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FashionVLP: vision Language Transformer for Fashion Retrieval with Feedback

FashionVLP: Vision Language Transformer for Fashion Retrieva...

引用

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

作者： Goenka, Sonam Zheng, Zhaoheng Jaiswal, Ayush Chada, Rakesh Wu, Yue Hedau, Varsha Natarajan, Pradeep Amazon Alexa Nat Understanding Berkeley CA 94720 USA USC Viterbi Sch Engn Los Angeles CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Fashion image retrieval based on a query pair of reference image and natural language feedback is a challenging task that requires models to assess fashion related information from visual and textual modalities simultaneously. We propose a new vision-language transformer based model, FashionVLP, that brings the prior knowledge contained in large image-text corpora to the domain of fashion image retrieval, and combines visual information from multiple levels of context to effectively capture fashion-related *** queries are encoded through the transformer layers, our asymmetric design adopts a novel attention-based approach for fusing target image features without involving text or transformer layers in the process. Extensive results show that FashionVLP achieves the state-of-the-art performance on benchmark datasets, with a large 23% relative improvement on the challenging FashionlQ dataset, which contains complex natural language feedback.

关键词： Visualization Computational modeling Natural languages Image retrieval Memory management Benchmark testing Transformers

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Cross-Image Relational Knowledge Distillation for Semantic Segmentation

Cross-Image Relational Knowledge Distillation for Semantic S...

引用

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

作者： Yang, Chuanguang Zhou, Helong An, Zhulin Jiang, Xue Xu, Yongjun Zhang, Qian Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Horizon Robot Beijing Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher's structured information generated from individual data samples. However, they ignore the global semantic relations among pixels across various images that are valuable for KD. This paper proposes a novel Cross-Image Relational KD (CIRKD), which focuses on transferring structured pixel-to-pixel and pixel-to-region relations among the whole images. The motivation is that a good teacher network could construct a well-structured feature space in terms of global pixel dependencies. CIRKD makes the student mimic better structured semantic relations from the teacher, thus improving the segmentation performance. Experimental results over Cityscapes, CamVid and Pascal VOC datasets demonstrate the effectiveness of our proposed approach against state-of-the-art distillation methods. The code is available at https://***/winycg/CIRKD.

关键词： Image segmentation computer vision Correlation Codes Shape Semantics pattern recognition

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Reflash Dropout in Image Super-Resolution

Reflash Dropout in Image Super-Resolution

引用

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

作者： Kong, Xiangtao Liu, Xina Gu, Jinjin Qiao, Yu Dong, Chao Chinese Acad Sci Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Sydney Sydney NSW Australia Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a different behaviour as high-level tasks and is sensitive to the dropout operation. However, in this paper, we show that appropriate usage of dropout benefits SR networks and improves the generalization ability. Specifically, dropout is better embedded at the end of the network and is significantly helpful for the multi-degradation settings. This discovery breaks our common sense and inspires us to explore its working mechanism. We further use two analysis tools - one is from a recent network interpretation work, and the other is specially designed for this task. The analysis results provide side proofs to our experimental findings and show us a new perspective to understand SR networks.

关键词： computer vision Superresolution pattern recognition Task analysis

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Proactive Image Manipulation Detection

Proactive Image Manipulation Detection

引用

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

作者： Asnani, Vishal Yin, Xi Hassner, Tal Liu, Sijia Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA Meta AI Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image manipulation detection algorithms are often trained to discriminate between images manipulated with particular Generative Models (GMs) and genuine/real images, yet generalize poorly to images manipulated with GMs unseen in the training. Conventional detection algorithms receive an input image passively. By contrast, we propose a proactive scheme to image manipulation detection. Our key enabling technique is to estimate a set of templates which when added onto the real image would lead to more accurate manipulation detection. That is, a template protected real image, and its manipulated version, is better discriminated compared to the original real image vs. its manipulated one. These templates are estimated using certain constraints based on the desired properties of templates. For image manipulation detection, our proposed approach outperforms the prior work by an average precision of 16% for CycleGAN and 32% for GauGAN. Our approach is generalizable to a variety of GMs showing an improvement over prior work by an average precision of 10% averaged across 12 GMs. Our code is available at https://***/visha13477/proactive_IMD.

关键词： Training computer vision Codes Cameras pattern recognition Detection algorithms

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Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction

Gradient-SDF: A Semi-Implicit Surface Representation for 3D ...

引用

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

作者： Sommer, Christiane Sang, Lu Schubert, David Cremers, Daniel Tech Univ Munich Comp Vis Grp Munich Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Gradient-SDF, a novel representation for 3D geometry that combines the advantages of implict and explicit representations. By storing at every voxel both the signed distance field as well as its gradient vector field, we enhance the capability of implicit representations with approaches originally formulated for explicit surfaces. As concrete examples, we show that (1) the Gradient-SDF allows us to perform direct SDF tracking from depth images, using efficient storage schemes like hash maps, and that (2) the Gradient-SDF representation enables us to perform photometric bundle adjustment directly in a voxel representation (without transforming into a point cloud or mesh), naturally a fully implicit optimization of geometry and camera poses and easy geometry upsampling. Experimental results confirm that this leads to significantly sharper reconstructions. Since the overall SDF voxel structure is still respected, the proposed Gradient-SDF is equally suited for (GPU) parallelization as related approaches.

关键词： Geometry Bundle adjustment Point cloud compression Surface reconstruction Pose estimation Sensors pattern recognition

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Projective Manifold Gradient Layer for Deep Rotation Regression

Projective Manifold Gradient Layer for Deep Rotation Regress...

