Domain-Invariant Projection Learning for Zero-Shot Recognition

By An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu, Tao A. Xiang, Ji-rong Wen. 2018

50 citations recorded by the source corpus.

Zero-shot learning (ZSL) aims to recognize unseen object classes without any training samples, which can be regarded as a form of transfer learning from seen classes to unseen ones. This is made possible by learning a projection between a feature space and a semantic space (e.g. attribute space). Key to ZSL is thus to learn a projection function that is robust against the often large domain gap between the seen and unseen classes. In this paper, we propose a novel ZSL model termed domain-invariant projection learning (DIPL). Our model has two novel components: (1) A domain-invariant feature self-reconstruction task is introduced to the seen/unseen class data, resulting in a simple linear formulation that casts ZSL into a min-min optimization problem. Solving the problem is non-trivial, and a novel iterative algorithm is formulated as the solver, with rigorous theoretic algorithm analysis provided. (2) To further align the two domains via the learned projection, shared semantic structure among seen and unseen classes is explored via forming superclasses in the semantic space. Extensive experiments show that our model outperforms the state-of-the-art alternatives by significant margins.

Subfields: Artificial Intelligence, Representation & Self-Supervised Learning

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Citations in the corpus

Learning Robust Global Representations by Penalizing Local Predictive Power (2019, background): “…invariance via adversarial learning (or further extensions) (Ganin et al., 2016; Bousmalis et al., 2017; Tzeng et al., 2017; Xie et al., 2018; Hoffman et al., 2018; Long et al., 2018; Zhao et al., 2018b; Kumar et al., 2018; Li et al., 2018b; Zhao et al., 2018a; Schoenauer-Sebag et al., 2019).”
On Generalizing Beyond Domains in Cross-Domain Continual Learning (2022, background): “The projection matrices are also known to be effective to compute similarity between two entities as proposed in [31, 39].”
Any-Shot Object Detection (2020, unknown): “The ZSR task has been investigated under popular themes such as transduction [35, 42], domain adaptation [16, 42], adversarial learning [40] and class-attribute association [2, 1].”
Transductive Learning for Zero-Shot Object Detection (2019, background): “A domaininvariant projection is learnt in [37] that maps visual features to semantic embeddings and then reconstructs back the same visual feature.”
Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects (2019, background): “Another way of addressing the same problem is to explore a transductive setting where models can take advantage of unlabeled unseen data [45].”
Transductive Zero-Shot Learning for 3D Point Cloud Classification (2019, methodology): “In 2D ZSL, the transductive setting has been shown to be effective [14, 66, 49], however in the case of 3D point cloud data it is a more challenging task.”