Search Results for author:
Found 6 papers, 2 papers with code
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
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
DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection
We solve this task by dividing it into two sub-tasks, co-peak search and instance mask segmentation.
Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization
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).
Robust Image Alignment With Multiple Feature Descriptors and Matching-Guided Neighborhoods
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.
Descriptor Ensemble: An Unsupervised Approach to Descriptor Fusion in the Homography Space
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.
Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data
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.
