1 code implementation • NeurIPS 2019 • Cheng-Chun Hsu, Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Yung-Yu Chuang
This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations.
Box-supervised Instance Segmentation Multiple Instance Learning +4
1 code implementation • CVPR 2019 • Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang
We solve this task by dividing it into two sub-tasks, co-peak search and instance mask segmentation.
no code implementations • ECCV 2018 • Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Xiaoning Qian, Yung-Yu Chuang
In this paper, we address co-saliency detection in a set of images jointly covering objects of a specific class by an unsupervised convolutional neural network (CNN).
no code implementations • CVPR 2015 • Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang
First, the performance of descriptor-based approaches to image alignment relies on the chosen descriptor, but the optimal descriptor typically varies from image to image, or even pixel to pixel.
no code implementations • 13 Dec 2014 • Yuan-Ting Hu, Yen-Yu Lin, Hsin-Yi Chen, Kuang-Jui Hsu, Bing-Yu Chen
Inspired by the observation that the homographies of correct feature correspondences vary smoothly along the spatial domain, our approach stands on the unsupervised nature of feature matching, and can select a good descriptor for matching each feature point.
no code implementations • CVPR 2014 • Feng-Ju Chang, Yen-Yu Lin, Kuang-Jui Hsu
By treating a bounding box as a bag with its segment hypotheses as structured instances, MSIL-CRF selects the most likely segment hypotheses by leveraging the knowledge derived from both the labeled and uncertain training data.