Most video compression methods focus on human visual perception, neglecting semantic preservation. This leads to severe semantic loss during the compression, hampering downstream video analysis tasks. In this paper, we propose a Masked Video Modeling (MVM)-powered compression framework that particularly preserves video semantics, by jointly mining and compressing the semantics in a self-supervised manner. While MVM is proficient at learning generalizable semantics through the masked patch prediction task, it may also encode non-semantic information like trivial textural details, wasting bitcost and bringing semantic noises. To suppress this, we explicitly regularize the non-semantic entropy of the compressed video in the MVM token space. The proposed framework is instantiated as a simple Semantic-Mining-then-Compression (SMC) model. Furthermore, we extend SMC as an advanced SMC++ model from several aspects. First, we equip it with a masked motion prediction objective, leading to better temporal semantic learning ability. Second, we introduce a Transformer-based compression module, to improve the semantic compression efficacy. Considering that directly mining the complex redundancy among heterogeneous features in different coding stages is non-trivial, we introduce a compact blueprint semantic representation to align these features into a similar form, fully unleashing the power of the Transformer-based compression module. Extensive results demonstrate the proposed SMC and SMC++ models show remarkable superiority over previous traditional, learnable, and perceptual quality-oriented video codecs, on three video analysis tasks and seven datasets. \textit{Codes and model are available at: https://github.com/tianyuan168326/VideoSemanticCompression-Pytorch.
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation
Xidian University（西安电子科技大学）；Rhode Island Hospital（罗德岛医院）；Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Central South University（中南大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
This paper presents a novel framework for unsupervised anomaly detection on masked objects called ODDObjects, which stands for Out-of-Distribution Detection on Objects. ODDObjects is designed to detect anomalies of various categories using unsupervised autoencoders trained on COCO-style datasets. The method utilizes autoencoder-based image reconstruction, where high reconstruction error indicates the possibility of an anomaly. The framework extends previous work on anomaly detection with autoencoders, comparing state-of-the-art models trained on object recognition datasets. Various model archi…
Unsupervised Part Discovery via Descriptor-Based Masked Image Restoration with Optimized Constraints
Faculty of Engineering and IT, University of Technology Sydney（技术悉尼大学工程与信息学院）；Dept. of Comp. Sci. and Eng., Southern University of Science and Technology（南方科技大学计算机科学与工程系）
Masked Clustering Prediction for Unsupervised Point Cloud Pre-training
University of Pisa（比萨大学）；University of Trento（特伦托大学）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）；Fondazione Bruno Kessler（布鲁诺·凯索尔基金会）
East China University of Science and Technology（华东理工大学）；Oklahoma State University（俄克拉荷马州立大学）
Previous work has shown that adversarial learning can be used for unsupervised monocular depth and visual odometry (VO) estimation, in which the adversarial loss and the geometric image reconstruction loss are utilized as the mainly supervisory signals to train the whole unsupervised framework. However, the performance of the adversarial framework and image reconstruction is usually limited by occlusions and the visual field changes between frames. This paper proposes a masked generative adversarial network (GAN) for unsupervised monocular depth and ego-motion estimation.The MaskNet and Boolea…
As a pioneering work, PointContrast conducts unsupervised 3D representation learning via leveraging contrastive learning over raw RGB-D frames and proves its effectiveness on various downstream tasks. However, the trend of large-scale unsupervised learning in 3D has yet to emerge due to two stumbling blocks: the inefficiency of matching RGB-D frames as contrastive views and the annoying mode collapse phenomenon mentioned in previous works. Turning the two stumbling blocks into empirical stepping stones, we first propose an efficient and effective contrastive learning framework, which generates…
Tsinghua University（清华大学）；Huazhong University of Science and Technology（华中科技大学）
Unsupervised visual anomaly detection conveys practical significance in many scenarios and is a challenging task due to the unbounded definition of anomalies. Moreover, most previous methods are application-specific, and establishing a unified model for anomalies across application scenarios remains unsolved. This paper proposes a novel hybrid framework termed Siamese Transition Masked Autoencoders(ST-MAE) to handle various visual anomaly detection tasks uniformly via deep feature transition. Concretely, the proposed method first extracts hierarchical semantics features from a pre-trained deep…
Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling
School of Automation Engineering, University of Electronic Science and Technology of China(UESTC)（电子科技大学自动化工程学院）；Shenzhen Institute for Advanced Study, UESTC（电子科技大学深圳高等研究院）
