1 code implementation • CVPR 2022 • Nirat Saini, Khoi Pham, Abhinav Shrivastava
We use visual decomposed features to hallucinate embeddings that are representative for the seen and novel compositions to better regularize the learning of our model.
no code implementations • CVPR 2021 • Khoi Pham, Kushal Kafle, Zhe Lin, Zhihong Ding, Scott Cohen, Quan Tran, Abhinav Shrivastava
In this paper, we introduce a large-scale in-the-wild visual attribute prediction dataset consisting of over 927K attribute annotations for over 260K object instances.