We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. Since it is an under-explored problem, we first investigate the difficulty of the problem and identify the performance bottleneck by conducting systematic analyses of model components and individual sub-tasks with a simple baseline model. Based on the analyses, we propose ENInst with sub-task enhancement methods: instance-wise mask refinement for enhancing pixel localization quality and novel classifier composition for improving classification accuracy. Our proposed method lifts the overall performance by enhancing the performance of each sub-task. We demonstrate that our ENInst is 7.5 times more efficient in achieving comparable performance to the existing fully-supervised few-shot models and even outperforms them at times.
Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation
Beihang University（北京航空航天大学）；State Key Laboratory of Virtual Reality Technology and Systems（虚拟现实技术与系统国家重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）
High-quality saliency maps are essential in several machine learning application areas including explainable AI and weakly supervised object detection and segmentation. Many techniques have been developed to generate better saliency using neural networks. However, they are often limited to specific saliency visualisation methods or saliency issues. We propose a novel saliency enhancing approach called SESS (Saliency Enhancing with Scaling and Sliding). It is a method and model agnostic extension to existing saliency map generation methods. With SESS, existing saliency approaches become robust …
The collection of a high number of pixel-based labeled training samples for tree species identification is time consuming and costly in operational forestry applications. To address this problem, in this paper we investigate the effectiveness of explanation methods for deep neural networks in performing weakly supervised semantic segmentation using only image-level labels. Specifically, we consider four methods:i) class activation maps (CAM); ii) gradient-based CAM; iii) pixel correlation module; and iv) self-enhancing maps (SEM). We compare these methods with each other using both quantitativ…
Shanghai Key Laboratory of Intelligent Information Processing（上海市智能信息处理重点实验室）；School of Computer Science, Fudan University（复旦大学计算机科学学院）
Text segmentation is a challenging vision task with many downstream applications. Current text segmentation methods require pixel-level annotations, which are expensive in the cost of human labor and limited in application scenarios. In this paper, we take the first attempt to perform weakly-supervised text instance segmentation by bridging text recognition and text segmentation. The insight is that text recognition methods provide precise attention position of each text instance, and the attention location can feed to both a text adaptive refinement head (TAR) and a text segmentation head. Sp…
Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result in highlighting the most discriminative part of objects while ignoring the entire object extent. Recently, the transformer architecture has been deployed to WSOL to capture the long-range feature dependencies with self-attention mechanism and multilayer perceptron structure. Nevertheless, transformers lack the locality inductive bias inherent to CNNs and therefore may deteriorate local feature details in WSOL. In this…
Context Patch Fusion With Class Token Enhancement for Weakly Supervised Semantic Segmentation
School of Cyber Science and Engineering, Wuxi University（网络安全科学与工程学院，无锡大学）；School of Informatics, Xiamen University（信息学院，厦门大学）；School of Computer Science, University of Liverpool（计算机科学学院，利物浦大学）
Most of the existing semantic segmentation approaches with image-level class labels as supervision, highly rely on the initial class activation map (CAM) generated from the standard classification network. In this paper, a novel "Progressive Patch Learning" approach is proposed to improve the local details extraction of the classification, producing the CAM better covering the whole object rather than only the most discriminative regions as in CAMs obtained in conventional classification models. "Patch Learning" destructs the feature maps into patches and independently processes each local pat…
Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size, and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented …
Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation
Xi’an Jiaotong-Liverpool University（西交利物浦大学）；University of Liverpool（利物浦大学）；University of Aberdeen（阿伯丁大学）；Microsoft（微软公司）；Jiangsu United Vocational and Technical College（江苏联合职业技术学院）
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
Academy for Engineering and Technology, Fudan University（复旦大学工程技术学院）；Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东计算中心（国家济南超级计算中心））
In this work, the case of semantic segmentation on a small image dataset (simulated by 1000 randomly selected images from PASCAL VOC 2012), where only weak supervision signals (scribbles from user interaction) are available is studied. Especially, to tackle the problem of limited data annotations in image segmentation, transferring different pre-trained models and CRF based methods are applied to enhance the segmentation performance. To this end, RotNet, DeeperCluster, and Semi&Weakly Supervised Learning (SWSL) pre-trained models are transferred and finetuned in a DeepLab-v2 baseline, and dens…
