Deep learning-based object detectors have shown remarkable improvements. However,
supervised learning-based methods perform poorly when the train data and the test data …

Abstract Multiple Instance Learning (MIL) is a fundamental method for weakly supervised
object detection (WSOD), but experiences difficulty in excluding local optimal solutions and …

Weakly supervised object detection has attracted more and more attention as it only needs
image-level annotations for training object detectors. A popular solution to this task is to train …

Deep networks have shown remarkable results in the task of object detection. However, their
performance suffers critical drops when they are subsequently trained on novel classes …

Object detection results have been rapidly improved over a short period of time with the
development of deep convolutional neural networks. Although impressive results have been …

Weakly-supervised semantic segmentation aims at tackling the dense labeling task using
weak supervision so as to reduce human annotation efforts. For weakly-supervised semantic …

Video salient object detection is a challenging and important problem in computer vision
domain. In recent years, deep-learning based methods have contributed to significant …

Automated nodule detection in the ultrasound image is essential for computer-aided thyroid
tumor diagnosis. However, in the ultrasound image, the solid nodule has imaging …

This paper presents a Convolutional Neural Network (CNN) approach for counting and
locating objects in high-density imagery. To the best of our knowledge, this is the first object …

In computer vision, the research community has been looking to how to benefit from weakly
supervised learning that utilizes easily obtained image-level labels to train neural network …