许多现实世界中的计算机视觉任务，如深度补全，必须处理具有任意形状缺失或无效数据区域的输入。对于卷积神经网络（CNNs），部分卷积通过仅基于有效像素的掩码意识重新归一化解决了这一问题。最近，State Space Models（SSMs）如Mamba出现，提供了高性能和线性复杂度。然而，这些架构缺乏处理此类任意形状无效数据的内在机制。为填补这一空白，我们引入了Partial Vision Mamba（PVM），一种将部分操作原理应用于Mamba骨干网络的新架构组件。我们还定义了一系列规则来设计使用PVM的架构。我们在深度补全、图像修复和带有无效数据的分类任务中展示了我们方法的有效性和通用性。
英文摘要：
Many real-world computer vision tasks, such as depth completion, must handle inputs with arbitrarily shaped regions of missing or invalid data. For Convolutional Neural Networks (CNNs), Partial Convolutions solved this by a mask-aware re-normalization conditioned only on valid pixels. Recently, State Space Models (SSMs) like Mamba have emerged, offering high performance with linear complexity. However, these architectures lack an inherent mechanism for handling such arbitrarily shaped invalid data at inference time. To bridge this gap, we introduce Partial Vision Mamba (PVM), a novel architectural component that ports the principles of partial operations to the Mamba backbone. We also define a series of rules to design architectures using PVM. We show the efficacy and generalizability of our approach in the tasks of depth completion, image inpainting, and classification with invalid data.
Baidu Inc.（百度公司）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）
Inpainting arbitrary missing regions is challenging because learning valid features for various masked regions is nontrivial. Though U-shaped encoder-decoder frameworks have been witnessed to be successful, most of them share a common drawback of mask unawareness in feature extraction because all convolution windows (or regions), including those with various shapes of missing pixels, are treated equally and filtered with fixed learned kernels. To this end, we propose our novel mask-aware inpainting solution. Firstly, a Mask-Aware Dynamic Filtering (MADF) module is designed to effectively learn…
Foreground-aware image synthesis aims to generate images as well as their foreground masks. A common approach is to formulate an image as an masked blending of a foreground image and a background image. It is a challenging problem because it is prone to reach the trivial solution where either image overwhelms the other, i.e., the masks become completely full or empty, and the foreground and background are not meaningfully separated. We present FurryGAN with three key components: 1) imposing both the foreground image and the composite image to be realistic, 2) designing a mask as a combination …
CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Video Subtitle Removal
University of Electronic Science and Technology of China（电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Technical University of Munich（慕尼黑技术大学）；Shanghai Jiao Tong University（上海交通大学）；National University of Singapore（新加坡国立大学）
Edit Where You Mean: Region-Aware Adapter Injection for Mask-Free Local Image Editing
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Beijing University of Aeronautics and Astronautics（北京航空航天大学）；Tsinghua University（清华大学）；Peking University（北京大学）；Xiaohongshu Inc.（小红书公司）
The task of face attribute manipulation has found increasing applications, but still remains challenging with the requirement of editing the attributes of a face image while preserving its unique details. In this paper, we choose to combine the Variational AutoEncoder (VAE) and Generative Adversarial Network (GAN) for photorealistic image generation. We propose an effective method to modify a modest amount of pixels in the feature maps of an encoder, changing the attribute strength continuously without hindering global information. Our training objectives of VAE and GAN are reinforced by the s…
Timestep-Aware Block Masking for Efficient Diffusion Model Inference
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Data Science, Fudan University（复旦大学数据科学学院）
The Chinese University of Hong Kong（香港中文大学）；Adobe Inc.（奥多比公司）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shanghai AI Laboratory（上海人工智能实验室）
Recent studies have shown the importance of modeling long-range interactions in the inpainting problem. To achieve this goal, existing approaches exploit either standalone attention techniques or transformers, but usually under a low resolution in consideration of computational cost. In this paper, we present a novel transformer-based model for large hole inpainting, which unifies the merits of transformers and convolutions to efficiently process high-resolution images. We carefully design each component of our framework to guarantee the high fidelity and diversity of recovered images. Specifi…
AVI-Edit: Audio-sync Video Instance Editing with Granularity-Aware Mask Refiner
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；State Key Lab of Multimedia Info. Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；Nat’l Eng. Research Ctr. of Visual Tech., School of Computer Science, Peking University（北京大学计算机学院视觉技术工程研究中心）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
MAGIC: Few-Shot Mask-Guided Anomaly Inpainting with Prompt Perturbation, Spatially Adaptive Guidance, and Context Awareness
Department of AI Semiconductor Engineering, Hanyang University, Seoul, Korea（汉阳大学人工智能半导体工程系）；Department of Electronic Engineering, Hanyang University, Seoul, Korea（汉阳大学电子工程系）
This paper presents Mask-aware Intersection-over-Union (maIoU) for assigning anchor boxes as positives and negatives during training of instance segmentation methods. Unlike conventional IoU or its variants, which only considers the proximity of two boxes; maIoU consistently measures the proximity of an anchor box with not only a ground truth box but also its associated ground truth mask. Thus, additionally considering the mask, which, in fact, represents the shape of the object, maIoU enables a more accurate supervision during training. We present the effectiveness of maIoU on a state-of-the-…
MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders
S-Lab, Nanyang Technological University（新加坡南洋理工大学S-Lab）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；University of Chinese Academy of Sciences（中国科学院大学）
CMAMRNet: A Contextual Mask-Aware Network Enhancing Mural Restoration Through Comprehensive Mask Guidance
