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Toward Accurate and Realistic Outfits Visualization with Attention to Details

Toward Accurate and Realistic Outfits Visualization with Att...

引用

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

作者： Li, Kedan Chong, Min Jin Zhang, Jeffrey Liu, Jingen Univ Illinois Urbana IL 61801 USA Revery AI Inc Emeryville CA 94608 USA JD AI Res Mountain View CA USA

ISBN: (纸本)9781665445092

Virtual try-on methods aim to generate images of fashion models wearing arbitrary combinations of garments. This is a challenging task because the generated image must appear realistic and accurately display the interaction between garments. Prior works produce images that are filled with artifacts and fail to capture important visual details necessary for commercial applications. We propose Outfit Visualization Net (OVNet) to capture these important details (e.g. buttons, shading, textures, realistic hemlines, and interactions between garments) and produce high quality multiple-garment virtual try-on images. OVNet consists of 1) a semantic layout generator and 2) an image generation pipeline using multiple coordinated warps. We train the warper to output multiple warps using a cascade loss, which refines each successive warp to focus on poorly generated regions of a previous warp and yields consistent improvements in detail. In addition, we introduce a method for matching outfits with the most suitable model and produce significant improvements for both our and other previous try-on methods. Through quantitative and qualitative analysis, we demonstrate our method generates substantially higher-quality studio images compared to prior works for multi-garment outfits. An interactive interface powered by this method has been deployed on fashion e-commerce websites and received overwhelmingly positive feedback.

关键词： Visualization computer vision Image synthesis Clothing Semantics Pipelines Layout

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BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View recognition via Perspective Supervision

BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-...

引用

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

作者： Yang, Chenyu Chen, Yuntao Tian, Hao Tao, Chenxin Zhu, Xizhou Zhang, Zhaoxiang Huang, Gao Li, Hongyang Qiao, Yu Lu, Lewei Zhou, Jie Dai, Jifeng Tsinghua Univ Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China SenseTime Res Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-the-art BEV detectors are often tied to certain depth pre-trained backbones like VoVNet, hindering the synergy between booming image backbones and BEV detectors. To address this limitation, we prioritize easing the optimization of BEV detectors by introducing perspective view supervision. To this end, we propose a two-stage BEV detector, where proposals from the perspective head are fed into the bird's-eye-view head for final predictions. To evaluate the effectiveness of our model, we conduct extensive ablation studies focusing on the form of supervision and the generality of the proposed detector. The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset. The code shall be released soon.

关键词： detection recognition: Categorization retrieval

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Texture-based Error Analysis for Image Super-Resolution

Texture-based Error Analysis for Image Super-Resolution

引用

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

作者： Magid, Salma Abdel Lin, Zudi Wei, Donglai Zhang, Yulun Gu, Jinjin Pfister, Hanspeter Harvard Univ Cambridge MA 02138 USA Boston Coll Chestnut Hill MA 02167 USA Swiss Fed Inst Technol Zurich Switzerland Univ Sydney Sydney NSW Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Evaluation practices for image super-resolution (SR) use a single-value metric, the PSNR or SSIM, to determine model performance. This provides little insight into the source of errors and model behavior. Therefore, it is beneficial to move beyond the conventional approach and reconceptualize evaluation with interpretability as our main priority. We focus on a thorough error analysis from a variety of perspectives. Our key contribution is to leverage a texture classifier, which enables us to assign patches with semantic labels, to identify the source of SR errors both globally and locally. We then use this to determine (a) the semantic alignment of SR datasets, (b) how SR models perform on each label, (c) to what extent high-resolution (HR) and SR patches semantically correspond, and more. Through these different angles, we are able to highlight potential pitfalls and blindspots. Our overall investigation highlights numerous unexpected insights. We hope this work serves as an initial step for debugging blackbox SR networks.

关键词： Measurement Error analysis Computational modeling Biological system modeling Superresolution Semantics pattern recognition

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Distilling Object Detectors via Decoupled Features

Distilling Object Detectors via Decoupled Features

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

作者： Guo, Jianyuan Han, Kai Wang, Yunhe Wu, Han Chen, Xinghao Xu, Chunjing Xu, Chang Huawei Technol Noahs Ark Lab Beijing Peoples R China Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781665445092

Knowledge distillation is a widely used paradigm for inheriting information from a complicated teacher network to a compact student network and maintaining the strong performance. Different from image classification, object detectors are much more sophisticated with multiple loss functions in which features that semantic information rely on are tangled. In this paper, we point out that the information of features derived from regions excluding objects are also essential for distilling the student detector, which is usually ignored in existing approaches. In addition, we elucidate that features from different regions should be assigned with different importance during distillation. To this end, we present a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector. Specifically, two levels of decoupled features will be processed for embedding useful information into the student, i.e., decoupled features from neck and decoupled proposals from classification head. Extensive experiments on various detectors with different backbones show that the proposed DeFeat is able to surpass the state-of-the-art distillation methods for object detection. For example, DeFeat improves ResNet50 based Faster R-CNN from 37.4% to 40.9% mAP, and improves ResNet50 based RetinaNet from 36.5% to 39.7% mAP on COCO benchmark. Code will be released(1,2).

