Search Results for author:
Found 11 papers, 8 papers with code
Clustering Propagation for Universal Medical Image Segmentation
}$ This enables knowledge acquired from prior slices to assist in the segmentation of the current slice, further efficiently bridging the communication between remote slices using mere 2D networks.
LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning
Current high-performance semantic segmentation models are purely data-driven sub-symbolic approaches and blind to the structured nature of the visual world.
Large-Scale Person Detection and Localization using Overhead Fisheye Cameras
Instead of existing efforts devoted to localizing tourist photos captured by perspective cameras, in this article, we focus on devising person positioning solutions using overhead fisheye cameras.
Segment and Track Anything
This report presents a framework called Segment And Track Anything (SAMTrack) that allows users to precisely and effectively segment and track any object in a video.
Boosting Video Object Segmentation via Space-time Correspondence Learning
Current top-leading solutions for video object segmentation (VOS) typically follow a matching-based regime: for each query frame, the segmentation mask is inferred according to its correspondence to previously processed and the first annotated frames.
Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation
The objective of this paper is self-supervised learning of video object segmentation.
Deep Hierarchical Semantic Segmentation
In this paper, we instead address hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise description of visual observation in terms of a class hierarchy.
Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning
Our target is to learn visual correspondence from unlabeled videos.
Locality-Aware Inter- and Intra-Video Reconstruction for Self-Supervised Correspondence Learning
Our target is to learn visual correspondence from unlabeled videos.
Group-Wise Learning for Weakly Supervised Semantic Segmentation
The framework explicitly encodes semantic dependencies in a group of images to discover rich semantic context for estimating more reliable pseudo ground-truths, which are subsequently employed to train more effective segmentation models.
Quality-Aware Memory Network for Interactive Volumetric Image Segmentation
The proposed network has two appealing characteristics: 1) The memory-augmented network offers the ability to quickly encode past segmentation information, which will be retrieved for the segmentation of other slices; 2) The quality assessment module enables the model to directly estimate the qualities of segmentation predictions, which allows an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement.
