Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences（复杂系统管理与控制国家重点实验室，中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Mechanical Engineering and Automation, Beihang University（北京航空航天大学机械工程及自动化学院）
Xingyu Chen, Yue Lu, Zhengxing Wu, Junzhi Yu, Li Wen
Underwater robotic perception usually requires visual restoration and object detection, both of which have been studied for many years. Meanwhile, data domain has a huge impact on modern data-driven leaning process. However, exactly indicating domain effect, the relation between restoration and detection remains unclear. In this paper, we generally investigate the relation of quality-diverse data domain to detection performance. In the meantime, we unveil how visual restoration contributes to object detection in real-world underwater scenes. According to our analysis, five key discoveries are reported: 1) Domain quality has an ignorable effect on within-domain convolutional representation and detection accuracy; 2) low-quality domain leads to higher generalization ability in cross-domain detection; 3) low-quality domain can hardly be well learned in a domain-mixed learning process; 4) degrading recall efficiency, restoration cannot improve within-domain detection accuracy; 5) visual restoration is beneficial to detection in the wild by reducing the domain shift between training data and real-world scenes. Finally, as an illustrative example, we successfully perform underwater object detection with an aquatic robot.
SeePerSea: Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles
Department of Computer Science, Dartmouth College（达特茅斯学院计算机科学系）；IQT Labs（IQT实验室）；Department of Computer Science, Columbia University（哥伦比亚大学计算机科学系）；Department of Computer Science, University of Maryland College Park（马里兰大学计算机科学系）；The Institute for Human and Machine Cognition and The University of West Florida（人机认知研究所与西佛罗里达大学）；School of Computing, Binghamton University（宾夕法尼亚州立大学计算学院）
Underwater360: Reconstructing Underwater Scenes from Panoramic Images with Omnidirectional Gaussian Splatting
School of Software, Dalian University of Technology（大连理工大学软件学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；SenseTime Research（商汤科技研究院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? …
The University of Sydney（悉尼大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；SenseTime Research（商汤科技研究院）；Shanghai Jiao Tong University（上海交通大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）
Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent perceptual IR algorithms based on generative adversarial networks (GANs) have brought in significant improvement on visual performance, but also pose great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. We present two questions: Can existing IQA methods objectively evaluate recent IR algorithms? With the focus on beating current benchmarks, are we getting better IR algorith…
Beijing National Research Center for Information Science and Technology（北京国家信息科学与技术研究中心）；Institute for Brain and Cognitive Sciences, Tsinghua University（清华大学脑与认知科学研究院）；Tsinghua University（清华大学）
Imaging and perception in photon-limited scenarios is necessary for various applications, e.g., night surveillance or photography, high-speed photography, and autonomous driving. In these cases, cameras suffer from low signal-to-noise ratio, which degrades the image quality severely and poses challenges for downstream high-level vision tasks like object detection and recognition. Data-driven methods have achieved enormous success in both image restoration and high-level vision tasks. However, the lack of high-quality benchmark dataset with task-specific accurate annotations for photon-limited …
National Taiwan University（台湾大学）；Mobile Drive Technology（智驾科技）；Qualcomm Inc.（高通公司）
Object detection in low-light conditions remains a challenging but important problem with many practical implications. Some recent works show that, in low-light conditions, object detectors using raw image data are more robust than detectors using image data processed by a traditional ISP pipeline. To improve detection performance in low-light conditions, one can fine-tune the detector to use raw image data or use a dedicated low-light neural pipeline trained with paired low- and normal-light data to restore and enhance the image. However, different camera sensors have different spectral sensi…
Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation
Laboratory of IEMN, CNRS, Centrale Lille, UMR 8520, Univ. Polytechnique Hauts-de-France（伊姆纳实验室，国家科学研究中心，里尔中央理工大学，UMR 8520，法国高等技术大学）；Khalifa University（卡利法大学）；School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；Sorbonne Center for Artificial Intelligence, Sorbonne University Abu Dhabi（索邦人工智能中心，索邦大学阿布扎克分校）
