Take a look at what we’re working on…
We are an academic research group focusing on Artificial Intelligence for Brain-inspired Visual Learning, Unmanned System and Medical Science.
Pathways to AI impact
Recently, the Part-Object Relational (POR) saliency underpinned by the Capsule Network (CapsNet) has been demonstrated to be an effective modeling mechanism to improve the saliency detection accuracy. However, it is widely known that the current capsule routing operations have huge computational complexity, which seriously limited the usability of the POR saliency models in real-time applications.
This repository is the official implementation of SAC. In this work, we tackle the temporal action localization task from the perspective of modality, and precisely assign frame-modality attention. Paper from arXiv or IEEE.
To facilitate integrity learning for SOD, we design a novel Integrity Cognition Network (ICON), which explores three important components to learn strong integrity features.
This paper aims at building a medical slice synthesis model to increase the inter-slice resolution of an input 3D volume.
We design a unified region feature synthesizer for feature synthesizing in real-world detection scenarios.
This repository is the official implementation of Onfocus Detection:Identifying Individual-Camera Eye Contact from Unconstrained Images.
This is the official implementation of Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation, arXiv, IJCV 2022.
Decoupling the sibling head has recently shown great potential in relieving the inherent task-misalignment problem in two-stage object detectors. However, existing works design similar structures for the classification and regression, ignoring task-specific characteristics and feature demands.
This repository is the official implementation of Colar. In this work, we study the online action detection and develop an effective and efficient exemplar-consultation mechanism. Paper from arXiv.
This is an implementation of the paper Cross-Modality Deep Feature Learning for Brain Tumor Segmentation published on Pattern Recognition. The “2m_re1” program is for BraTS 2018.
Object detectors that solely rely on image contrast are struggling to detect camouflaged objects in images because of the high similarity between camouflaged objects and their surroundings. To address this issue, in this paper, we investigate the role of the part-object relationship for camouflaged object detection.
Weakly supervised temporal action localization aims at learning the instance-level action pattern from the video-level labels, where a challenge is action-context confusion.
Weakly supervised temporal action localization is a newly emerging yet widely studied topic in recent years. The existing methods can be categorized into two localization-by-classification pipelines, i.e., the pre-classification pipeline and the post-classification pipeline.
This is an implementation of the paper Exploring Task Structure for Brain Tumor Segmentation From Multi-Modality MR Images published on IEEE Transaction on Image Processing.
Recent years have witnessed a big leap in automatic visual saliency detection attributed to advances in deep learning, especially Convolutional Neural Networks (CNNs). However, inferring the saliency of each image part separately, as was adopted by most CNNs methods, inevitably leads to an incomplete segmentation of the salient object.
What we’re up to…
On July 22, the list of “2022 Wu Wenjun AI Science and Technology Award”, the highest award in China’s AI field, was officially released.
Professor Junwei Han Have Been Elected Fellow of 2022 International Association for Pattern Recognition（IAPR Fellow）
ACM MM 2022 officially released the list of accepted papers. Multiple papers from our team are included.
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