Improvising Weakly Supervised Object Detection (WSOD) using Deep Learning Technique
Jyoti G. Wadmare1, Sunita R. Patil2
1Ms. Jyoti G. Wadmare*, Assistant Professor, Department of Computer Engineering, KJSIEIT, university of Mumbai, India.
2Dr. Sunita R. Patil, Vice Principal, Professors at KJSIEIT, Mumbai, University of Mumbai (UoM), India.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 728-732 | Volume-9 Issue-3, February, 2020. | Retrieval Number: B3796129219/2020©BEIESP | DOI: 10.35940/ijeat.B3796.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Object detection is closely related with video and image analysis. Under computer vision technology, object detection model training with image-level labels only is challenging research area.Researchers have not yet discovered accurate model for Weakly Supervised Object Detection (WSOD). WSOD is used for detecting and localizing the objects under the supervision of image level annotations only.The proposed work usesself-paced approach which is applied on region proposal network of Faster R-CNN architecture which gives better solution from previous weakly-supervised object detectors and it can be applied for computer visionapplications in near future.
Keywords: MIL, Object Detection, Weakly Supervised Learning, WSOD.