Improvising Weakly Supervised Object Detection (WSOD) using Deep Learning Technique

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 uses self-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 vision applications in near future.