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image features as extra supervision without explicitly exploiting the high-level semantic information of the objects, which will become ineffective when the foreground objects have similar appearances to the background or other objects nearby. We propose a new box-supervised instance segmentation approach by developing a Semantic-aware Instance Mask (SIM) generation paradigm. Instead of heavily relying on local pair-wise affi…
Plug-and-Play DISep: Separating Dense Instances for Scene-to-Pixel Weakly-Supervised Change Detection in High-Resolution Remote Sensing Images
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Institute of Artificial Intelligence, Wuhan University（武汉大学人工智能研究院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Graduate School of Frontier Sciences, University of Tokyo（东京大学前沿科学研究生院）；Institute of Geodesy and Photogrammetry, ETH Zürich（苏黎世联邦理工学院大地测量与摄影测量研究所）
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
Southern University of Science and Technology (SUSTech)（南方科技大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）；the University of Hong Kong（香港大学）；the Hong Kong University of Science and Technology（香港科技大学）
3D Weakly Supervised Semantic Segmentation via Class-Aware and Geometry-Guided Pseudo-Label Refinement
College of Computer Science, Beihang University（北京航空航天大学计算机科学学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；Meituan（美团）
Enhancing Weakly Supervised Multimodal Video Anomaly Detection through Text Guidance
School of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）；Alibaba Cloud（阿里云）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）
Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation
CMIC, Shanghai Jiao Tong University（上海交通大学CMIC）；Beijing Institute of Technology（北京理工大学）；National University of Defense Technology（国防科技大学）；CUHK（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）
EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications（智能工程与自动化学院，北京邮电大学）；State Grid Corporation of China（国家电网公司）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）
CAMEL2: Enhancing weakly supervised learning for histopathology images by incorporating the significance ratio
Multiscale Research Institute of Complex Systems, Fudan University（复旦大学复杂系统多尺度研究院）；Zhangjiang Fudan International Innovation Center, Fudan University（复旦大学张江复旦国际创新中心）；Shanghai AI Laboratory（上海人工智能实验室）；Thorough Lab, Thorough Future（透彻未来实验室）；China-Japan Friendship Hospital（中日友好医院）；Minjiang University（闽江学院）；Peking University（北京大学）；Xiamen University of Technology（厦门理工学院）；Chinese PLA General Hospital（中国人民解放军总医院）
Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning
Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Northeastern University（东北大学）；University of Lübeck（吕贝克大学）；Kindai University（近畿大学）；Jacobs University Bremen（不来梅雅各布大学）
The use of Environmental Microorganisms (EMs) offers a highly efficient, low cost and harmless remedy to environmental pollution, by monitoring and decomposing of pollutants. This relies on how the EMs are correctly segmented and identified. With the aim of enhancing the segmentation of weakly visible EM images which are transparent, noisy and have low contrast, a Pairwise Deep Learning Feature Network (PDLF-Net) is proposed in this study. The use of PDLFs enables the network to focus more on the foreground (EMs) by concatenating the pairwise deep learning features of each image to different b…
Beyond Euclidean: Dual-Space Representation Learning for Weakly Supervised Video Violence Detection
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Chongqing Institute for Brain and Intelligence（重庆脑与智能研究院）；Guangyang Bay Laboratory（广阳湾实验室）
Inception Institute of Artificial Intelligence（启元人工智能研究院）；Mohamed Bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Monash University（莫纳什大学）；Linköping University（林雪平大学）；University of California Merced（加州大学默塞德分校）；Google Research（谷歌研究院）；Yonsei University（延世大学）
This work proposes a weakly-supervised temporal action localization framework, called D2-Net, which strives to temporally localize actions using video-level supervision. Our main contribution is the introduction of a novel loss formulation, which jointly enhances the discriminability of latent embeddings and robustness of the output temporal class activations with respect to foreground-background noise caused by weak supervision. The proposed formulation comprises a discriminative and a denoising loss term for enhancing temporal action localization. The discriminative term incorporates a class…
DDG-Net: Discriminability-Driven Graph Network for Weakly-supervised Temporal Action Localization
Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Centre for Artificial Intelligence and Robotics, HKISI_CAS（中国科学院香港创新研究院人工智能与机器人创新中心）；University of Chinese Academy of Sciences（中国科学院大学）
CmFNet: Cross-modal Fusion Network for Weakly-supervised Segmentation of Medical Images
School of Physics, Peking University（北京大学物理学院）；School of Computer Science, Peking University（北京大学计算机学院）；Department of Radiotherapy, Peking University Cancer Hospital（北京大学肿瘤医院放疗科）；Department of Radiation Oncology, Peking University Third Hospital（北京大学第三医院放疗科）
