Inverting and Understanding Object Detectors

• URL: http://arxiv.org/abs/2106.13933v1
• Date: Sat, 26 Jun 2021 03:31:59 GMT
• Title: Inverting and Understanding Object Detectors
• Authors: Ang Cao, Justin Johnson
• Abstract summary: We propose using inversion as a primary tool to understand modern object detectors and develop an optimization-based approach to layout inversion. We reveal intriguing properties of detectors by applying our layout inversion technique to a variety of modern object detectors.
• Score: 15.207501110589924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a core problem in computer vision, the performance of object detection has improved drastically in the past few years. Despite their impressive performance, object detectors suffer from a lack of interpretability. Visualization techniques have been developed and widely applied to introspect the decisions made by other kinds of deep learning models; however, visualizing object detectors has been underexplored. In this paper, we propose using inversion as a primary tool to understand modern object detectors and develop an optimization-based approach to layout inversion, allowing us to generate synthetic images recognized by trained detectors as containing a desired configuration of objects. We reveal intriguing properties of detectors by applying our layout inversion technique to a variety of modern object detectors, and further investigate them via validation experiments: they rely on qualitatively different features for classification and regression; they learn canonical motifs of commonly co-occurring objects; they use diff erent visual cues to recognize objects of varying sizes. We hope our insights can help practitioners improve object detectors.

Related papers

• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection [101.15777242546649]
  Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection. We present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge.
  arXiv  Detail & Related papers  (2024-06-01T17:32:26Z)
• Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection [8.977792536037956]
  In everyday indoor navigation, robots often needto detect non-distinctive small-change objects. Existing techniques rely on high-quality class-specific object priors to regularize a change detector model. In this study, we explore the concept of degree-of-ill-posedness (DoI) to improve both passive and activevision.
  arXiv  Detail & Related papers  (2024-05-10T01:56:39Z)
• Deep Active Perception for Object Detection using Navigation Proposals [39.52573252842573]
  We propose a generic supervised active perception pipeline for object detection. It can be trained using existing off-the-shelf object detectors, while also leveraging advances in simulation environments. The proposed method was evaluated on synthetic datasets, constructed within the Webots robotics simulator.
  arXiv  Detail & Related papers  (2023-12-15T20:55:52Z)
• A Comprehensive Study on Object Detection Techniques in Unconstrained Environments [0.0]
  Object detection is a crucial task in computer vision that aims to identify and localize objects in images or videos. The recent advancements in deep learning and Convolutional Neural Networks (CNNs) have significantly improved the performance of object detection techniques. This paper presents a comprehensive study of object detection techniques in unconstrained environments, including various challenges, datasets, and state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-04-11T15:45:03Z)
• Fast and Accurate Object Detection on Asymmetrical Receptive Field [0.0]
  This article proposes methods for improving object detection accuracy from the perspective of changing receptive fields. The structure of the head part of YOLOv5 is modified by adding asymmetrical pooling layers. The performances of the new model in this article are compared with original YOLOv5 model and analyzed from several parameters.
  arXiv  Detail & Related papers  (2023-03-15T23:59:18Z)
• Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey [10.665235711722076]
  Oriented object detection is one of the most fundamental and challenging tasks in remote sensing. Recent years have witnessed remarkable progress in oriented object detection using deep learning techniques.
  arXiv  Detail & Related papers  (2023-02-21T06:31:53Z)
• Contrastive Object Detection Using Knowledge Graph Embeddings [72.17159795485915]
  We compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs. We propose a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
  arXiv  Detail & Related papers  (2021-12-21T17:10:21Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)
• Unadversarial Examples: Designing Objects for Robust Vision [100.4627585672469]
  We develop a framework that exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design "robust objects" We demonstrate the efficacy of the framework on a wide variety of vision-based tasks ranging from standard benchmarks to (in-simulation) robotics.
  arXiv  Detail & Related papers  (2020-12-22T18:26:07Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)

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.