9 papers
VideoWeave: A Data-Centric Approach for Efficient Video Understanding
Zane Durante, Silky Singh, Arpandeep Khatua +6
Training video-language models is often prohibitively expensive due to the high cost of processing long frame sequences and the limited availability of annotated long videos. We pr…
Temporal Grounding as a Learning Signal for Referring Video Object Segmentation
Seunghun Lee, Jiwan Seo, Jeonghoon Kim +9
Referring Video Object Segmentation (RVOS) aims to segment and track objects in videos based on natural language expressions, requiring precise alignment between visual content and…
T*: Re-thinking Temporal Search for Long-Form Video Understanding
Jinhui Ye, Zihan Wang, Haosen Sun +9
Efficiently understanding long-form videos remains a significant challenge in computer vision. In this work, we revisit temporal search paradigms for long-form video understanding…
Latest Object Memory Management for Temporally Consistent Video Instance Segmentation
Seunghun Lee, Jiwan Seo, Minwoo Choi +6
In this paper, we present Latest Object Memory Management (LOMM) for temporally consistent video instance segmentation that significantly improves long-term instance tracking. At t…
Towards Fine-Grained Video Question Answering
Wei Dai, Alan Luo, Zane Durante +5
In the rapidly evolving domain of video understanding, Video Question Answering (VideoQA) remains a focal point. However, existing datasets exhibit gaps in temporal and spatial gra…
Failures to Find Transferable Image Jailbreaks Between Vision-Language Models
Rylan Schaeffer, Dan Valentine, Luke Bailey +12
The integration of new modalities into frontier AI systems offers exciting capabilities, but also increases the possibility such systems can be adversarially manipulated in undesir…