Search Results for author:
Found 13 papers, 10 papers with code
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.
Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation
Particularly, we propose i) semantic contrast to drive network learning by contrasting massive categorical object regions, leading to a more holistic object pattern understanding, and ii) semantic aggregation to gather diverse relational contexts in the memory to enrich semantic representations.
Locality-Aware Inter- and Intra-Video Reconstruction for Self-Supervised Correspondence Learning
Our target is to learn visual correspondence from unlabeled videos.
Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual Learning
Handwritten mathematical expression recognition aims to automatically generate LaTeX sequences from given images.
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.
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation
To address this, we introduce a novel approach for more accurate and efficient spatio-temporal segmentation.
Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models.
Ranked #37 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
(using extra training data)
DTDN: Dual-task De-raining Network
Removing rain streaks from rainy images is necessary for many tasks in computer vision, such as object detection and recognition.
Motion-Attentive Transition for Zero-Shot Video Object Segmentation
In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation.
Ranked #9 on
Unsupervised Video Object Segmentation
on FBMS test
Less Memory, Faster Speed: Refining Self-Attention Module for Image Reconstruction
Self-attention (SA) mechanisms can capture effectively global dependencies in deep neural networks, and have been applied to natural language processing and image processing successfully.
