CoAPT: Context Attribute words for Prompt Tuning

• URL: http://arxiv.org/abs/2407.13808v1
• Date: Thu, 18 Jul 2024 08:58:01 GMT
• Title: CoAPT: Context Attribute words for Prompt Tuning
• Authors: Gun Lee, Subin An, Sungyong Baik, Soochahn Lee,
• Abstract summary: We propose a novel prompt tuning method called CoAPT for few/zero-shot image classification. The core motivation is that attributes are descriptive words with rich information about a given concept. CoAPT integrates words as additional prompts within learnable prompt tuning and can be easily incorporated into various existing prompt tuning methods.
• Score: 5.811993982861212
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel prompt tuning method called CoAPT(Context Attribute words in Prompt Tuning) for few/zero-shot image classification. The core motivation is that attributes are descriptive words with rich information about a given concept. Thus, we aim to enrich text queries of existing prompt tuning methods, improving alignment between text and image embeddings in CLIP embedding space. To do so, CoAPT integrates attribute words as additional prompts within learnable prompt tuning and can be easily incorporated into various existing prompt tuning methods. To facilitate the incorporation of attributes into text embeddings and the alignment with image embeddings, soft prompts are trained together with an additional meta-network that generates input-image-wise feature biases from the concatenated feature encodings of the image-text combined queries. Our experiments demonstrate that CoAPT leads to considerable improvements for existing baseline methods on several few/zero-shot image classification tasks, including base-to-novel generalization, cross-dataset transfer, and domain generalization. Our findings highlight the importance of combining hard and soft prompts and pave the way for future research on the interplay between text and image latent spaces in pre-trained models.

Related papers

• Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment [0.7499722271664144]
  Contrastive Language and Image Pairing (CLIP) is a transformative method in multimedia retrieval. CLIP typically trains two neural networks concurrently to generate joint embeddings for text and image pairs. This paper addresses the challenge of optimizing CLIP models for various image-based similarity search scenarios.
  arXiv  Detail & Related papers  (2024-09-03T14:33:01Z)
• CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization [14.01847471143144]
  We introduce Context Regularization (CoRe), which enhances the learning of the new concept's text embedding by regularizing its context tokens in the prompt. CoRe can be applied to arbitrary prompts without requiring the generation of corresponding images. Comprehensive experiments demonstrate that our method outperforms several baseline methods in both identity preservation and text alignment.
  arXiv  Detail & Related papers  (2024-08-28T16:27:58Z)
• Decoder Pre-Training with only Text for Scene Text Recognition [54.93037783663204]
  Scene text recognition (STR) pre-training methods have achieved remarkable progress, primarily relying on synthetic datasets. We introduce a novel method named Decoder Pre-training with only text for STR (DPTR) DPTR treats text embeddings produced by the CLIP text encoder as pseudo visual embeddings and uses them to pre-train the decoder.
  arXiv  Detail & Related papers  (2024-08-11T06:36:42Z)
• Dynamic Prompt Optimizing for Text-to-Image Generation [63.775458908172176]
  We introduce the textbfPrompt textbfAuto-textbfEditing (PAE) method to improve text-to-image generative models. We employ an online reinforcement learning strategy to explore the weights and injection time steps of each word, leading to the dynamic fine-control prompts.
  arXiv  Detail & Related papers  (2024-04-05T13:44:39Z)
• Improving Cross-modal Alignment with Synthetic Pairs for Text-only Image Captioning [13.357749288588039]
  Previous works leverage the CLIP's cross-modal association ability for image captioning, relying solely on textual information under unsupervised settings. This paper proposes a novel method to address these issues by incorporating synthetic image-text pairs. A pre-trained text-to-image model is deployed to obtain images that correspond to textual data, and the pseudo features of generated images are optimized toward the real ones in the CLIP embedding space.
  arXiv  Detail & Related papers  (2023-12-14T12:39:29Z)
• Sentence-level Prompts Benefit Composed Image Retrieval [69.78119883060006]
  Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. We propose to leverage pretrained V-L models, e.g., BLIP-2, to generate sentence-level prompts. Our proposed method performs favorably against the state-of-the-art CIR methods on the Fashion-IQ and CIRR datasets.
  arXiv  Detail & Related papers  (2023-10-09T07:31:44Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• Task-Oriented Multi-Modal Mutual Leaning for Vision-Language Models [52.3032592038514]
  We propose a class-aware text prompt to enrich generated prompts with label-related image information. We achieve an average improvement of 4.03% on new classes and 3.19% on harmonic-mean over eleven classification benchmarks.
  arXiv  Detail & Related papers  (2023-03-30T06:02:40Z)
• Texts as Images in Prompt Tuning for Multi-Label Image Recognition [70.9310322461598]
  We advocate that image-text contrastive learning makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-11-23T07:00:11Z)

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.