19 citations · 20 across the 11 of their papers we have counts for
11 papers
VideoTG-R1: Boosting Video Temporal Grounding via Curriculum Reinforcement Learning on Reflected Boundary Annotations
Lu Dong, Haiyu Zhang, Han Lin +8
Video temporal grounding (VTG) aims to locate precise segments in videos based on language queries, which is a fundamental challenge in video understanding. While recent Multimodal…
Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining
Lu Dong, Haiyu Zhang, Hongjie Zhang +5
The task of weakly supervised temporal sentence grounding (WSTSG) aims to detect temporal intervals corresponding to a language description from untrimmed videos with only video-le…
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Guo Chen, Zhiqi Li, Shihao Wang +16
We introduce Eagle 2.5, a family of frontier vision-language models (VLMs) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and h…
An Egocentric Vision-Language Model based Portable Real-time Smart Assistant
Yifei Huang, Jilan Xu, Baoqi Pei +16
We present Vinci, a vision-language system designed to provide real-time, comprehensive AI assistance on portable devices. At its core, Vinci leverages EgoVideo-VL, a novel model t…
Modeling Fine-Grained Hand-Object Dynamics for Egocentric Video Representation Learning
Baoqi Pei, Yifei Huang, Jilan Xu +8
In egocentric video understanding, the motion of hands and objects as well as their interactions play a significant role by nature. However, existing egocentric video representatio…
Vinci: A Real-time Embodied Smart Assistant based on Egocentric Vision-Language Model
Yifei Huang, Jilan Xu, Baoqi Pei +15
We introduce Vinci, a real-time embodied smart assistant built upon an egocentric vision-language model. Designed for deployment on portable devices such as smartphones and wearabl…
CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding
Guo Chen, Yicheng Liu, Yifei Huang +6
Most existing video understanding benchmarks for multimodal large language models (MLLMs) focus only on short videos. The limited number of benchmarks for long video understanding…
EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation
Baoqi Pei, Guo Chen, Jilan Xu +8
In this report, we present our solutions to the EgoVis Challenges in CVPR 2024, including five tracks in the Ego4D challenge and three tracks in the EPIC-Kitchens challenge. Buildi…
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
Guo Chen, Yifei Huang, Jilan Xu +7
Understanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and…
EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World
Yifei Huang, Guo Chen, Jilan Xu +8
Being able to map the activities of others into one's own point of view is one fundamental human skill even from a very early age. Taking a step toward understanding this human abi…
LvBench: A Benchmark for Long-form Video Understanding with Versatile Multi-modal Question Answering
Hongjie Zhang, Lu Dong, Yi Liu +4
Despite remarkable recent progress, existing long-form VideoQA datasets fall short of meeting the criteria for genuine long-form video understanding. This is primarily due to the u…