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Manipulation Detection in Satellite Images Using vision Transformer

Manipulation Detection in Satellite Images Using Vision Tran...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Horvath, Janos Baireddy, Sriram Hao, Hanxiang Montserrat, Daniel Mas Delp, Edward J. Purdue Univ Sch Elect & Comp Engn Video & Image Proc Lab VIPER W Lafayette IN 47907 USA

ISBN: (纸本)9781665448994

A growing number of commercial satellite companies provide easily accessible satellite imagery. Overhead imagery is used by numerous industries including agriculture, forestry, natural disaster analysis, and meteorology. Satellite images, just as any other images, can be tampered with image manipulation tools. Manipulation detection methods created for images captured by "consumer cameras" tend to fail when used on satellite images due to the differences in image sensors, image acquisition, and processing. In this paper we propose an unsupervised technique that uses a vision Transformer to detect spliced areas within satellite images. We introduce a new dataset which includes manipulated satellite images that contain spliced objects. We show that our proposed approach performs better than existing unsupervised splicing detection techniques.

关键词： Image sensors computer vision Satellites Splicing conferences Forestry Tools

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Glance and Gaze: Inferring Action-aware Points for One-Stage Human-Object Interaction Detection

Glance and Gaze: Inferring Action-aware Points for One-Stage...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhong, Xubin Qu, Xian Ding, Changxing Tao, Dacheng South China Univ Technol Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Modern human-object interaction (HOI) detection approaches can be divided into one-stage methods and two-stage ones. One-stage models are more efficient due to their straightforward architectures, but the two-stage models are still advantageous in accuracy. Existing one-stage models usually begin by detecting predefined interaction areas or points, and then attend to these areas only for interaction prediction;therefore, they lack reasoning steps that dynamically search for discriminative cues. In this paper, we propose a novel one-stage method, namely Glance and Gaze Network (GGNet), which adaptively models a set of action-aware points (ActPoints) via glance and gaze steps. The glance step quickly determines whether each pixel in the feature maps is an interaction point. The gaze step leverages feature maps produced by the glance step to adaptively infer ActPoints around each pixel in a progressive manner. Features of the refined ActPoints are aggregated for interaction prediction. Moreover, we design an action-aware approach that effectively matches each detected interaction with its associated human-object pair, along with a novel hard negative attentive loss to improve the optimization of GGNet. All the above operations are conducted simultaneously and efficiently for all pixels in the feature maps. Finally, GGNet outperforms state-of-the-art methods by significant margins on both V-COCO and HICO-DET benchmarks. Code of GGNet is available at https://***/SherlockHolmes221/GGNet.

关键词： Adaptation models Visualization computer vision Codes Color Predictive models Feature extraction

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Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)

Confronting Ambiguity in 6D Object Pose Estimation via Score...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Tsu-Ching Hsiao Hao-Wei Chen Hsuan-Kung Yang Chun-Yi Lee Elsa Lab National Tsing Hua University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions. In response, we introduce a novel score-based diffusion method applied to the SE(3) group, marking the first application of diffusion models to SE(3) within the image domain, specifically tailored for pose estimation tasks. Extensive evaluations demonstrate the method's efficacy in handling pose ambiguity, mitigating perspective-induced ambiguity, and showcasing the robustness of our surrogate Stein score formulation on SE(3). This formulation not only improves the convergence of denoising process but also enhances computational efficiency. Thus, we pioneer a promising strategy for 6D object pose estimation.

