Advanced diffusion models have made notable progress in text-to-image compositional generation. However, it is still a challenge for existing models to achieve text-image alignment when confronted with complex text prompts. In this work, we highlight two factors that affect this alignment: the quality of the randomly initialized noise and the reliability of the generated controlling mask. We then propose PiCo (Pick-and-Control), a novel training-free approach with two key components to tackle these two factors. First, we develop a noise selection module to assess the quality of the random noise and determine whether the noise is suitable for the target text. A fast sampling strategy is utilized to ensure efficiency in the noise selection stage. Second, we introduce a referring mask module to generate pixel-level masks and to precisely modulate the cross-attention maps. The referring mask is applied to the standard diffusion process to guide the reasonable interaction between text and image features. Extensive experiments have been conducted to verify the effectiveness of PiCo in liberating users from the tedious process of random generation and in enhancing the text-image alignment for diverse text descriptions.
Masked Conditional Diffusion Model for Enhancing Deepfake Detection
Chengdu University of Information Technology（成都信息工程大学）；Purdue University in Indianapolis（印第安纳波利斯普渡大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University at Albany, State University of New York (SUNY)（纽约州立大学奥尔巴尼分校）
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）
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models
Rutgers University（新泽西罗格斯大学）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；Red Hat AI Innovation（红帽AI创新）；Google DeepMind（谷歌DeepMind）；NYU（纽约大学）；Walmart Global Tech（沃尔玛全球技术）；NEC Labs America（NEC美国实验室）；Massachusetts Institute of Technology（麻省理工学院）；ANU（澳大利亚国立大学）；UT Arlington（德克萨斯大学阿灵顿分校）
Zhejiang University（浙江大学）；EPFL（洛桑联邦理工学院）；King’s College London（伦敦国王学院）；Alibaba Group（阿里巴巴集团）
Differentiable architecture search (DARTS) has been a mainstream direction in automatic machine learning. Since the discovery that original DARTS will inevitably converge to poor architectures, recent works alleviate this by either designing rule-based architecture selection techniques or incorporating complex regularization techniques, abandoning the simplicity of the original DARTS that selects architectures based on the largest parametric value, namely $α$. Moreover, we find that all the previous attempts only rely on classification labels, hence learning only single modal information and l…
SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)（多模态人工智能系统国家重点实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
Paul C. Lauterbur Research Center for Biomedical Imaging（保罗·C·劳特伯生物医学成像研究中心）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Pengcheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Harvard University（哈佛大学）；Shanghai AI Lab（上海人工智能实验室）
While nowadays deep neural networks achieve impressive performances on semantic segmentation tasks, they are usually trained by optimizing pixel-wise losses such as cross-entropy. As a result, the predictions outputted by such networks usually struggle to accurately capture the object boundaries and exhibit holes inside the objects. In this paper, we propose a novel approach to improve the structure of the predicted segmentation masks. We introduce a novel semantic edge detection network, which allows to match the predicted and ground truth segmentation masks. This Semantic Edge-Aware strategy…
MRIFE: A Mask-Recovering and Interactive-Feature-Enhancing Semantic Segmentation Network For Relic Landslide Detection
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Institute of Technology（北京理工大学）；China Aero Geophysical Survey and Remote Sensing Center for Natural Resources（中国自然资源航空物探遥感中心）；La Trobe University（拉筹伯大学）
College of Information Sciences and Technology, Donghua University（东华大学信息科学与技术学院）；Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University（东华大学数字化纺织服装技术教育部工程研究中心）
Change detection is a fundamental task in computer vision. Despite significant advances have been made, most of the change detection methods fail to work well in challenging scenes due to ubiquitous noise and interferences. Nowadays, post-processing methods (e.g. MRF, and CRF) aiming to enhance the binary change detection results still fall short of the requirements on universality for distinctive scenes, applicability for different types of detection methods, accuracy, and real-time performance. Inspired by the nature of image filtering, which separates noise from pixel observations and recov…
