Jifeng Dai
Associate Professor
Works on visual representation learning, deformable attention, agentic AI, and continual learning.
Jifeng Dai's lab at Tsinghua works on generative & foundation models and physics-based simulation & animation. 24 papers since 2023, 24 at top venues, mostly CVPR and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 100%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 7 papers2025 · T1 7
- 10 papers2024 · T1 10
- 7 papers2023 · T1 7
T1 venuesOtherVenues: CVPR, NeurIPS, TPAMI, ECCV, ICLR
Research focuspaper titles that use each topic's words
- Generative & foundation models6 papers
- deformable2 papers
- visual representation1 paper
- attention1 paper
- Physics-based simulation & animation0 papers
Paper list
From the lab homepage.
20257 papers
- TPAMI (T1)BEVFormer: Learning Bird's-Eye-View Representation From LiDAR-Camera Via Spatiotemporal Transformers (opens in a new tab)
- TPAMI (T1)Demystify Transformers & Convolutions in Modern Image Deep Networks (opens in a new tab)
- CVPR (T1)HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding (opens in a new tab)
- ICLR (T1)OmniCorpus: An Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text (opens in a new tab)
- TPAMI (T1)Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding (opens in a new tab)
- CVPR (T1)PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models (opens in a new tab)
- CVPR (T1)SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding (opens in a new tab)
202410 papers
- CVPR (T1)Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft (opens in a new tab)
- ECCV (T1)ControlLLM: Augment Language Models with Tools by Searching on Graphs (opens in a new tab)
- CVPR (T1)Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications (opens in a new tab)
- CVPR (T1)InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks (opens in a new tab)
- NeurIPS (T1)Learning 1D Causal Visual Representation with De-focus Attention Networks (opens in a new tab)
- NeurIPS (T1)Parameter-Inverted Image Pyramid Networks (opens in a new tab)
- ECCV (T1)The All-Seeing Project V2: Towards General Relation Comprehension of the Open World (opens in a new tab)
- ICLR (T1)The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World (opens in a new tab)
- NeurIPS (T1)Vision Model Pre-training on Interleaved Image-Text Data via Latent Compression Learning (opens in a new tab)
- NeurIPS (T1)VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks (opens in a new tab)
20237 papers
- CVPR (T1)BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision (opens in a new tab)
- CVPR (T1)InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions (opens in a new tab)
- CVPR (T1)Planning-oriented Autonomous Driving (opens in a new tab)
- CVPR (T1)Siamese Image Modeling for Self-Supervised Vision Representation Learning (opens in a new tab)
- CVPR (T1)Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information (opens in a new tab)
- CVPR (T1)Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks (opens in a new tab)
- NeurIPS (T1)VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to TsinghuaFall 2027
Computer Science PhD (international applicants)
- Round
- Main
- Opens
- GRE
- See program page
- English
- Per school catalog
- Fee
- RMB 800
Not yet confirmed for this intake.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- The lab site and the paper record do not agree.
- Collected
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- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
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