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Large Language Model Teaches Visual Students: Cross-Modality Transfer of Fine-Grained Conceptual Knowledge
Thomas Shih-Chao Liang, Zhuoran Yu, Yong Jae Lee
Large Language Models (LLMs) possess broad conceptual knowledge acquired through large-scale text pretraining, yet their potential to supervise models in other modalities remains u…
Unified Spatio-Temporal Token Scoring for Efficient Video VLMs
Jianrui Zhang, Yue Yang, Rohun Tripathi +5
Token pruning is essential for enhancing the computational efficiency of vision-language models (VLMs), particularly for video-based tasks where temporal redundancy is prevalent. P…
See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models
Le Thien Phuc Nguyen, Zhuoran Yu, Samuel Low Yu Hang +8
Multimodal large language models (MLLMs) are expected to jointly interpret vision, audio, and language, yet existing video benchmarks rarely assess fine-grained reasoning about hum…
How Multimodal LLMs Solve Image Tasks: A Lens on Visual Grounding, Task Reasoning, and Answer Decoding
Zhuoran Yu, Yong Jae Lee
Multimodal Large Language Models (MLLMs) have demonstrated strong performance across a wide range of vision-language tasks, yet their internal processing dynamics remain underexplo…
CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systems
Aniket Rege, Zinnia Nie, Mahesh Ramesh +5
Popular text-to-image (T2I) systems are trained on web-scraped data, which is heavily Amero and Euro-centric, underrepresenting the cultures of the Global South. To analyze these b…
Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness
Le Thien Phuc Nguyen, Zhuoran Yu, Khoa Quang Nhat Cao +8
We present UniTalk, a novel dataset emphasizing challenging scenarios to enhance model generalization for the task of active speaker detection (ASD). Previously established benchma…