关键词： computer vision Correlation Accuracy Pose estimation Noise reduction Diffusion models Robustness

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Bottom-Up Shift and Reasoning for Referring Image Segmentation

Bottom-Up Shift and Reasoning for Referring Image Segmentati...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Sibei Xia, Meng Li, Guanbin Zhou, Hong-Yu Yu, Yizhou ShanghaiTech Univ Shanghai Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Referring image segmentation aims to segment the referent that is the corresponding object or stuff referred by a natural language expression in an image. Its main challenge lies in how to effectively and efficiently differentiate between the referent and other objects of the same category as the referent. In this paper, we tackle the challenge by jointly performing compositional visual reasoning and accurate segmentation in a single stage via the proposed novel Bottom-Up Shift (BUS) and Bidirectional Attentive Refinement (BIAR) modules. Specifically, BUS progressively locates the referent along hierarchical reasoning steps implied by the expression. At each step, it locates the corresponding visual region by disambiguating between similar regions, where the disambiguation bases on the relationships between regions. By the explainable visual reasoning, BUS explicitly aligns linguistic components with visual regions so that it can identify all the mentioned entities in the expression. BIAR fuses multi-level features via a twoway attentive message passing, which captures the visual details relevant to the referent to refine segmentation results. Experimental results demonstrate that the proposed method consisting of BUS and BIAR modules, can not only consistently surpass all existing state-of-the-art algorithms across common benchmark datasets but also visualize interpretable reasoning steps for stepwise segmentation. Code is available at https://***/incredibleXM/BUSNet.

关键词： Location awareness Visualization Image segmentation computer vision Fuses Message passing Computational modeling

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Towards Accurate Text-based Image Captioning with Content Diversity Exploration

Towards Accurate Text-based Image Captioning with Content Di...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Xu, Guanghui Niu, Shuaicheng Tan, Mingkui Luo, Yucheng Du, Qing Wu, Qi South China Univ Technol Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Univ Adelaide Adelaide SA Australia Minist Educ Key Lab Big Data & Intelligent Robot Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Text-based image captioning (TextCap) which aims to read and reason images with texts is crucial for a machine to understand a detailed and complex scene environment, considering that texts are omnipresent in daily life. This task, however, is very challenging because an image often contains complex texts and visual information that is hard to be described comprehensively. Existing methods attempt to extend the traditional image captioning methods to solve this task, which focus on describing the overall scene of images by one global caption. This is infeasible because the complex text and visual information cannot be described well within one caption. To resolve this difficulty, we seek to generate multiple captions that accurately describe different parts of an image in detail. To achieve this purpose, there are three key challenges: 1) it is hard to decide which parts of the texts of images to copy or paraphrase;2) it is non-trivial to capture the complex relationship between diverse texts in an image;3) how to generate multiple captions with diverse content is still an open problem. To conquer these, we propose a novel Anchor-Captioner method. Specifically, we first find the important tokens which are supposed to be paid more attention to and consider them as anchors. Then, for each chosen anchor, we group its relevant texts to construct the corresponding anchor-centred graph (ACG). Last, based on different ACGs, we conduct the multi-view caption generation to improve the content diversity of generated captions. Experimental results show that our method not only achieves SOTA performance but also generates diverse captions to describe images.

关键词： Visualization computer vision Image resolution Benchmark testing pattern recognition Proposals Optical character recognition software

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How to Calibrate Your Event Camera

How to Calibrate Your Event Camera

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Muglikar, Manasi Gehrig, Mathias Gehrig, Daniel Scaramuzza, Davide Univ Zurich Dept Informat Zurich Switzerland Univ Zurich Dept Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

We propose a generic event camera calibration framework using image reconstruction. Instead of relying on blinking LED patterns or external screens, we show that neural-network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras. The advantage of our proposed approach is that we can use standard calibration patterns that do not rely on active illumination. Furthermore, our approach enables the possibility to perform extrinsic calibration between frame-based and event-based sensors without additional complexity. Both simulation and real-world experiments indicate that calibration through image reconstruction is accurate under common distortion models and a wide variety of distortion parameters.

关键词： Computational modeling Robot vision systems Cameras Distortion Calibration Sensors pattern recognition

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Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing

Probabilistic Sampling of Balanced K-Means using Adiabatic Q...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jan-Nico Zaech Martin Danelljan Tolga Birdal Luc Van Gool ETH Zurich INSAIT Sofia University Sofia Bulgaria ETH Zurich Imperial College London

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization prob-lems. Current AQCs allow to implement problems of re-search interest, which has sparked the development of quan-tum representations for many computer vision tasks. De-spite requiring multiple measurements from the noisy AQC, current approaches only utilize the best measurement, dis-carding information contained in the remaining ones. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little ad-ditional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.

