Box-based Refinement for Weakly Supervised and Unsupervised Localization Tasks
It has been established that training a box-based detector network can enhance the localization performance of weakly supervised and unsupervised methods. Moreover, we extend this understanding by demonstrating that these detectors can be utilized to improve the original network, paving the way for further advancements. To accomplish this, we train the detectors on top of the network output instead of the image data and apply suitable loss backpropagation. Our findings reveal a significant improvement in phrase grounding for the ``what is where by looking'' task, as well as various methods of unsupervised object discovery. Our code is available at https://github.com/eyalgomel/box-based-refinement.
Code (1)
Tasks
Object DiscoveryPhrase GroundingSimilar Papers 제목 키워드 기반
A Comparative Study on Effects of Original and Pseudo Labels for Weakly Supervised Learning for Car Localization Problem
In this study, the effects of different class labels created as a result of multiple conceptual meanings on localization using Weakly Supervised Learning presented on Car Dataset. In addition, the generated labels are in…
Edge DetectionWeakly-supervised LearningContext-Aware Weakly Supervised Image Manipulation Localization with SAM Refinement
Malicious image manipulation poses societal risks, increasing the importance of effective image manipulation detection methods. Recent approaches in image manipulation detection have largely been driven by fully supervis…
Image ManipulationImage Manipulation DetectionImage Manipulation LocalizationRefineLoc: Iterative Refinement for Weakly-Supervised Action Localization
Video action detectors are usually trained using datasets with fully-supervised temporal annotations. Building such datasets is an expensive task. To alleviate this problem, recent methods have tried to leverage weak lab…
Action LocalizationTemporal Action LocalizationTemporal LocalizationWeakly Supervised Action Localization+1Learning Temporal Co-Attention Models for Unsupervised Video Action Localization
Temporal action localization (TAL) in untrimmed videos recently receives tremendous research enthusiasm. To our best knowledge, this is the first attempt in the literature to explore this task under an unsupervised setti…
Action LocalizationClusteringTemporal Action LocalizationTripletDistilling Knowledge from Refinement in Multiple Instance Detection Networks
Weakly supervised object detection (WSOD) aims to tackle the object detection problem using only labeled image categories as supervision. A common approach used in WSOD to deal with the lack of localization information i…
Knowledge DistillationMultiple Instance LearningObjectobject-detection+4