PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition

• URL: http://arxiv.org/abs/2407.10918v1
• Date: Mon, 15 Jul 2024 17:19:50 GMT
• Title: PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition
• Authors: Xiao Li, Yining Liu, Na Dong, Sitian Qin, Xiaolin Hu,
• Abstract summary: Deep learning-based object recognition systems can be easily fooled by various adversarial perturbations. One reason for the weak robustness may be that they do not have part-based inductive bias like the human recognition process. Motivated by this, several part-based recognition models have been proposed to improve the adversarial robustness of recognition.
• Score: 23.264326593735316
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning-based object recognition systems can be easily fooled by various adversarial perturbations. One reason for the weak robustness may be that they do not have part-based inductive bias like the human recognition process. Motivated by this, several part-based recognition models have been proposed to improve the adversarial robustness of recognition. However, due to the lack of part annotations, the effectiveness of these methods is only validated on small-scale nonstandard datasets. In this work, we propose PIN++, short for PartImageNet++, a dataset providing high-quality part segmentation annotations for all categories of ImageNet-1K (IN-1K). With these annotations, we build part-based methods directly on the standard IN-1K dataset for robust recognition. Different from previous two-stage part-based models, we propose a Multi-scale Part-supervised Model (MPM), to learn a robust representation with part annotations. Experiments show that MPM yielded better adversarial robustness on the large-scale IN-1K over strong baselines across various attack settings. Furthermore, MPM achieved improved robustness on common corruptions and several out-of-distribution datasets. The dataset, together with these results, enables and encourages researchers to explore the potential of part-based models in more real applications.

Related papers

• PartImageNet++ Dataset: Enhancing Visual Models with High-Quality Part Annotations [31.652689569798003]
  We introduce PartImageNet++ (PIN++), a dataset that provides detailed part annotations for all categories in ImageNet-1K.<n>With 100 annotated images per category, totaling 100K images, PIN++ represents the most comprehensive dataset covering a diverse range of object categories.<n>We propose a Multi-scale Part-supervised recognition Model (MPM) for robust classification on ImageNet-1K.
  arXiv  Detail & Related papers  (2026-01-04T09:43:49Z)
• Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation [0.36832029288386137]
  We present a multi-scale OOD segmentation method that exploits the confidence information of a foreground-background segmentation model. We consider the per pixel confidence score of the model prediction which is close to 1 for a pixel in a foreground object. By aggregating these confidence values for different sized patches, objects of various sizes can be identified in a single image.
  arXiv  Detail & Related papers  (2024-12-22T12:09:27Z)
• Unleashing the Power of Generic Segmentation Models: A Simple Baseline for Infrared Small Target Detection [57.666055329221194]
  We investigate the adaptation of generic segmentation models, such as the Segment Anything Model (SAM), to infrared small object detection tasks. Our model demonstrates significantly improved performance in both accuracy and throughput compared to existing approaches.
  arXiv  Detail & Related papers  (2024-09-07T05:31:24Z)
• Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning [13.68867780184022]
  Few-shot learning aims to recognize new concepts using a limited number of visual samples. Our framework incorporates both the abstract class semantics and the concrete class entities extracted from Large Language Models (LLMs) For the challenging one-shot setting, our approach, utilizing the ResNet-12 backbone, achieves an average improvement of 1.95% over the second-best competitor.
  arXiv  Detail & Related papers  (2024-08-22T15:10:20Z)
• Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images [12.365801596593936]
  Medical image segmentation is one of the domains where sufficient annotated data is not available. We propose a prototype-based self-supervised one-way one-shot learning framework using pseudo-labels generated from superpixels. We show that the proposed simple but potent framework performs at par with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-12T15:38:51Z)
• A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap [50.079224604394]
  We present a novel model-agnostic framework called textbfContext-textbfEnhanced textbfFeature textbfAment (CEFA) CEFA consists of a feature alignment module and a context enhancement module. Our method can serve as a plug-and-play module to improve the detection performance of HOI models on rare categories.
  arXiv  Detail & Related papers  (2024-07-31T08:42:48Z)
• Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping [40.07070188661184]
  Weakly-Supervised Concealed Object (WSCOS) aims to segment objects well blended with surrounding environments. It is hard to distinguish concealed objects from the background due to the intrinsic similarity. We propose a new WSCOS method to address these two challenges.
  arXiv  Detail & Related papers  (2023-05-18T14:31:34Z)
• Part-Based Models Improve Adversarial Robustness [57.699029966800644]
  We show that combining human prior knowledge with end-to-end learning can improve the robustness of deep neural networks. Our model combines a part segmentation model with a tiny classifier and is trained end-to-end to simultaneously segment objects into parts. Our experiments indicate that these models also reduce texture bias and yield better robustness against common corruptions and spurious correlations.
  arXiv  Detail & Related papers  (2022-09-15T15:41:47Z)
• Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning [41.07029317930986]
  We propose a variance-sensitive class of models that operates in a low-label regime. The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier. We further extend this approach to a transductive learning setting, proposing Transductive CNAPS.
  arXiv  Detail & Related papers  (2022-01-13T18:59:02Z)
• Attentional Prototype Inference for Few-Shot Segmentation [128.45753577331422]
  We propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot segmentation. We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution. We conduct extensive experiments on four benchmarks, where our proposal obtains at least competitive and often better performance than state-of-the-art prototype-based methods.
  arXiv  Detail & Related papers  (2021-05-14T06:58:44Z)
• SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes [56.387647750094466]
  Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples. Most of advanced solutions exploit a metric learning framework that performs segmentation through matching each pixel to a learned foreground prototype. This framework suffers from biased classification due to incomplete construction of sample pairs with the foreground prototype only.
  arXiv  Detail & Related papers  (2021-04-19T11:21:47Z)
• Part-aware Prototype Network for Few-shot Semantic Segmentation [50.581647306020095]
  We propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes.
  arXiv  Detail & Related papers  (2020-07-13T11:03:09Z)
• Objectness-Aware Few-Shot Semantic Segmentation [31.13009111054977]
  We show how to increase overall model capacity to achieve improved performance. We introduce objectness, which is class-agnostic and so not prone to overfitting. Given only one annotated example of an unseen category, experiments show that our method outperforms state-of-art methods with respect to mIoU.
  arXiv  Detail & Related papers  (2020-04-06T19:12:08Z)

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.