Visual Probing and Correction of Object Recognition Models with Interactive user feedback

• URL: http://arxiv.org/abs/2012.14544v1
• Date: Tue, 29 Dec 2020 00:36:12 GMT
• Title: Visual Probing and Correction of Object Recognition Models with Interactive user feedback
• Authors: Viny Saajan Victor, Pramod Vadiraja, Jan-Tobias Sohns, Heike Leitte
• Abstract summary: This paper attempts to visualise the uncertainties in object recognition models and propose a correction process via user feedback. We demonstrate our approach on the data provided by the VAST 2020 Mini-Challenge 2.
• Score: 2.8101673772585736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advent of state-of-the-art machine learning and deep learning technologies, several industries are moving towards the field. Applications of such technologies are highly diverse ranging from natural language processing to computer vision. Object recognition is one such area in the computer vision domain. Although proven to perform with high accuracy, there are still areas where such models can be improved. This is in-fact highly important in real-world use cases like autonomous driving or cancer detection, that are highly sensitive and expect such technologies to have almost no uncertainties. In this paper, we attempt to visualise the uncertainties in object recognition models and propose a correction process via user feedback. We further demonstrate our approach on the data provided by the VAST 2020 Mini-Challenge 2.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Deepfake Generation and Detection: A Benchmark and Survey [134.19054491600832]
  Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions. This survey comprehensively reviews the latest developments in deepfake generation and detection. We focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing.
  arXiv  Detail & Related papers  (2024-03-26T17:12:34Z)
• Overview of Class Activation Maps for Visualization Explainability [0.0]
  Class Activation Maps (CAMs) enhance interpretability and insights into the decision-making process of deep learning models. This work presents a comprehensive overview of the evolution of Class Activation Maps over time. It also explores the metrics used for evaluating CAMs and introduces auxiliary techniques to improve the saliency of these methods.
  arXiv  Detail & Related papers  (2023-09-25T17:20:51Z)
• Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning [0.0]
  This paper explores computer vision's potential in security and surveillance, presenting a novel approach to track motion in videos. By categorizing motion into Arcs, Lanes, Converging/Diverging, and Random/Block motions, the paper examines different optical flow techniques, CNN models, and machine learning models. The results can train anomaly-detection models, provide behavioral insights based on motion, and enhance scene comprehension.
  arXiv  Detail & Related papers  (2023-08-26T16:09:20Z)
• Machine Unlearning: A Survey [56.79152190680552]
  A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
  arXiv  Detail & Related papers  (2023-06-06T10:18:36Z)
• Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California [4.089055556130725]
  We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition.
  arXiv  Detail & Related papers  (2023-03-14T00:57:11Z)
• A neuromorphic approach to image processing and machine vision [0.9137554315375922]
  We explore the implementation of visual tasks such as image segmentation, visual attention and object recognition. We have emphasized on the employment of non-volatile memory devices such as memristors to realize artificial visual systems.
  arXiv  Detail & Related papers  (2022-08-07T05:01:57Z)
• Cognitive Visual Inspection Service for LCD Manufacturing Industry [80.63336968475889]
  This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry. System is based on two cornerstones: robust/high-performance defect recognition model and cognitive visual inspection service architecture.
  arXiv  Detail & Related papers  (2021-01-11T08:14:35Z)
• Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning [69.99522650448213]
  Research in machine learning is at a turning point. Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks. This white paper provides an introduction and discussion of this emerging field in machine learning research.
  arXiv  Detail & Related papers  (2020-12-21T15:07:19Z)
• Object Detection and Recognition of Swap-Bodies using Camera mounted on a Vehicle [13.702911401489427]
  This project aims to jointly perform object detection of a swap-body and to find the type of swap-body by reading an ILU code. Recent research activities have drastically improved deep learning techniques which proves to enhance the field of computer vision.
  arXiv  Detail & Related papers  (2020-04-17T08:49:54Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

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.