University of Macau（澳门大学）；Guangdong University of Technology（广东工业大学）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）；Shenzhen Polytechnic University（深圳职业技术大学）
The University of Western Australia（西澳大学）；Griffith University（格里菲斯大学）
We propose a self-supervised spatio-temporal matching method, coined Motion-Aware Mask Propagation (MAMP), for video object segmentation. MAMP leverages the frame reconstruction task for training without the need for annotations. During inference, MAMP extracts high-resolution features from each frame to build a memory bank from the features as well as the predicted masks of selected past frames. MAMP then propagates the masks from the memory bank to subsequent frames according to our proposed motion-aware spatio-temporal matching module to handle fast motion and long-term matching scenarios. …
MUST（澳门科技大学）；Mininglamp Academy of Sciences, Mininglamp Technology（明略科技集团明略科学院）；NLPR, CASIA, UCAS（中国科学院自动化研究所模式识别国家重点实验室，中国科学院大学）；CVC, UB（巴塞罗那大学计算机视觉中心）；Baidu Research（百度研究院）；Westlake University（西湖大学）；University of Oulu（奥卢大学）
Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask identities, types of sensors, and a total number of videos; 2) low-fidelity quality of facial masks. Basic deep models and remote photoplethysmography (rPPG) methods achieved acceptable performance on these benchmarks but still far from the needs of practical scenarios. To bridge the gap to real-world applications, we introduce a largescale High-Fidelity Mask dataset, n…
Granular Ball Guided Masking: Structure-aware Data Augmentation
Chongqing Key Laboratory of Computational Intelligence（重庆计算智能重点实验室）；Key Laboratory of Cyberspace Big Data Intelligent Security（网络大数据智能安全重点实验室）；Sichuan-Chongqing Co-construction Key Laboratory of Digital Economy Intelligence（四川-重庆数字经济智能联合重点实验室）；Key Laboratory of Big Data Intelligent Computing（大数据智能计算重点实验室）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Carnegie Mellon University（卡内基梅隆大学）；Ryerson University（瑞尔森大学）；Facebook AI Research（Facebook人工智能研究院）
We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a "segmentation-aware" variant that allows a neuron …
We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation module using a novel top-$k$ filtering strategy in reading the space-time memory. To effectively take the user's intent into account, a novel difference-aware module is proposed to learn how to properly fuse the masks before and after each interaction, which are aligned with the targ…
Computer-aided diagnosis has recently received attention for its advantage of low cost and time efficiency. Although deep learning played a major role in the recent success of acne detection, there are still several challenges such as color shift by inconsistent illumination, variation in scales, and high density distribution. To address these problems, we propose an acne detection network which consists of three components, specifically: Composite Feature Refinement, Dynamic Context Enhancement, and Mask-Aware Multi-Attention. First, Composite Feature Refinement integrates semantic informatio…
MAPRPose: Mask-Aware Proposal and Amodal Refinement for Multi-Object 6D Pose Estimation
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（机器人系统国家重点实验室，哈尔滨工业大学）；School of Civil Engineering, Harbin Institute of Technology（土木工程学院，哈尔滨工业大学）；Shenzhen Infinite Meta Robot Co., Ltd（深圳无限元机器人有限公司）
Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；Huawei Inc.（华为公司）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）
Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形研究所）；TU Darmstadt（达姆施塔特工业大学）
Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARS-CoV-2 coronavirus. However, wearing a mask poses challenges for different face recognition tasks and raises concerns about the performance of masked face presentation detection (PAD). The main issues facing the mask face PAD are the wrongly classified bona fide masked faces and the wrongly classified partial attacks (covered by real masks). This work addresses these issues by proposing a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted …
ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentation
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University, Ministry of Education（东南大学教育部新一代人工智能技术及其跨学科应用重点实验室）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；School of Medicine, Case Western Reserve University（凯斯西储大学医学院）
Department of Computer Science, National Tsing Hua University（台湾清华大学计算机科学系）；Microsoft AI R&D Center（微软人工智能研发中心）
Deep learning approaches have achieved highly accurate face recognition by training the models with very large face image datasets. Unlike the availability of large 2D face image datasets, there is a lack of large 3D face datasets available to the public. Existing public 3D face datasets were usually collected with few subjects, leading to the over-fitting problem. This paper proposes two CNN models to improve the RGB-D face recognition task. The first is a segmentation-aware depth estimation network, called DepthNet, which estimates depth maps from RGB face images by including semantic segmen…
The Chinese University of Hong Kong（香港中文大学）；Massachusetts Institute of Technology（麻省理工学院）；MEGVII Technology（旷视科技）
In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features. Differentiating multiple potential instances within a single PoI feature is challenging because learning a high-dimensional mask feature for each instance using vanilla convolution demands a heavy computing burden. To address this challenge, we propose an instance-aware convolution. It decomposes this mask representation learning task into two tractable modules as instance-aware weights and instance-agnostic features. The former is to parametrize convolution for producing mask feat…
Generalized Mask-aware IoU for Anchor Assignment for Real-time Instance Segmentation
TU Darmstadt（达姆施塔特工业大学）；METU（中东技术大学）；Five AI Ltd.（Five AI 有限公司）；LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS（LIGM，巴黎高科桥路学校，古斯塔夫·埃菲尔大学，法国国家科学研究中心）；Center for Robotics and Artificial Intelligence (ROMER), METU（中东技术大学机器人与人工智能中心（ROMER））
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…
