DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot
Learning
- URL: http://arxiv.org/abs/2103.10764v1
- Date: Fri, 19 Mar 2021 12:24:42 GMT
- Title: DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot
Learning
- Authors: Bonan Li and Xuecheng Nie and Congying Han
- Abstract summary: Generative based strategy has shown great potential in the Generalized Zero-Shot Learning task.
Generative based strategy has shown great potential in the Generalized Zero-Shot Learning task.
We propose to enhance the generalizability of GZSL models via improving feature diversity of unseen classes.
- Score: 12.856168667514947
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative based strategy has shown great potential in the Generalized
Zero-Shot Learning task. However, it suffers severe generalization problem due
to lacking of feature diversity for unseen classes to train a good classifier.
In this paper, we propose to enhance the generalizability of GZSL models via
improving feature diversity of unseen classes. For this purpose, we present a
novel Diverse Feature Synthesis (DFS) model. Different from prior works that
solely utilize semantic knowledge in the generation process, DFS leverages
visual knowledge with semantic one in a unified way, thus deriving
class-specific diverse feature samples and leading to robust classifier for
recognizing both seen and unseen classes in the testing phase. To simplify the
learning, DFS represents visual and semantic knowledge in the aligned space,
making it able to produce good feature samples with a low-complexity
implementation. Accordingly, DFS is composed of two consecutive generators: an
aligned feature generator, transferring semantic and visual representations
into aligned features; a synthesized feature generator, producing diverse
feature samples of unseen classes in the aligned space. We conduct
comprehensive experiments to verify the efficacy of DFS. Results demonstrate
its effectiveness to generate diverse features for unseen classes, leading to
superior performance on multiple benchmarks. Code will be released upon
acceptance.
Related papers
- Exploring Data Efficiency in Zero-Shot Learning with Diffusion Models [38.36200871549062]
Zero-Shot Learning (ZSL) aims to enable classifiers to identify unseen classes by enhancing data efficiency at the class level.
This is achieved by generating image features from pre-defined semantics of unseen classes.
In this paper, we demonstrate that limited seen examples generally result in deteriorated performance of generative models.
This unified framework incorporates diffusion models to improve data efficiency at both the class and instance levels.
arXiv Detail & Related papers (2024-06-05T04:37:06Z) - SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized
Zero-Shot Learning [0.7420433640907689]
Generalized Zero-Shot Learning (GZSL) recognizes unseen classes by transferring knowledge from the seen classes.
This paper introduces a dual strategy to address the generalization gap.
arXiv Detail & Related papers (2023-12-20T15:18:51Z) - Synthetic Sample Selection for Generalized Zero-Shot Learning [4.264192013842096]
Generalized Zero-Shot Learning (GZSL) has emerged as a pivotal research domain in computer vision.
This paper proposes a novel approach for synthetic feature selection using reinforcement learning.
arXiv Detail & Related papers (2023-04-06T03:22:43Z) - GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot
Learning [55.79997930181418]
Generalized Zero-Shot Learning aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes.
It is a promising solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes.
We propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.
arXiv Detail & Related papers (2022-07-05T04:04:37Z) - Zero-Shot Logit Adjustment [89.68803484284408]
Generalized Zero-Shot Learning (GZSL) is a semantic-descriptor-based learning technique.
In this paper, we propose a new generation-based technique to enhance the generator's effect while neglecting the improvement of the classifier.
Our experiments demonstrate that the proposed technique achieves state-of-the-art when combined with the basic generator, and it can improve various generative zero-shot learning frameworks.
arXiv Detail & Related papers (2022-04-25T17:54:55Z) - Mitigating Generation Shifts for Generalized Zero-Shot Learning [52.98182124310114]
Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not observable during training.
We propose a novel Generation Shifts Mitigating Flow framework for learning unseen data synthesis efficiently and effectively.
Experimental results demonstrate that GSMFlow achieves state-of-the-art recognition performance in both conventional and generalized zero-shot settings.
arXiv Detail & Related papers (2021-07-07T11:43:59Z) - Generalized Few-shot Semantic Segmentation [68.69434831359669]
We introduce a new benchmark called Generalized Few-Shot Semantic (GFS-Seg) to analyze the ability of simultaneously segmenting the novel categories.
It is the first study showing that previous representative state-of-the-art generalizations fall short in GFS-Seg.
We propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the conditioned, on the content of each query image.
arXiv Detail & Related papers (2020-10-11T10:13:21Z) - Generative Hierarchical Features from Synthesizing Images [65.66756821069124]
We show that learning to synthesize images can bring remarkable hierarchical visual features that are generalizable across a wide range of applications.
The visual feature produced by our encoder, termed as Generative Hierarchical Feature (GH-Feat), has strong transferability to both generative and discriminative tasks.
arXiv Detail & Related papers (2020-07-20T18:04:14Z) - Adversarial Feature Hallucination Networks for Few-Shot Learning [84.31660118264514]
Adversarial Feature Hallucination Networks (AFHN) is based on conditional Wasserstein Generative Adversarial networks (cWGAN)
Two novel regularizers are incorporated into AFHN to encourage discriminability and diversity of the synthesized features.
arXiv Detail & Related papers (2020-03-30T02:43:16Z)
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.