39 citations · 92 across the 39 of their papers we have counts for
7 papers · 1 filter
High Quality Segmentation for Ultra High-resolution Images
Tiancheng Shen, Yuechen Zhang, Lu Qi +5
To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation. Common strategies, such as down-sampling, patch cropping, and cascade…
CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation
Lu Qi, Jason Kuen, Zhe Lin +7
To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either task-unrelated or task-specific training signals f…
Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling
Dat Huynh, Jason Kuen, Zhe Lin +2
Open-vocabulary instance segmentation aims at segmenting novel classes without mask annotations. It is an important step toward reducing laborious human supervision. Most existing…
Multi-Scale Aligned Distillation for Low-Resolution Detection
Lu Qi, Jason Kuen, Jiuxiang Gu +5
In instance-level detection tasks (e.g., object detection), reducing input resolution is an easy option to improve runtime efficiency. However, this option traditionally hurts the…
Open-World Entity Segmentation
Lu Qi, Jason Kuen, Yi Wang +5
We introduce a new image segmentation task, called Entity Segmentation (ES), which aims to segment all visual entities (objects and stuffs) in an image without predicting their sem…
SelfDoc: Self-Supervised Document Representation Learning
Peizhao Li, Jiuxiang Gu, Jason Kuen +5
We propose SelfDoc, a task-agnostic pre-training framework for document image understanding. Because documents are multimodal and are intended for sequential reading, our framework…
Multimodal Contrastive Training for Visual Representation Learning
Xin Yuan, Zhe Lin, Jason Kuen +5
We develop an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives. Unlik…