268 citations · 550 across the 35 of their papers we have counts for
5 papers · 1 filter
VoCap: Video Object Captioning and Segmentation from Any Prompt
Jasper Uijlings, Xingyi Zhou, Xiuye Gu +5
Understanding objects in videos in terms of fine-grained localization masks and detailed semantic properties is a fundamental task in video understanding. In this paper, we propose…
OVFact: Measuring and Improving Open-Vocabulary Factuality for Long Caption Models
Monika Wysoczańska, Shyamal Buch, Anurag Arnab +1
Large vision-language models (VLMs) often struggle to generate long and factual captions. However, traditional measures for hallucination and factuality are not well suited for eva…
Temporal Chain of Thought: Long-Video Understanding by Thinking in Frames
Anurag Arnab, Ahmet Iscen, Mathilde Caron +2
Despite recent advances in Vision-Language Models (VLMs), long-video understanding remains a challenging problem. Although state-of-the-art long-context VLMs can process around 100…
Continual Learning in Vision-Language Models via Aligned Model Merging
Ghada Sokar, Gintare Karolina Dziugaite, Anurag Arnab +3
Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors plasticity over the stability needed to re…
What Are You Doing? A Closer Look at Controllable Human Video Generation
Emanuele Bugliarello, Anurag Arnab, Roni Paiss +3
High-quality benchmarks are crucial for driving progress in machine learning research. However, despite the growing interest in video generation, there is no comprehensive dataset…