引用

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

作者： Chen, Jiayi Yin, Yingda Birdal, Tolga Chen, Baoquan Guibas, Leonidas J. Wang, He Peking Univ CFCS Beijing Peoples R China Beijing Inst Gen AI Beijing Peoples R China Stanford Univ Stanford CA 94305 USA Imperial Coll London London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem. The gap between the Euclidean network output space and the non-Euclidean SO(3) manifold imposes a severe challenge for neural network learning in both forward and backward passes. While several works have proposed different regression-friendly rotation representations, very few works have been devoted to improving the gradient back-propagating in the backward pass. In this paper, we propose a manifold-aware gradient that directly backpropagates into deep network weights. Leveraging Riemannian optimization to construct a novel projective gradient, our proposed regularized projective manifold gradient (RPMG) method helps networks achieve new state-of-the-art performance in a variety of rotation estimation tasks. Our proposed gradient layer can also be applied to other smooth manifolds such as the unit sphere. Our project page is at https://***/RPMG.

关键词： Manifolds Deep learning computer vision Neural networks Pose estimation pattern recognition Task analysis

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Deblurring via Stochastic Refinement

Deblurring via Stochastic Refinement

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

作者： Whang, Jay Delbracio, Mauricio Talebi, Hossein Saharia, Chitwan Dimakis, Alexandros G. Milanfar, Peyman Univ Texas Austin Austin TX 78712 USA Google Res Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions. We present an alternative framework for blind deblurring based on conditional diffusion models. Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input. This leads to a significant improvement in perceptual quality over existing state-of-the-art methods across multiple standard benchmarks. Our predict-and-refine approach also enables much more efficient sampling compared to typical diffusion models. Combined with a carefully tuned network architecture and inference procedure, our method is competitive in terms of distortion metrics such as PSNR. These results show clear benefits of our diffusion-based method for deblurring and challenge the widely used strategy of producing a single, deterministic reconstruction.

关键词： Measurement computer vision Stochastic processes Predictive models Network architecture Distortion pattern recognition

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Globetrotter: Connecting Languages by Connecting Images

Globetrotter: Connecting Languages by Connecting Images

引用

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

作者： Suris, Didac Epstein, Dave Vondrick, Carl Columbia Univ New York NY 10027 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Machine translation between many languages at once is highly challenging, since training with ground truth requires supervision between all language pairs, which is difficult to obtain. Our key insight is that, while languages may vary drastically, the underlying visual appearance of the world remains consistent. We introduce a method that uses visual observations to bridge the gap between languages, rather than relying on parallel corpora or topological properties of the representations. We train a model that aligns segments of text from different languages if and only if the images associated with them are similar and each image in turn is well-aligned with its textual description. We train our model from scratch on a new dataset of text in over fifty languages with accompanying images. Experiments show that our method outperforms previous work on unsupervised word and sentence translation using retrieval. Code, models and data are available on ***

关键词： Training Deep learning Visualization Image segmentation computer vision Codes Computational modeling

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RFNet: Unsupervised Network for Mutually Reinforcing Multi-modal Image Registration and Fusion

RFNet: Unsupervised Network for Mutually Reinforcing Multi-m...

引用

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

作者： Xu, Han Ma, Jiayi Yuan, Jiteng Le, Zhuliang Liu, Wei Wuhan Univ Wuhan Peoples R China Tencent Data Platform Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose a novel method to realize multi-modal image registration and fusion in a mutually reinforcing framework, termed as RFNet. We handle the registration in a coarse-to-fine fashion. For the first time, we exploit the feedback of image fusion to promote the registration accuracy rather than treating them as two separate issues. The fine-registered results also improve the fusion performance. Specifically, for image registration, we solve the bottlenecks of defining registration metrics applicable for multi-modal images and facilitating the network convergence. The metrics are defined based on image translation and image fusion respectively in the coarse and fine stages. The convergence is facilitated by the designed metrics and a deformable convolution-based network. For image fusion, we focus on texture preservation, which not only increases the information amount and quality of fusion results but also improves the feedback of fusion results. The proposed method is evaluated on multi-modal images with large global parallaxes, images with local misalignments and aligned images to validate the performances of registration and fusion. The results in these cases demonstrate the effectiveness of our method.

关键词： Measurement Deformable models Image registration computer vision pattern recognition Task analysis Image fusion

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Hyperbolic vision Transformers: Combining Improvements in Metric Learning

Hyperbolic Vision Transformers: Combining Improvements in Me...

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

作者： Ermolov, Aleksandr Mirvakhabova, Leyla Khrulkov, Valentin Sebe, Nicu Oseledets, Ivan Univ Trento Trento Italy Skolkovo Inst Sci & Technol Moscow Russia Yandex Moscow Russia AIRI Moscow Russia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Metric learning aims to learn a highly discriminative model encouraging the embeddings of similar classes to be close in the chosen metrics and pushed apart for dissimilar ones. The common recipe is to use an encoder to extract embeddings and a distance-based loss function to match the representations - usually, the Euclidean distance is utilized. An emerging interest in learning hyperbolic data embeddings suggests that hyperbolic geometry can be beneficial for natural data. Following this line of work, we propose a new hyperbolic-based model for metric learning. At the core of our method is a vision transformer with output embeddings mapped to hyperbolic space. These embeddings are directly optimized using modified pairwise cross-entropy loss. We evaluate the proposed model with six different formulations on four datasets achieving the new state-of-the-art performance. The source code is available at https://***/htdt/hyp_metric.

关键词： Measurement Geometry Visualization Semantics Self-supervised learning Transformer cores Transformers

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