Growing Visual Generative Capacity for Pre-Trained MLLMs

Hanyu Wang, Jiaming Han, Ziyan Yang, Qi Zhao, Shanchuan Lin, Xiangyu Yue, Abhinav Shrivastava, Zhenheng Yang, Hao Chen^†

Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations

Jiamin Han, Hao Chen^†, Yang Zhao, Hanyu Wang, Qi Zhao, Zhiyan Yang, Hao He, Xiangyu Yue, Lu Jiang

Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model

ByteDance Seed VideoGen Team

LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior

Hanyu Wang, Saurabh Suri, Yixuan Ren, Hao Chen^†, Abhinav Shrivastava

NeRV-Diffusion: Diffuse Implicit Neural Representations for Video Synthesis

Yixuan Ren, Hanyu Wang, Hao Chen^†, Bo He, Abhinav Shrivastava

Seedance 1.0: Exploring the Boundaries of Video Generation Models

ByteDance Seed VideoGen Team

SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training

Jianyi Wang, Shanchuan Lin, Zhijie Lin, Yuxi Ren, Meng Wei, Zongsheng Yue, Shangchen Zhou, Hao Chen, Yang Zhao, Ceyuan Yang, Xuefeng Xiao, Chen Change Loy, Lu Jiang

SkipSR: Faster Super-Resolution with Token Skipping

Rohan Choudhury, Shanchuan Lin, Jianyi Wang, Hao Chen, Qi Zhao, Feng Cheng, Lu Jiang, Kris Kitani, Laszlo A. Jeni

Fast Encoding and Decoding for Implicit Video Representation

Hao Chen, Saining Xie, Ser-Nam Lim, Abhinav Shrivastava

We propose NeRV-Enc, which encodes videos 10⁴ times faster than its predecessor NeRV, utilizing hyper-networks. Additionally, we introduce NeRV-Dec, which decodes video 8.9 times faster than NeRV via parallel decoding, and is 11 times faster compared to the H.264 codec.

HNeRV: A Hybrid Neural Representation for Videos

Hao Chen, Matt Gwilliam, Ser-Nam Lim, Abhinav Shrivastava

We propose a hybrid video neural representation and a evenly distributed neural network to improve modeling capacity and introduce internal generalization.

Towards Scalable Neural Representation for Diverse Videos

Bo He, Xitong Yang, Hanyu Wang, Zuxuan Wu, Hao Chen, Shuaiyi Huang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava

We propose D-NeRV, a novel neural representation framework designed to encode large-scale and diverse videos.

CNeRV: Content-adaptive Neural Representation for Visual Data

Hao Chen, Matt Gwilliam, Bo He, Ser-Nam Lim, Abhinav Shrivastava

We propose a hybrid video neural representation with content-adaptive embedding to introduce internal generalization.

NeRV: Neural Representations for Videos

Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava

We propose an image-wise neural representation for videos, which achieves good compression results and fast decoding speed.

The Lottery Ticket Hypothesis for Object Recognition

Sharath Girish, Shishira R. Maiya, Kamal Gupta, Hao Chen, Larry Davis, Abhinav Shrivastava

Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNet

Hao Chen, Abhinav Shrivastava

HR-RCNN: Hierarchical Relational Reasoning for Object Detection

Hao Chen, Abhinav Shrivastava

Progressive Object Transfer Detection

Hao Chen, Yali Wang, Guoyou Wang, Xiang Bai, Yu Qiao

LSTD: A Low-Shot Transfer Detector for Object Detection

Hao Chen, Yali Wang, Guoyou Wang, Yu Qiao