CLIP-ProbCR:CLIP-based Probability embedding Combination Retrieval

Author:

Li Mingyong1ORCID,Zhao Zongwei1ORCID,Jiang Xiaolong1ORCID,Jiang Zheng1ORCID

Affiliation:

1. College of Computer and Information Science, Chongqing Normal University, Chongqing, China

Publisher

ACM

Reference42 articles.

1. Neculai Andrei, Yanbei Chen, and Zeynep Akata. 2022. Probabilistic Compositional Embeddings for Multimodal Image Retrieval. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops,CVPR Workshops 2022, New Orleans, LA, USA, June 19--20, 2022. 4546--4556.

2. Conditioned and composed image retrieval combining and partially fine-tuning CLIP-based features

3. Hé di Ben-Younes, Ré mi Cadè ne, Nicolas Thome, and Matthieu Cord. 2019. BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI2019. AAAI Press, 8102--8109.

4. Gaurab Bhattacharya, Nikhil Kilari, Jayavardhana Gubbi, Bagya Lakshmi V, Arpan Pal, and Balamuralidhar P. 2022. DAtRNet: Disentangling Fashion Attribute Embedding for Substitute Item Retrieval. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2022, New Orleans, LA, USA, June 19--20, 2022. IEEE, 2282--2286.

5. Products and convolutions of Gaussian probability density functions;Bromiley Paul;Tina-Vision Memo,2003

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