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Neural Congealing: Aligning Images to a Joint Semantic Atlas

Neural Congealing: Aligning Images to a Joint Semantic Atlas

引用

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

作者： Ofri-Amar, Dolev Geyer, Michal Kasten, Yoni Dekel, Tali Weizmann Inst Sci Rehovot Israel NVIDIA Res Tel Aviv Israel

ISBN: (纸本)9798350301298

We present Neural Congealing - a zero-shot self-supervised framework for detecting and jointly aligning semantically-common content across a given set of images. Our approach harnesses the power of pre-trained DINO-ViT features to learn: (i) a joint semantic atlas - a 2D grid that captures the mode of DINO-ViT features in the input set, and (ii) dense mappings from the unified atlas to each of the input images. We derive a new robust self-supervised framework that optimizes the atlas representation and mappings per image set, requiring only a few real-world images as input without any additional input information (e.g., segmentation masks). Notably, we design our losses and training paradigm to account only for the shared content under severe variations in appearance, pose, background clutter or other distracting objects. We demonstrate results on a plethora of challenging image sets including sets of mixed domains (e.g., aligning images depicting sculpture and artwork of cats), sets depicting related yet different object categories (e.g., dogs and tigers), or domains for which large-scale training data is scarce (e.g., coffee mugs). We thoroughly evaluate our method and show that our test-time optimization approach performs favorably compared to a state-of-the-art method that requires extensive training on large-scale datasets. Project webpage: https://***/

关键词： vision + graphics

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Restore from Restored: Video Restoration with Pseudo Clean Video

Restore from Restored: Video Restoration with Pseudo Clean V...

引用

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

作者： Lee, Seunghwan Cho, Donghyeon Kim, Jiwon Kim, Tae Hyun Hanyang Univ Dept Comp Sci Seoul South Korea Chungnam Natl Univ Dept Elect Engn Daejeon South Korea SKT Vis AI Labs T Brain Seoul South Korea

ISBN: (纸本)9781665445092

In this study, we propose a self-supervised video denoising method called "restore-from-restored." This method fine-tunes a pre-trained network by using a pseudo clean video during the test phase. The pseudo clean video is obtained by applying a noisy video to the baseline network. By adopting a fully convolutional neural network (FCN) as the baseline, we can improve video denoising performance without accurate optical flow estimation and registration steps, in contrast to many conventional video restoration methods, due to the translation equivariant property of the FCN. Specifically, the proposed method can take advantage of plentiful similar patches existing across multiple consecutive frames (i.e., patch-recurrence);these patches can boost the performance of the baseline network by a large margin. We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames. In our experiments, we apply the proposed method to state-of-the-art denoisers and show that our fine-tuned networks achieve a considerable improvement in denoising performance.

关键词： Training computer vision Noise reduction Estimation Image restoration pattern recognition Noise measurement

来源：评论

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Face recognition: Too Bias, or Not Too Bias?

Face Recognition: Too Bias, or Not Too Bias?

引用

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

作者： Robinson, Joseph P. Livitz, Gennady Henon, Yann Qin, Can Fu, Yun Timoner, Samson Northeastern Univ Boston MA 02115 USA ISM Connect Doylestown PA USA

ISBN: (纸本)9781728193601

We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR) systems using a novel Balanced Faces In the Wild (BFW) dataset: data balanced for gender and ethnic groups. We show variations in the optimal scoring threshold for face-pairs across different subgroups. Thus, the conventional approach of learning a global threshold for all pairs results in performance gaps between subgroups. By learning subgroup-specific thresholds, we reduce performance gaps, and also show a notable boost in overall performance. Furthermore, we do a human evaluation to measure bias in humans, which supports the hypothesis that an analogous bias exists in human perception. For the BFW database, source code, and more, visit https://***/visionjo/facerec-bias-bfw.

关键词： Face recognition

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On partial least squares in head pose estimation: How to simultaneously deal with misalignment

On partial least squares in head pose estimation: How to sim...

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2012 ieee conference on computer vision and pattern recognition, cvpr 2012

作者： Haj, Murad Al Gonzalez, Jordi Davis, Larry S. Centre de Visió per Computador Universitat Autònoma de Barcelona Bellaterra Barcelona 08193 Spain Institute for Advanced Computer Studies University of Maryland College Park MD 20742 United States

ISBN: (纸本)9781467312264

Head pose estimation is a critical problem in many computer vision applications. These include human computer interaction, video surveillance, face and expression recognition. In most prior work on heads pose estimation, the positions of the faces on which the pose is to be estimated are specified manually. Therefore, the results are reported without studying the effect of misalignment. We propose a method based on partial least squares (PLS) regression to estimate pose and solve the alignment problem simultaneously. The contributions of this paper are two-fold: 1) we show that the kernel version of PLS (kPLS) achieves better than state-of-the-art results on the estimation problem and 2) we develop a technique to reduce misalignment based on the learned PLS factors. © 2012 ieee.

关键词： Human computer interaction

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OCSampler: Compressing Videos to One Clip with Single-step Sampling

OCSampler: Compressing Videos to One Clip with Single-step S...

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

作者： Lin, Jintao Duan, Haodong Chen, Kai Lin, Dahua Wang, Limin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Videos incorporate rich semantics as well as redundant information. Seeking a compact yet effective video representation, e.g., sample informative frames from the entire video, is critical to efficient video recognition. There have been works that formulate frame sampling as a sequential decision task by selecting frames one by one according to their importance. In this paper, we present a more efficient framework named OCSampler, which explores such a representation with one short clip. OCSampler designs a new paradigm of learning instance-specific video condensation policies to select frames only in a single step. Rather than picking up frames sequentially like previous methods, we simply process a whole sequence at once. Accordingly, these policies are derived from a light-weighted skim network together with a simple yet effective policy network. Moreover, we extend the proposed method with a frame number budget, enabling the framework to produce correct predictions in high confidence with as few frames as possible. Experiments on various benchmarks demonstrate the effectiveness of OCSampler over previous methods in terms of accuracy and efficiency. Specifically, it achieves 76.9% mAP and 21.7 GFLOPs on ActivityNet with an impressive throughput: 123.9 Video/s on a single TITAN Xp GPU.

