通用多模态大模型：
1. 研究类R-1 强推理多模态大模型，包括Thinking with Image 推理范式，强化学习对齐算法，特定场景下的推理能力评估等。

2. 研究复杂多模态Agent智能体，包括面向GUI的多模态Agent  构建， 面向风光储氢新能源控制的智能体设计优化等。
新能源多模态大模型：

1. 面向复杂设备运维的智慧运维大模型
           2. 综合能源智慧管控大模型

News： 

(2026.04)     One paper is accepted by ICML 2026 ! 

(2026.03)     One paper is accepted by CVPR 2026 ! 

(2026.03)     One paper is accepted by ICLR 2026 ! 

(2025.09)     One paper is accepted by NeurlPS 2025 ! 

已发表一作/通讯论文：

[1] Heng, Y,Jiang, C.*^(Equal Contribution, Corresponding Author) , Zhang, Shi, Ye, W.et al. (2026).  From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models. *Forty-Third International Conference on Machine Learning  (ICML 2026 )*. (CCF-A)

[2] Jiang, C.*, Heng, Y*(Equal Contribution), Ye, W., Xu, H., Yan, M., et al. (2025).  VLM-R3 : Region Recognition, Reasoning, and Refinement for Enhanced Multimodal Chain-of-Thought. *Proceedings of the Conference on Neural Information Processing Systems (NeurlPS 2025)*. (CCF-A)

[3] Jiang, C.*, Hongruijia*(Equal Contribution), Ye, W., Xu, H., Yan, M., et al. (2024).   MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model.  *Proceedings of the Conference on Neural Information Processing Systems (NeurlPS 2024)*. (CCF-A)

[4] Hongruijia*, Jiang, C.*(Equal Contribution), Ye, W., et al. (2025). SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025)*. (CCF-A)   

[5] Jiang, C., Ye, W., Dong, M., Jia, H., et al. (2024). Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models. *Proceedings of the ACM International Conference on Multimedia (MM 2024)*. (CCF-A)  

[6] Jiang, C., Xu, H., Dong, M., Chen, J., Ye, W., et al. (2024). Hallucination Augmented Contrastive Learning for Multimodal Large Language Model. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)*. (CCF-A)

[7] Jiang, C., Ye, W., Xu, H., Ye, Q., Yan, M., Zhang, J., & Zhang, S. (2024). TiMix: Text-aware Image Mixing for Effective Vision-Language Pretraining. *Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2024)*. (CCF-A)

[8] Jiang, C., Xu, H., Ye, W., Ye, Q., et al. (2023). BUS: Efficient and Effective Vision-language Pre-training with Bottom-Up Patch Summarization. *Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023)*. (CCF-A)

[9] Jiang, C., Xu, H., Ye, W., Ye, Q., et al. (2023). COPA: Efficient Vision-Language Pre-training through Collaborative Object- and Patch-Text Alignment. *Proceedings of the ACM International Conference on Multimedia (MM 2023)*. (CCF-A)

[10] Jiang, C., Ye, W., Xu, H., Yan, M., et al. (2023). Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation. *Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023)*. (CCF-A)

[11] Jiang, C., Xie, R., Ye, W., Sun, J., & Zhang, S. (2023). Exploiting Pseudo Image Captions for Multimodal Summarization. *Finds of the Association for Computational Linguistics: ACL 2023*.  

[12] Jiang, C., Xu, H., Li, C., Yan, M., et al. (2022). TRIPS: Efficient Vision-and-Language Pre-training with Text-relevant Patch Selection. *Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)*. (CCF-B)

[13] Jiang, C., Yang, D., & Chen, X. (2020). Similarity Learning For Cover Song Identification Using Cross-Similarity Matrices of Multi-Level Deep Sequences. *Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020)*. (CCF-B)

[14] Jiang, C., Yang, D., & Chen, X. (2020). Learn A Robust Representation For Cover Song Identification Via Aggregating Local And Global Music Temporal Context. *Proceedings of the IEEE International Conference on Multimedia and Expo (ICME 2020)*. (CCF-B)