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Outline

Title
Abstract
Introduction
Related Work
Comparative Analysis with Existing Models
Experimental Results with Response-Time
Experimental Results with Unseen Concepts
Conclusion and Future Work
References
All Topics
Computer Science
Artificial Intelligence

UnseenNet: Fast Training Detector for Any Unseen Concept

Profile image of asra aslamasra aslam

2022, ArXiv

https://doi.org/10.48550/ARXIV.2203.08759
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10 pages

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Abstract

Training of object detection models using less data is currently the focus of existing N-shot learning models in computer vision. Such methods use object-level labels and takes hours to train on unseen classes. There are many cases where we have large amount of image-level labels available for training but cannot be utilized by few shot object detection models for training. There is a need for a machine learning framework that can be used for training any unseen class and can become useful in real-time situations. In this paper, we proposed an “Unseen Class Detector” that can be trained within a very short time for any possible unseen class without bounding boxes with competitive accuracy. We build our approach on “Strong” and “Weak” baseline detectors, which we trained on existing object detection and image classification datasets, respectively. Unseen concepts are fine-tuned on the strong baseline detector using only image-level labels and further adapted by transferring the classifi...

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Ayush Jaiswal

2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021

Class-agnostic object detection using object proposal methods (OPMs), conventional class-aware detectors, and the proposed adversarially trained class-agnostic detectors. Green and magenta boxes indicate ground-truth and detection outputs, respectively. OPMs generate regions of interest that could contain objects without precisely locating any object. Class-aware detectors detect objects from a set of known object-types. The proposed class-agnostic detection aims to localize all objects irrespective of their types including those of unknown classes. Results show that the proposed adversarially trained class-agnostic models detect novel objects for which no annotations are available. Best viewed digitally and zoomed in.

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Real Time Object Detection using CNN based Single Shot Detector Model
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2021

Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs con...

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Weakly-supervised Any-shot Object Detection
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Classifier Crafting: Turn Your ConvNet into a Zero-Shot Learner!
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Few-Shot Object Detection: A Survey
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