关键词： computer vision Semantics Graphics processing units Benchmark testing Throughput Sampling methods pattern recognition

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Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis

Towards Unsupervised Learning of Generative Models for 3D Co...

引用

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

作者： Liao, Yiyi Schwarz, Katja Mescheder, Lars Geiger, Andreas Max Planck Inst Intelligent Syst Tubingen Germany Univ Tubingen Tubingen Germany Amazon Tubingen Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task.

关键词： Three-dimensional displays Two dimensional displays Solid modeling Image generation Rendering (computer graphics) Generators Task analysis

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MUREL: Multimodal Relational Reasoning for Visual Question Answering 32

MUREL: Multimodal Relational Reasoning for Visual Question A...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cadene, Remi Ben-younes, Hedi Cord, Matthieu Thome, Nicolas Sorbonne Univ CNRS LIP6 4 Pl Jussieu F-75005 Paris France Heuritech 110 Ave Republ F-75011 Paris France Conservatoire Natl Arts & Metiers F-75003 Paris France

ISBN: (纸本)9781728132938

Multimodal attentional networks are currently state-of-the-art models for Visual Question Answering (VQA) tasks involving real images. Although attention allows to focus on the visual content relevant to the question, this simple mechanism is arguably insufficient to model complex reasoning features required for VQA or other high-level tasks. In this paper, we propose MuRel, a multimodal relational network which is learned end-to-end to reason over real images. Our first contribution is the introduction of the MuRel cell, an atomic reasoning primitive representing interactions between question and image regions by a rich vectorial representation, and modeling region relations with pairwise combinations. Secondly, we incorporate the cell into a full MuRel network, which progressively refines visual and question interactions, and can be leveraged to define visualization schemes finer than mere attention maps. We validate the relevance of our approach with various ablation studies, and show its superiority to attention-based methods on three datasets: VQA 2.0, VQA-CP v2 and TDIUC. Our final MuRel network is competitive to or outperforms state-of-the-art results in this challenging context.

关键词： Scene Analysis and Understanding vision + Language Visual Reasoning

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Unprocessing Images for Learned Raw Denoising 32

Unprocessing Images for Learned Raw Denoising

引用

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

作者： Brooks, Tim Mildenhall, Ben Xue, Tianfan Chen, Jiawen Sharlet, Dillon Barron, Jonathan T. Google Res Mountain View CA 94043 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781728132938

Machine learning techniques work best when the data used for training resembles the data used for evaluation. This holds true for learned single-image denoising algorithms, which are applied to real raw camera sensor readings but, due to practical constraints, are often trained on synthetic image data. Though it is understood that generalizing from synthetic to real images requires careful consideration of the noise properties of camera sensors, the other aspects of an image processing pipeline (such as gain, color correction, and tone mapping) are often overlooked, despite their significant effect on how raw measurements are transformed into finished images. To address this, we present a technique to "unprocess" images by inverting each step of an image processing pipeline, thereby allowing us to synthesize realistic raw sensor measurements from commonly available Internet photos. We additionally model the relevant components of an image processing pipeline when evaluating our loss function, which allows training to be aware of all relevant photometric processing that will occur after denoising. By unprocessing and processing training data and model outputs in this way, we are able to train a simple convolutional neural network that has 14%-38% lower error rates and is 9x-18x faster than the previous state of the art on the Darmstadt Noise Dataset [31], and generalizes to sensors outside of that dataset as well.

关键词： Computational Photography Deep Learning Low-level vision

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GAN Prior Embedded Network for Blind Face Restoration in the Wild

GAN Prior Embedded Network for Blind Face Restoration in the...

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

作者： Yang, Tao Ren, Peiran Xie, Xuansong Zhang, Lei Alibaba Grp DAMO Acad Hangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Blind face restoration (BFR) from severely degraded face images in the wild is a very challenging problem. Due to the high illness of the problem and the complex unknown degradation, directly training a deep neural network (DNN) usually cannot lead to acceptable results. Existing generative adversarial network (GAN) based methods can produce better results but tend to generate over-smoothed restorations. In this work, we propose a new method by first learning a GAN for high-quality face image generation and embedding it into a U-shaped DNN as a prior decoder, then fine-tuning the GAN prior embedded DNN with a set of synthesized low-quality face images. The GAN blocks are designed to ensure that the latent code and noise input to the GAN can be respectively generated from the deep and shallow features of the DNN, controlling the global face structure, local face details and background of the reconstructed image. The proposed GAN prior embedded network (GPEN) is easy-to-implement, and it can generate visually photo-realistic results. Our experiments demonstrated that the proposed GPEN achieves significantly superior results to state-of-the-art BFR methods both quantitatively and qualitatively, especially for the restoration of severely degraded face images in the wild.

关键词： Training Degradation Deep learning Codes Image synthesis Face recognition Generative adversarial networks

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Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery 31

Stacked U-Nets for Ground Material Segmentation in Remote Se...

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

作者： Ghosh, Arthita Ehrlich, Max Shah, Sohil Davis, Larry Chellappa, Rama Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other applications. We perform additional post-processing by blending image tiles and degridding the result. Our method gives competitive results on the DeepGlobe dataset.

关键词： Semantics Image segmentation Remote sensing Convolution Training Satellites computer architecture

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