State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of finetuning on labeled data. On the other hand, models pre-trained with large-scale text-image supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classification tasks. However, the zero-shot performance of CLIP-like models are often insufficient for real-world adoption. In this…
Nanyang Technological University（南洋理工大学）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)（南洋理工大学-不列颠哥伦比亚大学活跃老年生活卓越联合研究中心（LILY））
Recent advances in skeleton-based person re-identification (re-ID) obtain impressive performance via either hand-crafted skeleton descriptors or skeleton representation learning with deep learning paradigms. However, they typically require skeletal pre-modeling and label information for training, which leads to limited applicability of these methods. In this paper, we focus on unsupervised skeleton-based person re-ID, and present a generic Simple Masked Contrastive learning (SimMC) framework to learn effective representations from unlabeled 3D skeletons for person re-ID. Specifically, to fully…
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs. Taking inspiration from autoregressive generative models that predict the current pixel from past pixels in a raster-scan ordering created with masked convolutions, we propose to use different orderings over the inputs using various forms of masked convolutions to construct different views of the data. For a given input, the model produces a pair of predictions with two valid orderings, and is then trained to maximize the mutual inf…
Xiamen University（厦门大学）；PKU-Wuhan Institute for Artificial Intelligence（北大武汉人工智能研究院）
Due to the limited availability of anomalous samples for training, video anomaly detection is commonly viewed as a one-class classification problem. Many prevalent methods investigate the reconstruction difference produced by AutoEncoders (AEs) under the assumption that the AEs would reconstruct the normal data well while reconstructing anomalies poorly. However, even with only normal data training, the AEs often reconstruct anomalies well, which depletes their anomaly detection performance. To alleviate this issue, we propose a simple yet efficient framework for video anomaly detection. The p…
The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets. Unsupervised learning does not require labels and is more suitable for solving medical image analysis problems. However, most of the current unsupervised learning methods need to be applied to large datasets. To make unsupervised learning applicable to small datasets, we proposed Swin MAE, which is a masked autoencoder with Swin Transformer as its backbone. Even on a dataset of only a few thousand medical images and without using any pre-trained models, S…
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping
Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH)（图宾根心理健康中心精神病学与心理治疗系）；Norwegian Centre for Mental Disorders Research（挪威精神障碍研究中心）；Donders Institute for Brain, Cognition, and Behaviour, Radboud University（拉德堡德大学唐德斯脑、认知与行为研究所）
SMC-NCA: Semantic-guided Multi-level Contrast for Semi-supervised Temporal Action Segmentation
School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder
Harvard University（哈佛大学）；Singapore Management University（新加坡管理大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；University of Surrey（萨里大学）
Monash University（莫纳什大学）；Curtin University（科廷大学）；Indian Institute of Technology Ropar（印度理工学院鲁尔基分校）
This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). Our proposed framework, named MARLIN, is a facial video masked autoencoder, that learns highly robust and generic facial embeddings from abundantly available non-annotated web crawled facial videos. As a challenging auxiliary task, MARLIN reconstructs the spatio-temporal details of the face from the …
LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space
Department of Engineering University of Fukui（工程系冈山大学）；Department of Electronics and Information Engineering National Institute of Technology, Ishikawa College（电子信息工程系石川县国立技术学院）
Masked Representation Modeling for Domain-Adaptive Segmentation
South China University of Technology（华南理工大学）；School of Electronic and Information Engineering, Wuyi University（武夷大学电子与信息工程学院）；South China Agricultural University（华南农业大学）
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
Columbia University（哥伦比亚大学）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）；University of Maryland, College Park（马里兰大学帕克分校）；University of California, Irvine（加州大学欧文分校）；University of Rochester（罗切斯特大学）；Universidade Federal de São Paulo（圣保罗联邦大学）；Duke University（杜克大学）；Emory University（埃默里大学）
Unsupervised Region-Based Image Editing of Denoising Diffusion Models
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Pengcheng Laboratory（鹏城实验室）；University of Trento（特伦托大学）；Lanzhou Jiaotong University（兰州交通大学）
Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optic…
LetsMap: Unsupervised Representation Learning for Semantic BEV Mapping
University of Freiburg（弗莱堡大学）；Qualcomm SARL France（法国高通公司）；QT Technologies Ireland Limited（爱尔兰QT技术有限公司）；Federal University of Rio Grande（里奥格兰德联邦大学）；University of Technology Nuremberg（纽伦堡工业大学）
