Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection

• URL: http://arxiv.org/abs/2212.04613v1
• Date: Fri, 9 Dec 2022 00:34:50 GMT
• Title: Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection
• Authors: Kyle Buettner, Adriana Kovashka
• Abstract summary: We conduct an empirical study of contrastive learning and out-of-domain object detection. We propose strategies to augment views and enhance robustness in appearance-shifted and context-shifted scenarios. Our results and insights show how to ensure robustness through the choice of views in contrastive learning.
• Score: 37.06088084592779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning has emerged as a competitive pretraining method for object detection. Despite this progress, there has been minimal investigation into the robustness of contrastively pretrained detectors when faced with domain shifts. To address this gap, we conduct an empirical study of contrastive learning and out-of-domain object detection, studying how contrastive view design affects robustness. In particular, we perform a case study of the detection-focused pretext task Instance Localization (InsLoc) and propose strategies to augment views and enhance robustness in appearance-shifted and context-shifted scenarios. Amongst these strategies, we propose changes to cropping such as altering the percentage used, adding IoU constraints, and integrating saliency based object priors. We also explore the addition of shortcut-reducing augmentations such as Poisson blending, texture flattening, and elastic deformation. We benchmark these strategies on abstract, weather, and context domain shifts and illustrate robust ways to combine them, in both pretraining on single-object and multi-object image datasets. Overall, our results and insights show how to ensure robustness through the choice of views in contrastive learning.

Related papers

• Self-Supervised Representation Learning for Adversarial Attack Detection [6.528181610035978]
  Supervised learning-based adversarial attack detection methods rely on a large number of labeled data. We propose a self-supervised representation learning framework for the adversarial attack detection task to address this drawback.
  arXiv  Detail & Related papers  (2024-07-05T09:37:16Z)
• Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange [50.45953583802282]
  We introduce a novel self-supervised learning (SSL) strategy for point cloud scene understanding. Our approach leverages both object patterns and contextual cues to produce robust features. Our experiments demonstrate the superiority of our method over existing SSL techniques.
  arXiv  Detail & Related papers  (2024-04-11T06:39:53Z)
• GOOD: Towards Domain Generalized Orientated Object Detection [39.76969237020444]
  Oriented object detection has been rapidly developed in the past few years, but most of these methods assume the training and testing images are under the same statistical distribution. We propose the task of domain generalized oriented object detection, which intends to explore the generalization of oriented object detectors on arbitrary unseen target domains.
  arXiv  Detail & Related papers  (2024-02-20T07:12:22Z)
• ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection [70.11264880907652]
  Recent object (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. We propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and camouflaged zooming in and out. Our framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks.
  arXiv  Detail & Related papers  (2023-10-31T06:11:23Z)
• PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation [50.556961575275345]
  We propose a perception-aware fusion framework to promote segmentation robustness in adversarial scenes. We show that our scheme substantially enhances the robustness, with gains of 15.3% mIOU, compared with advanced competitors.
  arXiv  Detail & Related papers  (2023-08-08T01:55:44Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• CoDo: Contrastive Learning with Downstream Background Invariance for Detection [10.608660802917214]
  We propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo) The pretext task is converted to focus on instance location modeling for various backgrounds, especially for downstream datasets. Experiments on MSCOCO demonstrate that the proposed CoDo with common backbones, ResNet50-FPN, yields strong transfer learning results for object detection.
  arXiv  Detail & Related papers  (2022-05-10T01:26:15Z)
• Object-aware Contrastive Learning for Debiased Scene Representation [74.30741492814327]
  We develop a novel object-aware contrastive learning framework that localizes objects in a self-supervised manner. We also introduce two data augmentations based on ContraCAM, object-aware random crop and background mixup, which reduce contextual and background biases during contrastive self-supervised learning.
  arXiv  Detail & Related papers  (2021-07-30T19:24:07Z)
• Instance Localization for Self-supervised Detection Pretraining [68.24102560821623]
  We propose a new self-supervised pretext task, called instance localization. We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection.
  arXiv  Detail & Related papers  (2021-02-16T17:58:57Z)

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.