Learning Conditional Attributes for Compositional Zero-Shot Learning
- URL: http://arxiv.org/abs/2305.17940v2
- Date: Wed, 14 Jun 2023 13:55:00 GMT
- Title: Learning Conditional Attributes for Compositional Zero-Shot Learning
- Authors: Qingsheng Wang, Lingqiao Liu, Chenchen Jing, Hao Chen, Guoqiang Liang,
Peng Wang, Chunhua Shen
- Abstract summary: Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts.
One of the challenges is to model attributes interacted with different objects, e.g., the attribute wet" in wet apple" and wet cat" is different.
We argue that attributes are conditioned on the recognized object and input image and explore learning conditional attribute embeddings.
- Score: 78.24309446833398
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Compositional Zero-Shot Learning (CZSL) aims to train models to recognize
novel compositional concepts based on learned concepts such as attribute-object
combinations. One of the challenges is to model attributes interacted with
different objects, e.g., the attribute ``wet" in ``wet apple" and ``wet cat" is
different. As a solution, we provide analysis and argue that attributes are
conditioned on the recognized object and input image and explore learning
conditional attribute embeddings by a proposed attribute learning framework
containing an attribute hyper learner and an attribute base learner. By
encoding conditional attributes, our model enables to generate flexible
attribute embeddings for generalization from seen to unseen compositions.
Experiments on CZSL benchmarks, including the more challenging C-GQA dataset,
demonstrate better performances compared with other state-of-the-art approaches
and validate the importance of learning conditional attributes. Code is
available at https://github.com/wqshmzh/CANet-CZSL
Related papers
- Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning [12.558701595138928]
Compositional Zero-Shot Learning (CZSL) aims to predict unknown compositions made up of attribute and object pairs.
We are exploring Open World Compositional Zero-Shot Learning (OW-CZSL) in this study, where our test space encompasses all potential combinations of attributes and objects.
Our approach involves utilizing the self-attention mechanism between attributes and objects to achieve better generalization from seen to unseen compositions.
arXiv Detail & Related papers (2024-07-18T17:11:29Z) - MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot Learning [33.12021227971062]
Compositional Zero-Shot Learning (CZSL) aims to learn semantic primitives (attributes and objects) from seen neglecting and recognize unseen attribute-object compositions.
We introduce the Multi-Attribute Composition dataset, encompassing 18,217 images and 11,067 compositions with comprehensive, representative, and diverse attribute annotations.
Our dataset supports deeper semantic understanding and higher-order attribute associations, providing a more realistic and challenging benchmark for the CZSL task.
arXiv Detail & Related papers (2024-06-18T16:24:48Z) - 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) - Boosting Zero-shot Learning via Contrastive Optimization of Attribute
Representations [28.46906100680767]
We propose a new framework to boost Zero-shot learning (ZSL) by explicitly learning attribute prototypes beyond images.
A prototype generation module is designed to generate attribute prototypes from attribute semantics.
A hard example-based contrastive optimization scheme is introduced to reinforce attribute-level features in the embedding space.
arXiv Detail & Related papers (2022-07-08T11:05:35Z) - Learning Invariant Visual Representations for Compositional Zero-Shot
Learning [30.472541551048508]
Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen-object compositions in the training set.
We propose an invariant feature learning framework to align different domains at the representation and gradient levels.
Experiments on two CZSL benchmarks demonstrate that the proposed method significantly outperforms the previous state-of-the-art.
arXiv Detail & Related papers (2022-06-01T11:33:33Z) - Compositional Fine-Grained Low-Shot Learning [58.53111180904687]
We develop a novel compositional generative model for zero- and few-shot learning to recognize fine-grained classes with a few or no training samples.
We propose a feature composition framework that learns to extract attribute features from training samples and combines them to construct fine-grained features for rare and unseen classes.
arXiv Detail & Related papers (2021-05-21T16:18:24Z) - Learning to Infer Unseen Attribute-Object Compositions [55.58107964602103]
A graph-based model is proposed that can flexibly recognize both single- and multi-attribute-object compositions.
We build a large-scale Multi-Attribute dataset with 116,099 images and 8,030 composition categories.
arXiv Detail & Related papers (2020-10-27T14:57:35Z) - Attribute Prototype Network for Zero-Shot Learning [113.50220968583353]
We propose a novel zero-shot representation learning framework that jointly learns discriminative global and local features.
Our model points to the visual evidence of the attributes in an image, confirming the improved attribute localization ability of our image representation.
arXiv Detail & Related papers (2020-08-19T06:46:35Z)
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.