Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition

• URL: http://arxiv.org/abs/2004.00705v1
• Date: Wed, 1 Apr 2020 21:00:06 GMT
• Title: Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
• Authors: Luming Tang, Davis Wertheimer, Bharath Hariharan
• Abstract summary: Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes. A solution is to use pose-normalized representations. We show that they are extremely effective for few-shot fine-grained classification.
• Score: 46.15360203412185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e.g., birds) based on a few images alone. This requires a remarkable degree of invariance to pose, articulation and background. A solution is to use pose-normalized representations: first localize semantic parts in each image, and then describe images by characterizing the appearance of each part. While such representations are out of favor for fully supervised classification, we show that they are extremely effective for few-shot fine-grained classification. With a minimal increase in model capacity, pose normalization improves accuracy between 10 and 20 percentage points for shallow and deep architectures, generalizes better to new domains, and is effective for multiple few-shot algorithms and network backbones. Code is available at https://github.com/Tsingularity/PoseNorm_Fewshot

Related papers

• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• What does a platypus look like? Generating customized prompts for zero-shot image classification [52.92839995002636]
  This work introduces a simple method to generate higher accuracy prompts without relying on any explicit knowledge of the task domain. We leverage the knowledge contained in large language models (LLMs) to generate many descriptive sentences that contain important discriminating characteristics of the image categories. This approach improves accuracy on a range of zero-shot image classification benchmarks, including over one percentage point gain on ImageNet.
  arXiv  Detail & Related papers  (2022-09-07T17:27:08Z)
• Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation [50.621269117524925]
  Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. We present a semantic distribution-aware contrastive adaptation algorithm that enables pixel-wise representation alignment. We evaluate SDCA on multiple benchmarks, achieving considerable improvements over existing algorithms.
  arXiv  Detail & Related papers  (2021-05-11T13:21:25Z)
• Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning [116.91819311885166]
  We propose a hierarchical semantic alignment strategy via expanding the views generated by a single image to textbfCross-samples and Multi-level representation. Our method, termed as CsMl, has the ability to integrate multi-level visual representations across samples in a robust way.
  arXiv  Detail & Related papers  (2020-12-04T17:26:24Z)
• Background Splitting: Finding Rare Classes in a Sea of Background [55.03789745276442]
  We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories. In these scenarios, almost all images belong to the background category in the dataset (>95% of the dataset is background) We demonstrate that both standard fine-tuning approaches and state-of-the-art approaches for training on imbalanced datasets do not produce accurate deep models in the presence of this extreme imbalance.
  arXiv  Detail & Related papers  (2020-08-28T23:05:15Z)
• 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)
• Continual Local Replacement for Few-shot Learning [13.956960291580938]
  The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few of labeled data could not accurately represent the true data distribution. A novel continual local replacement strategy is proposed to address the data deficiency problem.
  arXiv  Detail & Related papers  (2020-01-23T04:26:21Z)

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.