关键词： computer vision Visualization Quantum computing Costs Current measurement Posterior probability Prototypes

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SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction

SGCN:Sparse Graph Convolution Network for Pedestrian Traject...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Shi, Liushuai Wang, Le Long, Chengjiang Zhou, Sanping Zhou, Mo Niu, Zhenxing Hua, Gang Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China JD Finance Amer Corp Mountain View CA USA Alibaba Grp Machine Intelligence Lab Hangzhou Peoples R China Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9781665445092

Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very challenging due to complex interactions between pedestrians. However, previous works based on dense undirected interaction suffer from modeling superfluous interactions and neglect of trajectory motion tendency, and thus inevitably result in a considerable deviance from the reality. To cope with these issues, we present a Sparse Graph Convolution Network (SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. Meanwhile, we use a sparse directed temporal graph to model the motion tendency, thus to facilitate the prediction based on the observed direction. Finally, parameters of a bi-Gaussian distribution for trajectory prediction are estimated by fusing the above two sparse graphs. We evaluate our proposed method on the ETH and UCY datasets, and the experimental results show our method outperforms comparative state-of-the-art methods by 9% in Average Displacement Error (ADE) and 13% in Final Displacement Error (FDE). Notably, visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.

关键词： Legged locomotion Adaptation models Visualization computer vision Convolution Predictive models Trajectory

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DAVE – A Detect-and-Verify Paradigm for Low-Shot Counting

DAVE – A Detect-and-Verify Paradigm for Low-Shot Counting

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conference on computer vision and pattern recognition (cvpr)

作者： Jer Pelhan Alan Lukežič Vitjan Zavrtanik Matej Kristan Faculty of Computer and Information Science University of Ljubljana Slovenia

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Low-shot counters estimate the number of objects corresponding to a selected category, based on only few or no exemplars annotated in the image. The current state-of-the-art estimates the total counts as the sum over the object location density map, but does not provide individual object locations and sizes, which are crucial for many applications. This is addressed by detection-based counters, which, however fall behind in the total count accuracy. Furthermore, both approaches tend to overestimate the counts in the presence of other object classes due to many false positives. We propose DAVE, a low-shot counter based on a detect-and-verify paradigm, that avoids the aforementioned issues by first generating a high-recall detection set and then verifying the detections to identify and remove the out-liers. This jointly increases the recall and precision, leading to accurate counts. DAVE outperforms the top density-based counters by ~20% in the total count MAE, it outper-forms the most recent detection-based counter by ~20% in detection quality and sets a new state-of-the-art in zero-shot as well as text-prompt-based counting. The code and models are available on GitHub.

关键词： computer vision Accuracy Codes Estimation Benchmark testing Human in the loop pattern recognition

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Adaptive Plasticity Improvement for Continual Learning

Adaptive Plasticity Improvement for Continual Learning

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conference on computer vision and pattern recognition (cvpr)

作者： Yan-Shuo Liang Wu-Jun Li Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University P. R. China

Many works have tried to solve the catastrophic forgetting (CF) problem in continual learning (lifelong learning). However, pursuing non-forgetting on old tasks may damage the model's plasticity for new tasks. Although some methods have been proposed to achieve stability-plasticity trade-off, no methods have considered evaluating a model's plasticity and improving plasticity adaptively for a new task. In this work, we propose a new method, called adaptive plasticity improvement (API), for continual learning. Besides the ability to overcome CF on old tasks, API also tries to evaluate the model's plasticity and then adaptively improve the model's plasticity for learning a new task if necessary. Experiments on several real datasets show that API can outperform other state-of-the-art baselines in terms of both accuracy and memory usage.

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