Conceptual Codebook Learning for Vision-Language Models
- URL: http://arxiv.org/abs/2407.02350v3
- Date: Mon, 15 Jul 2024 14:00:24 GMT
- Title: Conceptual Codebook Learning for Vision-Language Models
- Authors: Yi Zhang, Ke Yu, Siqi Wu, Zhihai He,
- Abstract summary: We propose Codebook Learning (CoCoLe) to address the challenge of improving the generalization capability of vision-language models (VLMs)
We learn a conceptual codebook consisting of visual concepts as keys and conceptual prompts as values.
We observe that this conceptual codebook learning method is able to achieve enhanced alignment between visual and linguistic modalities.
- Score: 27.68834532978939
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose Conceptual Codebook Learning (CoCoLe), a novel fine-tuning method for vision-language models (VLMs) to address the challenge of improving the generalization capability of VLMs while fine-tuning them on downstream tasks in a few-shot setting. We recognize that visual concepts, such as textures, shapes, and colors are naturally transferable across domains and play a crucial role in generalization tasks. Motivated by this interesting finding, we learn a conceptual codebook consisting of visual concepts as keys and conceptual prompts as values, which serves as a link between the image encoder's outputs and the text encoder's inputs. Specifically, for a given image, we leverage the codebook to identify the most relevant conceptual prompts associated with the class embeddings to perform the classification. Additionally, we incorporate a handcrafted concept cache as a regularization to alleviate the overfitting issues in low-shot scenarios. We observe that this conceptual codebook learning method is able to achieve enhanced alignment between visual and linguistic modalities. Extensive experimental results demonstrate that our CoCoLe method remarkably outperforms the existing state-of-the-art methods across various evaluation settings, including base-to-new generalization, cross-dataset evaluation, and domain generalization tasks. Detailed ablation studies further confirm the efficacy of each component in CoCoLe.
Related papers
- Descriminative-Generative Custom Tokens for Vision-Language Models [101.40245125955306]
This paper explores the possibility of learning custom tokens for representing new concepts in Vision-Language Models (VLMs)
Our aim is to learn tokens that can be effective for both discriminative and generative tasks while composing well with words to form new input queries.
arXiv Detail & Related papers (2025-02-17T18:13:42Z) - V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer [19.177297480709512]
Concept Bottleneck Models (CBMs) offer inherent interpretability by translating images into human-comprehensible concepts.
Recent approaches have leveraged the knowledge of large language models to construct concept bottlenecks.
In this study, we investigate to avoid these issues by constructing CBMs directly from multimodal models.
arXiv Detail & Related papers (2025-01-09T05:12:38Z) - Knowledge Transfer Across Modalities with Natural Language Supervision [8.493435472659646]
We present a way to learn novel concepts by only using their textual description. Similarly to human perception, we leverage cross-modal interaction to introduce new concepts.
We show that Knowledge Transfer can successfully introduce novel concepts in multimodal models, in a very efficient manner.
arXiv Detail & Related papers (2024-11-23T17:26:50Z) - Enhancing Visual Document Understanding with Contrastive Learning in
Large Visual-Language Models [56.76307866160105]
We propose a contrastive learning framework, termed Document Object COntrastive learning (DoCo)
DoCo leverages an auxiliary multimodal encoder to obtain the features of document objects and align them to the visual features generated by the vision encoder of Large Visual-Language Models (LVLMs)
We demonstrate that the proposed DoCo serves as a plug-and-play pre-training method, which can be employed in the pre-training of various LVLMs without inducing any increase in computational complexity during the inference process.
arXiv Detail & Related papers (2024-02-29T10:17:27Z) - Concept-Guided Prompt Learning for Generalization in Vision-Language
Models [33.361744437967126]
We propose Concept-Guided Prompt Learning for vision-language models.
We leverage the well-learned knowledge of Contrastive Language-Image Pretraining to create a visual concept cache.
In order to refine the text features, we develop a projector that transforms multi-level visual features into text features.
arXiv Detail & Related papers (2024-01-15T04:04:47Z) - Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks [24.45212348373868]
This paper presents a novel concept learning framework for enhancing model interpretability and performance in visual classification tasks.
Our approach appends an unsupervised explanation generator to the primary classifier network and makes use of adversarial training.
This work presents a significant step towards building inherently interpretable deep vision models with task-aligned concept representations.
arXiv Detail & Related papers (2024-01-09T16:16:16Z) - Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models [64.24227572048075]
We propose a Knowledge-Aware Prompt Tuning (KAPT) framework for vision-language models.
Our approach takes inspiration from human intelligence in which external knowledge is usually incorporated into recognizing novel categories of objects.
arXiv Detail & Related papers (2023-08-22T04:24:45Z) - Cross-Modal Concept Learning and Inference for Vision-Language Models [31.463771883036607]
In existing fine-tuning methods, the class-specific text description is matched against the whole image.
We develop a new method called cross-model concept learning and inference (CCLI)
Our method automatically learns a large set of distinctive visual concepts from images using a set of semantic text concepts.
arXiv Detail & Related papers (2023-07-28T10:26:28Z) - ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image
Diffusion Models [79.10890337599166]
We introduce ConceptBed, a large-scale dataset that consists of 284 unique visual concepts and 33K composite text prompts.
We evaluate visual concepts that are either objects, attributes, or styles, and also evaluate four dimensions of compositionality: counting, attributes, relations, and actions.
Our results point to a trade-off between learning the concepts and preserving the compositionality which existing approaches struggle to overcome.
arXiv Detail & Related papers (2023-06-07T18:00:38Z) - K-LITE: Learning Transferable Visual Models with External Knowledge [242.3887854728843]
K-LITE (Knowledge-augmented Language-Image Training and Evaluation) is a strategy to leverage external knowledge to build transferable visual systems.
In training, it enriches entities in natural language with WordNet and Wiktionary knowledge.
In evaluation, the natural language is also augmented with external knowledge and then used to reference learned visual concepts.
arXiv Detail & Related papers (2022-04-20T04:47:01Z) - Concept Learners for Few-Shot Learning [76.08585517480807]
We propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions.
We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization and cell type annotation.
arXiv Detail & Related papers (2020-07-14T22:04:17Z)
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.