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Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration

Dual Pixel Exploration: Simultaneous Depth Estimation and Im...

引用

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

作者： Pan, Liyuan Chowdhury, Shah Hartley, Richard Liu, Miaomiao Zhang, Hongguang Li, Hongdong Australian Natl Univ Canberra ACT Australia CSIRO A&F Canberra ACT Australia CSIRO Data61 Canberra ACT Australia SE Inst AMS Amsterdam Netherlands

ISBN: (纸本)9781665445092

The dual-pixel (DP) hardware works by splitting each pixel in half and creating an image pair in a single snapshot. Several works estimate depth/inverse depth by treating the DP pair as a stereo pair. However, dual-pixel disparity only occurs in image regions with the defocus blur. The heavy defocus blur in DP pairs affects the performance of matching-based depth estimation approaches. Instead of removing the blur effect blindly, we study the formation of the DP pair which links the blur and the depth information. In this paper, we propose a mathematical DP model which can benefit depth estimation by the blur. These explorations motivate us to propose an end-to-end DDDNet (DP-based Depth and Deblur Network) to jointly estimate the depth and restore the image. Moreover, we define a reblur loss, which reflects the relationship of the DP image formation process with depth information, to regularise our depth estimate in training. To meet the requirement of a large amount of data for learning, we propose the first DP image simulator which allows us to create datasets with DP pairs from any existing RGBD dataset. As a side contribution, we collect a real dataset for further research. Extensive experimental evaluation on both synthetic and real datasets shows that our approach achieves competitive performance compared to state-of-the-art approaches.

关键词： Training computer vision Computational modeling Buildings Estimation Cameras Mathematical models

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DeFLOCNet: Deep Image Editing via Flexible Low-level Controls

DeFLOCNet: Deep Image Editing via Flexible Low-level Control...

引用

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

作者： Liu, Hongyu Wan, Ziyu Huang, Wei Song, Yibing Han, Xintong Liao, Jing Jiang, Bin Liu, Wei Huya Inc Guangzhou Peoples R China City Univ Hong Kong Hong Kong Peoples R China Hunan Univ Changsha Peoples R China Tencent AI Lab Shenzhen Peoples R China Tencent Data Platform Shenzhen Peoples R China

ISBN: (纸本)9781665445092

User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse low-level inputs, which typically consist of sparse sketch lines and color dots, convey user intentions for content creation (i.e., free-form editing). While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content. In this paper, we propose DeFLOCNet which relies on a deep encoder-decoder CNN to retain the guidance of these controls in the deep feature representations. In each skip-connection layer, we design a structure generation block. Instead of attaching low-level controls to an input image, we inject these controls directly into each structure generation block for sketch line refinement and color propagation in the CNN feature space. We then concatenate the modulated features with the original decoder features for structure generation. Meanwhile, DeFLOCNet involves another decoder branch for texture generation and detail enhancement. Both structures and textures are rendered in the decoder, leading to user-intended editing results. Experiments on benchmarks demonstrate that DeFLOCNet effectively transforms different user intentions to create visually pleasing content.

关键词： Visualization computer vision Image color analysis Transforms Benchmark testing Aerospace electronics Decoding

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Multi-Target Domain Adaptation with Collaborative Consistency Learning

Multi-Target Domain Adaptation with Collaborative Consistenc...

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

作者： Isobe, Takashi Jia, Xu Chen, Shuaijun He, Jianzhong Shi, Yongjie Liu, Jianzhuang Lu, Huchuan Wang, Shengjin Tsinghua Univ Dept Elect Engn Beijing Peoples R China Dalian Univ Technol Dalian Peoples R China Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Peking Univ Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extended to multiple target domains. In this work, we propose a collaborative learning framework to achieve unsupervised multi-target domain adaptation. An unsupervised domain adaptation expert model is first trained for each source-target pair and is further encouraged to collaborate with each other through a bridge built between different target domains. These expert models are further improved by adding the regularization of making the consistent pixel-wise prediction for each sample with the same structured context. To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights. Extensive experiments demonstrate that the proposed method can effectively exploit rich structured information contained in both labeled source domain and multiple unlabeled target domains. Not only does it perform well across multiple target domains but also performs favorably against state-of-the-art unsupervised domain adaptation methods specially trained on a single source-target pair. Code is available at https://***/junpan19/MTDA.

关键词： Adaptation models Image segmentation computer vision Computational modeling Semantics Collaboration Predictive models

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Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching

Hybrid Functional Maps for Crease-Aware Non-Isometric Shape ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Lennart Bastian Yizheng Xie Nassir Navab Zorah Lähner Technical University of Munich Germany University of Siegen Germany University of Bonn Germany Lamarr Institute Germany

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Non-isometric shape correspondence remains a fundamental challenge in computer vision. Traditional methods using Laplace-Beltrami operator (LBO) eigenmodes face limitations in characterizing high-frequency extrinsic shape changes like bending and creases. We propose a novel approach of combining the non-orthogonal extrinsic basis of eigenfunctions of the elastic thin-shell hessian with the intrinsic ones of the LBO, creating a hybrid spectral space in which we construct functional maps. To this end, we present a theoretical framework to effectively integrate non-orthogonal basis functions into descriptor- and learning-based functional map methods. Our approach can be in-corporated easily into existing functional map pipelines across varying applications and can handle complex de-formations beyond isometries. We show extensive evaluations across various supervised and unsupervised settings and demonstrate significant improvements. Notably, our approach achieves up to 15% better mean geodesic error for non-isometric correspondence settings and up to 45% improvement in scenarios with topological noise. Code is available at: https://***/

关键词： computer vision Codes Shape Face recognition Pipelines Noise Bending

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Learning to Fuse Asymmetric Feature Maps in Siamese Trackers

Learning to Fuse Asymmetric Feature Maps in Siamese Trackers

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

作者： Han, Wencheng Dong, Xingping Khan, Fahad Shahbaz Shao, Ling Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCorr) to obtain multi-channel correlation information from the two feature maps (target and search region). However, DW-XCorr has several limitations within Siamese-based tracking: it can easily be fooled by distractors, has fewer activated channels and provides weak discrimination of object boundaries. Further, DW-XCorr is a handcrafted parameter-free module and cannot fully benefit from offline learning on large-scale data. We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on largescale data. Different from DW-XCorr and its predecessor (XCorr), which regard a single feature map as the convolution kernel, our ACM decomposes the convolution operation on a concatenated feature map into two mathematically equivalent operations, thereby avoiding the need for the feature maps to be of the same size (width and height) during concatenation. Our ACM can incorporate useful prior information, such as bounding-box size, with standard visual features. Furthermore, ACM can easily be integrated into existing Siamese trackers based on DW-XCorr or XCorr. To demonstrate its generalization ability, we integrate ACM into three representative trackers: SiamFC, SiamRPN++ and SiamBAN. Our experiments reveal the benefits of the proposed ACM, which outperforms existing methods on six tracking benchmarks. On the LaSOT test set, our ACM-based tracker obtains a significant improvement of 5.8% in terms of success (AUC), over the baseline.

关键词： Training Visualization Target tracking Correlation Convolution Fuses pattern recognition

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Manipulation Detection in Satellite Images Using vision Transformer

Manipulation Detection in Satellite Images Using Vision Tran...

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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...

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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...

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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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