Energy-Regularized Spatial Masking: A Novel Approach to Enhancing Robustness and Interpretability in Vision Models
DAVID Lab, UVSQ, Paris-Saclay University（DAVID实验室，UVSQ，巴黎-萨克雷大学）；LIPN, UMR CNRS 7030, Sorbonne Paris Nord University（LIPN，UMR CNRS 7030，索邦巴黎北大学）；LISN, Paris-Saclay University（LISN，巴黎-萨克雷大学）
SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting
J. Crayton Pruitt Family Department of Biomedical Engineering University of Florida；Department of Electrical \& Computer Engineering University of Florida
Mask-RadarNet: Enhancing Transformer With Spatial-Temporal Semantic Context for Radar Object Detection in Autonomous Driving
University of Science and Technology of China（中国科学技术大学）；Wuhan Electronic Information Institute（武汉电子信息研究所）；Università degli Studi “Niccolò Cusano”（尼科洛·库萨诺大学）
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）
Enhancing Object Discovery for Unsupervised Instance Segmentation and Object Detection
College of Computer Science and Artificial Intelligence, Wenzhou University（计算机科学与人工智能学院，温州大学）；Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University（瓯江实验室（浙江省再生医学、视觉与脑健康实验室），温州医科大学眼医院）
MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models
Microsoft Health Futures（微软健康未来）；Microsoft Research India（微软研究院印度分院）；University of Cambridge（剑桥大学）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院国民保健信托基金）
Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China（中国科学技术大学，教育部脑启发智能感知与认知重点实验室）；Independent Researcher（独立研究员）
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区生物医学工程学院）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；Inselspital, University of Bern（伯尔尼大学因塞尔医院）；Technical University of Munich（慕尼黑工业大学）
Enhancing Vision-Language Model with Unmasked Token Alignment
Institute for AI Industry Research (AIR)（人工智能产业研究院）；Tsinghua University（清华大学）；Sensetime Research（商汤科技研究院）；CPII under InnoHK（InnoHK下属CPII）；Shanghai AI Laboratory（上海人工智能实验室）
No Masks Needed: Explainable AI for Deriving Segmentation from Classification
Dept. Electrical and Electronic Engineering, Imperial College London（帝国理工学院伦敦分校电子与电气工程系）；Centre for Vision, Speech and Signal Processing, University of Surrey（萨里大学视觉、语音与信号处理中心）
Segmenting salient objects in an image is an important vision task with ubiquitous applications. The problem becomes more challenging in the presence of a cluttered and textured background, low resolution and/or low contrast images. Even though existing algorithms perform well in segmenting most of the object(s) of interest, they often end up segmenting false positives due to resembling salient objects in the background. In this work, we tackle this problem by iteratively attending to image patches in a recurrent fashion and subsequently enhancing the predicted segmentation mask. Saliency feat…
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Lensless imaging can provide visual privacy due to the highly multiplexed characteristic of its measurements. However, this alone is a weak form of security, as various adversarial attacks can be designed to invert the one-to-many scene mapping of such cameras. In this work, we enhance the privacy provided by lensless imaging by (1) downsampling at the sensor and (2) using a programmable mask with variable patterns as our optical encoder. We build a prototype from a low-cost LCD and Raspberry Pi components, for a total cost of around 100 USD. This very low price point allows our system to be d…
WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
Hainan University, China（海南大学）；Huazhong University of Science and Technology, China（华中科技大学）；Shenzhen University, China（深圳大学）；Lingnan University, Hong Kong（岭南大学）
SARD: Segmentation-Aware Anomaly Synthesis via Region-Constrained Diffusion with Discriminative Mask Guidance
Global Institute of Future Technology（未来技术全球研究所）；Shanghai Jiao Tong University（上海交通大学）；Department of Intelligent Manufacturing（智能制造系）；Contemporary Amperex Technology Co.,Ltd（当代电子技术有限公司）
Hangzhou Dianzi University（杭州电子科技大学）；Alibaba Group（阿里巴巴集团）；Hefei University of Technology（合肥工业大学）
Vision-and-language pretraining (VLP) aims to learn generic multimodal representations from massive image-text pairs. While various successful attempts have been proposed, learning fine-grained semantic alignments between image-text pairs plays a key role in their approaches. Nevertheless, most existing VLP approaches have not fully utilized the intrinsic knowledge within the image-text pairs, which limits the effectiveness of the learned alignments and further restricts the performance of their models. To this end, we introduce a new VLP method called ROSITA, which integrates the cross- and i…
