Bo Zhao
副教授
Works on embodied intelligence, multimodal large models and data-centric AI.
Bo Zhao's lab at SJTU works on multimodal & grounded language, vision-language & multimodal and large language models. 35 papers since 2023, 25 at top venues, mostly NeurIPS and ACL. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 71%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 12 papers2026 · T1 10 · collecting
- 13 papers2025 · T1 9
- 8 papers2024 · T1 6
- 2 papers2023 · T1 0
T1 venuesOtherVenues: NeurIPS, ACL, CVPR, ECCV, ICCV
Research focuspaper titles that use each topic's words
- Vision-language & multimodal10 papers
- Multimodal & grounded language9 papers
- Large language models8 papers
- data-centric0 papers
- multimodal large0 papers
Paper list
From the lab homepage.
202612 papers
- TIP (T1)Active Dataset Distillation via Dual-Space Informative Matching (opens in a new tab)
- NeurIPS (T1)CondenseVLA: Learnable History Condensation for Efficient Multi-Frame VLA
- NeurIPS E&D Track OralDD-Ranking: Rethinking the Evaluation of Dataset Distillation
- Nature (T1)Emu3: Multimodal learning with next-token prediction for large multimodal models (opens in a new tab)
- CVPR Efficient CVPR BadgeEvo-1: Lightweight Vision-Language-Action Model with Preserved Semantic Alignment (opens in a new tab)
- NeurIPS (T1)Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model
- NeurIPS (T1)Focusable Monocular Depth Estimation
- AAAI (T1)Mask2IV: Interaction-Centric Video Generation via Mask Trajectories (opens in a new tab)
- NeurIPS (T1)Probing Visual Planning in Image Editing Models
- ECCV (T1)Training-free Discriminative Patch Mining for Robust Few-Shot Recognition with CLIP (opens in a new tab)
- TPAMI (T1)Understanding Data Influence with Differential Approximation (opens in a new tab)
- ACL (T1)v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound (opens in a new tab)
202513 papers
- ICML (T1)BOOD: Boundary-based Out-Of-Distribution Data Generation
- IJCV (T1)Image Captions are Natural Prompts for Training Data Synthesis
- ACL OralMegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval
- CVPR (T1)MLVU: Benchmarking Multi-task Long Video Understanding (opens in a new tab)
- ICCV (T1)MMCR: Benchmarking Cross-Source Reasoning in Scientific Papers (opens in a new tab)
- NeurIPS D&B TrackMomentSeeker: A Task-Oriented Benchmark For Long-Video Moment Retrieval (opens in a new tab)
- ICRA (T1)NaVid-4D: Unleashing Spatial Intelligence in Egocentric RGB-D Videos for Vision-and-Language Navigation (opens in a new tab)
- CVPR (T1)Seeing Clearly, Answering Incorrectly: A Multimodal Robustness Benchmark For Evaluating MLLMs On Leading Questions (opens in a new tab)
- ICRA (T1)SpatialBot: Precise Spatial Understanding with Vision Language Models (opens in a new tab)
- ICCV (T1)STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding? (opens in a new tab)
- CVPR OralTowards Universal Dataset Distillation via Task-Driven Diffusion
- NeurIPS (T1)UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptation
20248 papers
- NeurIPS (T1)Fetch and Forge: Efficient Dataset Condensation for Object Detection (opens in a new tab)
- TIP (T1)Normalizing Batch Normalization for Long-Tailed Recognition (opens in a new tab)
- ECCV (T1)Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking (opens in a new tab)
- RSS (T1)RAG-Driver Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model (opens in a new tab)
- ICLR (T1)Real-Fake: Effective Training Data Synthesis Through Distribution Matching (opens in a new tab)
- NeurIPS SpotlightSegVol: Universal and Interactive Volumetric Medical Image Segmentation (opens in a new tab)
- NeurIPS D&B TrackTouchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? (opens in a new tab)
- ACL (T1)VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to SJTUFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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