Transductive Zero-Shot Learning by Decoupled Feature Generation

• URL: http://arxiv.org/abs/2102.03266v1
• Date: Fri, 5 Feb 2021 16:17:52 GMT
• Title: Transductive Zero-Shot Learning by Decoupled Feature Generation
• Authors: Federico Marmoreo, Jacopo Cavazza, Vittorio Murino
• Abstract summary: We focus on the transductive setting, in which unlabelled visual data from unseen classes is available. We propose to decouple tasks of generating realistic visual features and translating semantic attributes into visual cues. We present a detailed ablation study to dissect the effect of our proposed decoupling approach, while demonstrating its superiority over the related state-of-the-art.
• Score: 30.664199050468472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address zero-shot learning (ZSL), the problem of recognizing categories for which no labeled visual data are available during training. We focus on the transductive setting, in which unlabelled visual data from unseen classes is available. State-of-the-art paradigms in ZSL typically exploit generative adversarial networks to synthesize visual features from semantic attributes. We posit that the main limitation of these approaches is to adopt a single model to face two problems: 1) generating realistic visual features, and 2) translating semantic attributes into visual cues. Differently, we propose to decouple such tasks, solving them separately. In particular, we train an unconditional generator to solely capture the complexity of the distribution of visual data and we subsequently pair it with a conditional generator devoted to enrich the prior knowledge of the data distribution with the semantic content of the class embeddings. We present a detailed ablation study to dissect the effect of our proposed decoupling approach, while demonstrating its superiority over the related state-of-the-art.

Related papers

• CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning [48.46511584490582]
  Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories. Real-world challenges such as distribution imbalances and attribute co-occurrence hinder the discernment of local variances in images. We propose a bidirectional cross-modal ZSL approach CREST to overcome these challenges.
  arXiv  Detail & Related papers  (2024-04-15T10:19:39Z)
• Hierarchical Visual Primitive Experts for Compositional Zero-Shot Learning [52.506434446439776]
  Compositional zero-shot learning (CZSL) aims to recognize compositions with prior knowledge of known primitives (attribute and object) We propose a simple and scalable framework called Composition Transformer (CoT) to address these issues. Our method achieves SoTA performance on several benchmarks, including MIT-States, C-GQA, and VAW-CZSL.
  arXiv  Detail & Related papers  (2023-08-08T03:24:21Z)
• Exploiting Semantic Attributes for Transductive Zero-Shot Learning [97.61371730534258]
  Zero-shot learning aims to recognize unseen classes by generalizing the relation between visual features and semantic attributes learned from the seen classes. We present a novel transductive ZSL method that produces semantic attributes of the unseen data and imposes them on the generative process. Experiments on five standard benchmarks show that our method yields state-of-the-art results for zero-shot learning.
  arXiv  Detail & Related papers  (2023-03-17T09:09:48Z)
• Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning [125.99533416395765]
  We argue that the performance of the final classifier depends on the data separation present in the latent space and visual separation present in the projection. We demonstrate our results by the classification of five real-world challenging image datasets of human intestinal parasites with only 1% supervised samples.
  arXiv  Detail & Related papers  (2023-02-06T10:01:38Z)
• Cross-modal Representation Learning for Zero-shot Action Recognition [67.57406812235767]
  We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR) Our model employs a conceptually new pipeline by which visual representations are learned in conjunction with visual-semantic associations in an end-to-end manner. Experiment results show our model considerably improves upon the state of the arts in ZSAR, reaching encouraging top-1 accuracy on UCF101, HMDB51, and ActivityNet benchmark datasets.
  arXiv  Detail & Related papers  (2022-05-03T17:39:27Z)
• Semantic Disentangling Generalized Zero-Shot Learning [50.259058462272435]
  Generalized Zero-Shot Learning (GZSL) aims to recognize images from both seen and unseen categories. In this paper, we propose a novel feature disentangling approach based on an encoder-decoder architecture. The proposed model aims to distill quality semantic-consistent representations that capture intrinsic features of seen images.
  arXiv  Detail & Related papers  (2021-01-20T05:46:21Z)
• Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning [21.89909688056478]
  We propose a new two-level joint idea to augment the generative network with an inference network during training. This provides strong cross-modal interaction for effective transfer of knowledge between visual and semantic domains. We evaluate our approach on four benchmark datasets against several state-of-the-art methods, and show its performance.
  arXiv  Detail & Related papers  (2020-07-15T15:34:09Z)
• Generative Model-driven Structure Aligning Discriminative Embeddings for Transductive Zero-shot Learning [21.181715602603436]
  We propose a neural network-based model for learning a projection function which aligns the visual and semantic data in the latent space. We show superior performance on standard benchmark datasets AWA1, AWA2, CUB, SUN, FLO, and. We also show the efficacy of our model in the case of extremely less labelled data regime.
  arXiv  Detail & Related papers  (2020-05-09T18:48:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.