Jiamian Wang personal website

About Me

Greetings! I’m Jiamian Wang (王加冕), a student at Golisano College of Computing and Information Sciences, Rochester Institute of Technology, under the guidance of Dr. Zhiqiang Tao. Prior to joining RIT, I spent one year in the Department of Computer Science and Engineering at Santa Clara University (San Jose, USA) with Dr.Tao. I received my M.S. degree from University of Southern California (Los Angeles, USA) in 2020 and B.E. degree from Tianjin University (Tianjin, China) in 2018.

My research primarily revolves around low-level vision tasks, such as image super-resolution. I am engaged in enhancing the trustworthiness, robustness, and performance of snapshot compressive imaging systems (SCI). My recent work delves into harnessing the power of diffusion models for both low-level vision tasks and high-level multi-modal retrieval task.

News

2024.03: I will join Bosch Research and Technology Center as a research intern, focusing on autoregressive image generation, starting from May 2024.

2024.02: [CVPR’24] One paper on multi-modality text-video retrieval is accepted by CVPR 2024 as Highlight (11.9%). Check out the manuscript and the code.

2024.02: I will serve as a reviewer for ECCV 2024.

2023.11: I will serve as a reviewer for CVPR 2024.

2023.08: [ICCV’23] Code, including training, testing scripts, and pretrained models have been released. Check out Iterative-Soft-Shrinkage-SR for more details.

2023.07: [ICCV’23] One paper on efficient image super-resolution (Arxiv) is accepted by ICCV 2023. Looking forward to sharing our work in Paris.

2023.06: [Preprint] Check out our Federated learning method on snapshot compressive imaging, Federated Hardware-Prompt Learning (FedHP). This is the first attempt of discussing the power of FL in the field of SCI.

2023.03: [Preprint] Check out our new pruning method that flexibly handles diverse off-the-shelf SR network architectures without pre-training: Arxiv. Thanks to my co-authors’ great support.

2022.06. [ECCV’22] One paper on uncertainty quantification on SCI system is accpeted as an Oral paper by ECCV 2022 (2.7%). Check out the manuscript and the code.

Selected Publications and Preprints

	Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval. Jiamian Wang, Guohao Sun, Pichao Wang, Dongfang Liu, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Zhiqiang Tao. CVPR Highlight (11.9%), 2024. Arxiv Code
	SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant. Guohao Sun, Can Qin, Jiamian Wang, Zeyuan Chen, Ran Xu, Zhiqiang Tao. Arxiv, 2024. Arxiv Code
	Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging. Jiamian Wang, Zongliang Wu, Yulun Zhang, Xin Yuan, Tao Lin, Zhiqiang Tao. Arxiv, 2023. Arxiv
	Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution. Jiamian Wang, Huan Wang, Yulun Zhang, Yun Fu, Zhiqiang Tao. ICCV Poster (26.15%), 2023. Paper Code
	Modeling Mask Uncertainty in Hyperspectral Image Reconstruction. Jiamian Wang, Yulun Zhang, Xin Yuan, Ziyi Meng, Zhiqiang Tao. ECCV Oral (2.7%), 2022. Paper Code
	Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty. Xueying Yang, Jiamian Wang, Sheng Li, Zhiqiang Tao. CIKM, 2022. Paper Code
	S2-Transformer for Mask-Aware Hyperspectral Image Reconstruction. Jiamian Wang, Kunpeng Li, Yulun Zhang, Xin Yuan, Zhiqiang Tao. Arxiv, 2022. Arxiv Code
	A new backbone for hyperspectral image reconstruction. Jiamian Wang, Yulun Zhang, Xin Yuan, Yun Fu, Zhiqiang Tao. Arxiv, 2022. Arxiv Code