关键词： Knowledge engineering Semantics Detectors Object detection Feature extraction pattern recognition Neck

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Object classification from randomized EEG trials

Object classification from randomized EEG trials

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

作者： Ahmed, Hamad Wilbur, Ronnie B. Bharadwaj, Hari M. Siskind, Jeffrey Mark Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781665445092

New results suggest strong limits to the feasibility of object classification from human brain activity evoked by image stimuli, as measured through EEG. Considerable prior work suffers from a confound between the stimulus class and the time since the start of the experiment. A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments. Here, we attempt object classification from EEG using an array of methods that are representative of the state-of-the-art, with a far larger (20x) dataset of randomized EEG trials, 1,000 stimulus presentations of each of forty classes, all from a single subject. To our knowledge, this is the largest such EEG data-collection effort from a single subject and is at the bounds of feasibility. We obtain classification accuracy that is marginally above chance and above chance in a statistically significant fashion, and further assess how accuracy depends on the classifier used, the amount of training data used, and the number of classes. Reaching the limits of data collection with only marginally above-chance performance suggests that the prevailing literature substantially exaggerates the feasibility of object classification from EEG.

关键词： Support vector machines Training Recurrent neural networks Training data Electroencephalography pattern recognition Convolutional neural networks

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Inferring temporal order of images from 3D structure

Inferring temporal order of images from 3D structure

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ieee conference on computer vision and pattern recognition

作者： Schindler, Grant Dellaert, Frank Kang, Sing Bing Georgia Inst Technol Atlanta GA 30332 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781424411795

In this paper, we describe a technique to temporally sort a collection of photos that span many years. By reasoning about persistence of visible structures, we show how this sorting task can be formulated as a constraint satisfaction problem (CSP). Casting this problem as a CSP allows us to efficiently find a suitable ordering of the images despite the large size of the solution space (factorial in the number of images) and the presence of occlusions. We present experimental resultsfor photographs of a city acquired over a one hundred year period.

关键词： Three dimensional computer graphics

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Fast Solvers for Minimal Radial Distortion Relative Pose Problems

Fast Solvers for Minimal Radial Distortion Relative Pose Pro...

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

作者： Oskarsson, Magnus Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9781665448994

In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.

关键词： Geometry computer vision conferences Estimation Tools Distortion Cameras

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Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation 30

Noisy Softmax: Improving the Generalization Ability of DCNN ...

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Binghui Deng, Weihong Du, Junping Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

Over the past few years, softmax and SGD have become a commonly used component and the default training strategy in CNN frameworks, respectively. However, when optimizing CNNs with SGD, the saturation behavior behind softmax always gives us an illusion of training well and then is omitted. In this paper, we first emphasize that the early saturation behavior of softmax will impede the exploration of SGD, which sometimes is a reason for model converging at a bad local-minima, then propose Noisy Softmax to mitigating this early saturation issue by injecting annealed noise in softmax during each iteration. This operation based on noise injection aims at postponing the early saturation and further bringing continuous gradients propagation so as to significantly encourage SGD solver to be more exploratory and help to find a better local-minima. This paper empirically verifies the superiority of the early softmax desaturation, and our method indeed improves the generalization ability of CNN model by regularization. We experimentally find that this early desaturation helps optimization in many tasks, yielding state-of-the-art or competitive results on several popular benchmark datasets.

关键词： Training Noise measurement Annealing Standards Robustness computer vision Telecommunications

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Pushing it out of the Way: Interactive Visual Navigation

Pushing it out of the Way: Interactive Visual Navigation

引用

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

作者： Zeng, Kuo-Hao Weihs, Luca Farhadi, Ali Mottaghi, Roozbeh Univ Washington Paul G Allen Sch Comp Sci & Engn Seattle WA 98195 USA Prior Allen Inst AI Seattle WA USA

ISBN: (纸本)9781665445092

We have observed significant progress in visual navigation for embodied agents. A common assumption in studying visual navigation is that the environments are static;this is a limiting assumption. Intelligent navigation may involve interacting with the environment beyond just moving forward/backward and turning left/right. Sometimes, the best way to navigate is to push something out of the way. In this paper, we study the problem of interactive navigation where agents learn to change the environment to navigate more efficiently to their goals. To this end, we introduce the Neural Interaction Engine (NIE) to explicitly predict the change in the environment caused by the agent's actions. By modeling the changes while planning, we find that agents exhibit significant improvements in their navigational capabilities. More specifically, we consider two downstream tasks in the physics-enabled, visually rich, AI2-THOR environment: (1) reaching a target while the path to the target is blocked (2) moving an object to a target location by pushing it. For both tasks, agents equipped with an NIE significantly outperform agents without the understanding of the effect of the actions indicating the benefits of our approach. The code and dataset are available at github. com/ KuoHaoZeng/ Interactive_Visual_Navigation.

关键词： Visualization computer vision Limiting Navigation Predictive models Turning Planning

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Self Texture Transfer Networks for Low Bitrate Image Compression

Self Texture Transfer Networks for Low Bitrate Image Compres...

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

作者： Iwai, Shoma Miyazaki, Tomo Sugaya, Yoshihiro Omachi, Shinichiro Tohoku Univ Grad Sch Engn Dept Commun Sendai Miyagi Japan

ISBN: (纸本)9781665448994

Lossy image compression causes a loss of texture, especially at low bitrate. To mitigate this problem, we propose a novel image compression method that utilizes a reference-based image super-resolution model. We use two image compression models and a self texture transfer model. The image compression models encode and decode a whole input image and selected reference patches. The reference patches are small but compressed with high quality. The self texture transfer model transfers the texture of reference patches into similar regions in the compressed image. The experimental results show that our method can reconstruct accurate texture by transferring the texture of reference patches.

关键词： computer vision Image coding conferences Bit rate Superresolution pattern recognition Image reconstruction

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