yuxiao Chen, Jue Wang, Zhikang Zhang +8
With recent advancements in video backbone architectures, combined with the remarkable achievements of large language models (LLMs), the analysis of long-form videos spanning tens…
cs.CV2023
Augment the Pairs: Semantics-Preserving Image-Caption Pair Augmentation for Grounding-Based Vision and Language Models
Jingru Yi, Burak Uzkent, Oana Ignat +4
Grounding-based vision and language models have been successfully applied to low-level vision tasks, aiming to precisely locate objects referred in captions. The effectiveness of g…