iCAR: Bridging Image Classification and Image-text Alignment for Visual Recognition

• URL: http://arxiv.org/abs/2204.10760v1
• Date: Fri, 22 Apr 2022 15:27:21 GMT
• Title: iCAR: Bridging Image Classification and Image-text Alignment for Visual Recognition
• Authors: Yixuan Wei, Yue Cao, Zheng Zhang, Zhuliang Yao, Zhenda Xie, Han Hu, Baining Guo
• Abstract summary: Image classification, which classifies images by pre-defined categories, has been the dominant approach to visual representation learning over the last decade. Visual learning through image-text alignment, however, has emerged to show promising performance, especially for zero-shot recognition. We propose a deep fusion method with three adaptations that effectively bridge two learning tasks.
• Score: 33.2800417526215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image classification, which classifies images by pre-defined categories, has been the dominant approach to visual representation learning over the last decade. Visual learning through image-text alignment, however, has emerged to show promising performance, especially for zero-shot recognition. We believe that these two learning tasks are complementary, and suggest combining them for better visual learning. We propose a deep fusion method with three adaptations that effectively bridge two learning tasks, rather than shallow fusion through naive multi-task learning. First, we modify the previous common practice in image classification, a linear classifier, with a cosine classifier which shows comparable performance. Second, we convert the image classification problem from learning parametric category classifier weights to learning a text encoder as a meta network to generate category classifier weights. The learnt text encoder is shared between image classification and image-text alignment. Third, we enrich each class name with a description to avoid confusion between classes and make the classification method closer to the image-text alignment. We prove that this deep fusion approach performs better on a variety of visual recognition tasks and setups than the individual learning or shallow fusion approach, from zero-shot/few-shot image classification, such as the Kornblith 12-dataset benchmark, to downstream tasks of action recognition, semantic segmentation, and object detection in fine-tuning and open-vocabulary settings. The code will be available at https://github.com/weiyx16/iCAR.

Related papers

• Finetuning CLIP to Reason about Pairwise Differences [52.028073305958074]
  We propose an approach to train vision-language models such as CLIP in a contrastive manner to reason about differences in embedding space. We first demonstrate that our approach yields significantly improved capabilities in ranking images by a certain attribute. We also illustrate that the resulting embeddings obey a larger degree of geometric properties in embedding space.
  arXiv  Detail & Related papers  (2024-09-15T13:02:14Z)
• Towards Image Semantics and Syntax Sequence Learning [8.033697392628424]
  We introduce the concept of "image grammar", consisting of "image semantics" and "image syntax" We propose a weakly supervised two-stage approach to learn the image grammar relative to a class of visual objects/scenes. Our framework is trained to reason over patch semantics and detect faulty syntax.
  arXiv  Detail & Related papers  (2024-01-31T00:16:02Z)
• Knowledge Mining with Scene Text for Fine-Grained Recognition [53.74297368412834]
  We propose an end-to-end trainable network that mines implicit contextual knowledge behind scene text image. We employ KnowBert to retrieve relevant knowledge for semantic representation and combine it with image features for fine-grained classification. Our method outperforms the state-of-the-art by 3.72% mAP and 5.39% mAP, respectively.
  arXiv  Detail & Related papers  (2022-03-27T05:54:00Z)
• Learning Contrastive Representation for Semantic Correspondence [150.29135856909477]
  We propose a multi-level contrastive learning approach for semantic matching. We show that image-level contrastive learning is a key component to encourage the convolutional features to find correspondence between similar objects.
  arXiv  Detail & Related papers  (2021-09-22T18:34:14Z)
• Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition [38.49419948988415]
  Deep networks can learn to accurately recognize objects of a category by training on a large number of images. A meta-learning challenge known as a low-shot image recognition task comes when only a few images with annotations are available for learning a recognition model for one category. Our method, called Cascaded Feature Matching Network (CFMN), is proposed to solve this problem. Experiments for few-shot learning on two standard datasets, emphminiImageNet and Omniglot, have confirmed the effectiveness of our method.
  arXiv  Detail & Related papers  (2021-01-13T11:37:28Z)
• Grafit: Learning fine-grained image representations with coarse labels [114.17782143848315]
  This paper tackles the problem of learning a finer representation than the one provided by training labels. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods.
  arXiv  Detail & Related papers  (2020-11-25T19:06:26Z)
• Zero-Shot Recognition through Image-Guided Semantic Classification [9.291055558504588]
  We present a new embedding-based framework for zero-shot learning (ZSL) Motivated by the binary relevance method for multi-label classification, we propose to inversely learn the mapping between an image and a semantic classifier. IGSC is conceptually simple and can be realized by a slight enhancement of an existing deep architecture for classification.
  arXiv  Detail & Related papers  (2020-07-23T06:22:40Z)
• Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation [128.03739769844736]
  Two neural co-attentions are incorporated into the classifier to capture cross-image semantic similarities and differences. In addition to boosting object pattern learning, the co-attention can leverage context from other related images to improve localization map inference. Our algorithm sets new state-of-the-arts on all these settings, demonstrating well its efficacy and generalizability.
  arXiv  Detail & Related papers  (2020-07-03T21:53:46Z)
• SCAN: Learning to Classify Images without Labels [73.69513783788622]
  We advocate a two-step approach where feature learning and clustering are decoupled. A self-supervised task from representation learning is employed to obtain semantically meaningful features. We obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime.
  arXiv  Detail & Related papers  (2020-05-25T18:12:33Z)
• One-Shot Image Classification by Learning to Restore Prototypes [11.448423413463916]
  One-shot image classification aims to train image classifiers over the dataset with only one image per category. For one-shot learning, the existing metric learning approaches would suffer poor performance because the single training image may not be representative of the class. We propose a simple yet effective regression model, denoted by RestoreNet, which learns a class transformation on the image feature to move the image closer to the class center in the feature space.
  arXiv  Detail & Related papers  (2020-05-04T02:11:30Z)

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.