activity
20202026
most citedOmniVL:One Foundation Model for Image-Language and Video-Language Tasks

69 citations · 129 across the 27 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.CV2025

DeRA: Decoupled Representation Alignment for Video Tokenization

Pengbo Guo, Junke Wang, Zhen Xing +4

This paper presents DeRA, a novel 1D video tokenizer that decouples the spatial-temporal representation learning in video tokenization to achieve better training efficiency and per…

cs.CV2025

TempoMaster: Efficient Long Video Generation via Next-Frame-Rate Prediction

Yukuo Ma, Cong Liu, Junke Wang +5

We present TempoMaster, a novel framework that formulates long video generation as next-frame-rate prediction. Specifically, we first generate a low-frame-rate clip that serves as…

cs.CV2025

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis

Peng Zheng, Junke Wang, Yi Chang +3

Recent advances in large language models (LLMs) have spurred interests in encoding images as discrete tokens and leveraging autoregressive (AR) frameworks for visual generation. Ho…

cs.CV2025

Perception Encoder: The best visual embeddings are not at the output of the network

Daniel Bolya, Po-Yao Huang, Peize Sun +15

We introduce Perception Encoder (PE), a state-of-the-art vision encoder for image and video understanding trained via simple vision-language learning. Traditionally, vision encoder…

cs.CV2025

SimpleAR: Pushing the Frontier of Autoregressive Visual Generation through Pretraining, SFT, and RL

Junke Wang, Zhi Tian, Xun Wang +4

This work presents SimpleAR, a vanilla autoregressive visual generation framework without complex architecure modifications. Through careful exploration of training and inference o…

cs.CV2025

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…