1 citations · 1 across the 7 of their papers we have counts for
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Enrich and Detect: Video Temporal Grounding with Multimodal LLMs
Shraman Pramanick, Effrosyni Mavroudi, Yale Song +3
We introduce ED-VTG, a method for fine-grained video temporal grounding utilizing multi-modal large language models. Our approach harnesses the capabilities of multimodal LLMs to j…
The 9th AI City Challenge
Zheng Tang, Shuo Wang, David C. Anastasiu +25
The ninth AI City Challenge continues to advance real-world applications of computer vision and AI in transportation, industrial automation, and public safety. The 2025 edition fea…
Improving Coverage in Combined Prediction Sets with Weighted p-values
Gina Wong, Drew Prinster, Suchi Saria +2
Conformal prediction quantifies the uncertainty of machine learning models by augmenting point predictions with valid prediction sets. For complex scenarios involving multiple tria…
ViT-Linearizer: Distilling Quadratic Knowledge into Linear-Time Vision Models
Guoyizhe Wei, Rama Chellappa
Vision Transformers (ViTs) have delivered remarkable progress through global self-attention, yet their quadratic complexity can become prohibitive for high-resolution inputs. In th…