4 citations · 4 across the 6 of their papers we have counts for
6 papers
IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
Yitong Chen, Zijie Diao, Junke Wang +5
Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing semantically rich latent spac…
FluxMem: Adaptive Hierarchical Memory for Streaming Video Understanding
Yiweng Xie, Bo He, Junke Wang +3
This paper presents FluxMem, a training-free framework for efficient streaming video understanding. FluxMem adaptively compresses redundant visual memory through a hierarchical, tw…
VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding
Jiapeng Shi, Junke Wang, Zuyao You +2
This paper presents VideoLoom, a unified Video Large Language Model (Video LLM) for joint spatial-temporal understanding. To facilitate the development of fine-grained spatial and…
Pix2Cap-COCO: Advancing Visual Comprehension via Pixel-Level Captioning
Zuyao You, Junke Wang, Lingyu Kong +2
We present Pix2Cap-COCO, the first panoptic pixel-level caption dataset designed to advance fine-grained visual understanding. To achieve this, we carefully design an automated ann…
OmniVid: A Generative Framework for Universal Video Understanding
Junke Wang, Dongdong Chen, Chong Luo +4
The core of video understanding tasks, such as recognition, captioning, and tracking, is to automatically detect objects or actions in a video and analyze their temporal evolution.…
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Junke Wang, Lingchen Meng, Zejia Weng +3
Existing visual instruction tuning methods typically prompt large language models with textual descriptions to generate instruction-following data. Despite the promising performanc…