Email: jiangyi0425-at-gmail.com jiangyi.enjoy-at-bytedance.com

Biography

Currently, I am a Research Lead at ByteDance Ads GenAI, where I work on Generative Foundation models.

I got my Master's degree from the Department of Computer Science and Engineering, Zhejiang University.

My previous research focus is on large scale open world visual understanding and pretraining in images & videos.

My work of Visual AutoRegressive modeling won the Best Paper Award of NeurIPS 2024.

Research Interests

Visual Foundation Models, Generative Pretrain Models and Large Language Models.

Unified visual generation and understanding the complex world for computer vision.

Large-scale multi-modality generative Pretrain and Alignment.

Open world/vocabulary Visual Recognition : Equip vision with knowledge and semantics.

Highlights

  • Visual AutoRegressive modeling: a new visual generation Framework elevates Autoregressive models beyond diffusion, indicate scaling law in image generation.

  • GLEE is accepted by CVPR 2024 as Highlight, An object-level foundation model for locating and identifying objects in images and videos.

  • UNINEXT unifies 10 instance perception tasks using a single model with the same model parameters

  • ByteTrack ranks 1th of the most influential papers in ECCV 2022. Code is available on github with 4.3k stars

  • Sparse R-CNN accepted by CVPR'21. Sparse R-CNN is integrated into several famous frameworks(Detectron2, MMDetection, PaddlePaddle)

I'm looking for self-motivated interns and full-time engineer/researcher for visual generative foundation models. Feel free to reach out if you are interested.

Publications [Google Scholar]

(* Equal contribution, Corresponding Author or Project Lead)

Conference and Preprint

Honors and Awards

Competitions

  • Winner of CVPR 2022 Large-scale Video Object Segmentation Challenge: Video Instance Segmentation

  • Runner up of CVPR 2021 FGVC8 iNaturalist Challenge

  • Runner up of ICCV 2019 WIDER Face and Person Challenge: Face Detection

  • Competition Master in kaggle 2018

Professional Activities