no code implementations • 25 Apr 2024 • Zhijie Rao, Jingcai Guo, Xiaocheng Lu, Jingming Liang, Jie Zhang, Haozhao Wang, Kang Wei, Xiaofeng Cao
Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation.
no code implementations • 11 Mar 2024 • Sikai Bai, Jie Zhang, Shuaicheng Li, Song Guo, Jingcai Guo, Jun Hou, Tao Han, Xiaocheng Lu
Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains).
no code implementations • 15 Dec 2023 • Jingcai Guo, Qihua Zhou, Ruibing Li, Xiaocheng Lu, Ziming Liu, Junyang Chen, Xin Xie, Jie Zhang
Then, to facilitate the generalization of local linearities, we construct a maximal margin geometry on the learned features by enforcing low-rank constraints on intra-class samples and high-rank constraints on inter-class samples, resulting in orthogonal subspaces for different classes and each subspace lies on a compact manifold.
no code implementations • 23 Nov 2023 • Zhijie Rao, Jingcai Guo, Xiaocheng Lu, Qihua Zhou, Jie Zhang, Kang Wei, Chenxin Li, Song Guo
In this paper, we propose a simple yet effective Attribute-Aware Representation Rectification framework for GZSL, dubbed $\mathbf{(AR)^{2}}$, to adaptively rectify the feature extractor to learn novel features while keeping original valuable features.
no code implementations • 2 Sep 2023 • Ziming Liu, Jingcai Guo, Xiaocheng Lu, Song Guo, Peiran Dong, Jiewei Zhang
That is, in the process of inferring unseen classes, global features represent the principal direction of the image in the feature space, while local features should maintain uniqueness within a certain range.
no code implementations • 2 May 2023 • Xiaocheng Lu, Ziming Liu, Song Guo, Jingcai Guo, Fushuo Huo, Sikai Bai, Tao Han
Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts composed of known knowledge without training samples.
no code implementations • CVPR 2023 • Ziming Liu, Song Guo, Xiaocheng Lu, Jingcai Guo, Jiewei Zhang, Yue Zeng, Fushuo Huo
Recent studies usually approach multi-label zero-shot learning (MLZSL) with visual-semantic mapping on spatial-class correlation, which can be computationally costly, and worse still, fails to capture fine-grained class-specific semantics.
1 code implementation • CVPR 2023 • Xiaocheng Lu, Ziming Liu, Song Guo, Jingcai Guo
Existing methods either learn the combined state-object representation, challenging the generalization of unseen compositions, or design two classifiers to identify state and object separately from image features, ignoring the intrinsic relationship between them.
no code implementations • 19 Nov 2022 • Fushuo Huo, Wenchao Xu, Song Guo, Jingcai Guo, Haozhao Wang, Ziming Liu, Xiaocheng Lu
Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which induces a tremendously large output space containing all possible state-object compositions.