136 citations · 274 across the 11 of their papers we have counts for
10 papers · 1 filter
PP-MobileSeg: Explore the Fast and Accurate Semantic Segmentation Model on Mobile Devices
Shiyu Tang, Ting Sun, Juncai Peng +6
The success of transformers in computer vision has led to several attempts to adapt them for mobile devices, but their performance remains unsatisfactory in some real-world applica…
RAIS: Robust and Accurate Interactive Segmentation via Continual Learning
Yuying Hao, Yi Liu, Juncai Peng +5
Interactive image segmentation aims at segmenting a target region through a way of human-computer interaction. Recent works based on deep learning have achieved excellent performan…
EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle
Yuying Hao, Yi Liu, Yizhou Chen +7
In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks. However, to unleash the ful…
PP-Matting: High-Accuracy Natural Image Matting
Guowei Chen, Yi Liu, Jian Wang +11
Natural image matting is a fundamental and challenging computer vision task. It has many applications in image editing and composition. Recently, deep learning-based approaches hav…
PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model
Juncai Peng, Yi Liu, Shiyu Tang +13
Real-world applications have high demands for semantic segmentation methods. Although semantic segmentation has made remarkable leap-forwards with deep learning, the performance of…
PP-HumanSeg: Connectivity-Aware Portrait Segmentation with a Large-Scale Teleconferencing Video Dataset
Lutao Chu, Yi Liu, Zewu Wu +8
As the COVID-19 pandemic rampages across the world, the demands of video conferencing surge. To this end, real-time portrait segmentation becomes a popular feature to replace backg…
EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow
Yuying Hao, Yi Liu, Zewu Wu +8
High-quality training data play a key role in image segmentation tasks. Usually, pixel-level annotations are expensive, laborious and time-consuming for the large volume of trainin…
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
Shiyu Tang, Ruihao Gong, Yan Wang +9
Deep neural networks (DNNs) are vulnerable to adversarial noises, which motivates the benchmark of model robustness. Existing benchmarks mainly focus on evaluating defenses, but th…
Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning
Shiyu Tang, Peijun Tang, Yanxiang Gong +2
Previous feature alignment methods in Unsupervised domain adaptation(UDA) mostly only align global features without considering the mismatch between class-wise features. In this wo…
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World
Jiakai Wang, Aishan Liu, Zixin Yin +3
Deep learning models are vulnerable to adversarial examples. As a more threatening type for practical deep learning systems, physical adversarial examples have received extensive r…