67 citations · 101 across the 8 of their papers we have counts for
8 papers
Learning Video Context as Interleaved Multimodal Sequences
Kevin Qinghong Lin, Pengchuan Zhang, Difei Gao +6
Narrative videos, such as movies, pose significant challenges in video understanding due to their rich contexts (characters, dialogues, storylines) and diverse demands (identify wh…
The Role of Chain-of-Thought in Complex Vision-Language Reasoning Task
Yifan Wu, Pengchuan Zhang, Wenhan Xiong +3
The study explores the effectiveness of the Chain-of-Thought approach, known for its proficiency in language tasks by breaking them down into sub-tasks and intermediate steps, in i…
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
Jun Chen, Deyao Zhu, Xiaoqian Shen +7
Large language models have shown their remarkable capabilities as a general interface for various language-related applications. Motivated by this, we target to build a unified int…
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding
Mohamed Afham, Satya Narayan Shukla, Omid Poursaeed +3
While most modern video understanding models operate on short-range clips, real-world videos are often several minutes long with semantically consistent segments of variable length…
EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone
Shraman Pramanick, Yale Song, Sayan Nag +5
Video-language pre-training (VLP) has become increasingly important due to its ability to generalize to various vision and language tasks. However, existing egocentric VLP framewor…
UniVTG: Towards Unified Video-Language Temporal Grounding
Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen +5
Video Temporal Grounding (VTG), which aims to ground target clips from videos (such as consecutive intervals or disjoint shots) according to custom language queries (e.g., sentence…