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Leveraging Style and Content features for Text Conditioned Image Retrieval

Leveraging Style and Content features for Text Conditioned I...

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

作者： Chawla, Pranit Jandial, Surgan Badjatiya, Pinkesh Chopra, Ayush Sarkar, Mausoom Krishnamurthy, Balaji IIT Kharagpur Kharagpur W Bengal India IIT Hyderabad Kandi Telangana India Adobe Media & Data Sci Res Lab San Jose CA USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665448994

Image Search is a fundamental task playing a significant role in the success of wide variety of frameworks and applications. However, with the increasing sizes of product catalogues and the number of attributes per product, it has become difficult for users to express their needs effectively. Therefore, we focus on the problem of Image Retrieval with Text Feedback, which involves retrieving modified images according to the natural language feedback provided by users. In this work, we hypothesise that since an image can be delineated by its content and style features, modifications to the image can also take place in the two sub spaces respectively. Hence, we decompose an input image into its corresponding style and content features, apply modification of the text feedback individually in both the style and content spaces and finally fuse them for retrieval. Our experiments show that our approach outperforms a recent state of the art method in this task, TIRG, that seeks to use a single vector in contrast to leveraging the modification via text over style and content spaces separately.

关键词： computer vision Fuses conferences Image retrieval Natural languages pattern recognition Task analysis

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Dynamic Appearance Modelling from Minimal Cameras

Dynamic Appearance Modelling from Minimal Cameras

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

作者： Bridgeman, Lewis Guillemaut, Jean-Yves Hilton, Adrian Univ Surrey CVSSP Guildford Surrey England

ISBN: (纸本)9781665448994

We present a novel method for modelling dynamic texture appearance from a minimal set of cameras. Previous methods to capture the dynamic appearance of a human from multi-view video have relied on large, expensive camera setups, and typically store texture on a frame-by-frame basis. We fit a parameterised human body model to multi-view video from minimal cameras (as few as 3), and combine the partial texture observations from multiple viewpoints and frames in a learned framework to generate full-body textures with dynamic details given an input pose. Key to our method are our multi-band loss functions, which apply separate blending functions to the high and low spatial frequencies to reduce texture artefacts. We evaluate our method on a range of multi-view datasets, and show that our model is able to accurately produce full-body dynamic textures, even with only partial camera coverage. We demonstrate that our method outperforms other texture generation methods on minimal camera setups.

关键词： computer vision Computational modeling conferences Biological system modeling Cameras pattern recognition

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A Deep Adversarial Framework for Visually Explainable Periocular recognition

A Deep Adversarial Framework for Visually Explainable Perioc...

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

作者： Brito, Joao Proenca, Hugo Univ Beira Interior IT Inst Telecomunicacoes P-6200001 Covilha Portugal

ISBN: (纸本)9781665448994

In the biometrics context, the ability to provide the reasoning behind a decision has been at the core of major research efforts. Explanations serve not only to increase the trust amongst the users of a system, but also to augment the system's overall accountability and transparency. In this work, we describe a periocular recognition framework that not only performs biometric recognition, but also provides visual representations of the features/regions that supported a decision. Being particularly designed to explain non-match ("impostors") decisions, our solution uses adversarial generative techniques to synthesise a large set of "genuine" image pairs, from where the most similar elements with respect to a query are retrieved. Then, assuming the alignment between the query/retrieved pairs, the element-wise differences between the query and a weighted average of the retrieved elements yields a visual explanation of the regions in the query pair that would have to be different to transform it into a "genuine" pair. Our quantitative and qualitative experiments validate the proposed solution, yielding recognition rates that are similar to the state-of-the-art, but - most importantly - also providing the visual explanations for every decision.

关键词： Visualization computer vision Biometrics (access control) conferences Transforms Cognition pattern recognition

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Initialization and Transfer Learning of Stochastic Binary Networks from Real-Valued Ones

Initialization and Transfer Learning of Stochastic Binary Ne...

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

作者： Livochka, Anastasiia Shekhovtsov, Alexander Ukrainian Catholic Univ Lvov Ukraine Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9781665448994

We consider the training of binary neural networks (BNNs) using the stochastic relaxation approach, which leads to stochastic binary networks (SBNs). We identify that a severe obstacle to training deep SBNs without skip connections is already the initialization phase. While smaller models can be trained from a random (possibly data-driven) initialization, for deeper models and large datasets, it becomes increasingly difficult to obtain non-vanishing and low variance gradients when initializing randomly. In this work, we initialize SBNs from real-valued networks with ReLU activations. Real valued networks are well established, easier to train and benefit from many techniques to improve their generalization properties. We propose that closely approximating their internal features can provide a good initialization for SBN. We transfer features incrementally, layer-by-layer, accounting for noises in the SBN, exploiting equivalent reparametrizations of ReLU networks and using a novel transfer loss formulation. We demonstrate experimentally that with the proposed initialization, binary networks can be trained faster and achieve a higher accuracy than when initialized randomly.

关键词： Training computer vision conferences Transfer learning Neural networks Stochastic processes pattern recognition

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PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes

PanoDR: Spherical Panorama Diminished Reality for Indoor Sce...

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

作者： Gkitsas, Vasileios Sterzentsenko, Vladimiros Zioulis, Nikolaos Albanis, Georgios Zarpalas, Dimitrios Ctr Res & Technol Hellas Thessaloniki Greece

ISBN: (纸本)9781665448994

The rising availability of commercial 360 degrees cameras that democratize indoor scanning, has increased the interest for novel applications, such as interior space re-design. Diminished Reality (DR) fulfills the requirement of such applications, to remove existing objects in the scene, essentially translating this to a counterfactual inpainting task. While recent advances in data-driven inpainting have shown significant progress in generating realistic samples, they are not constrained to produce results with reality mapped structures. To preserve the 'reality' in indoor (re-)planning applications, the scene's structure preservation is crucial. To ensure structure-aware counterfactual inpainting, we propose a model that initially predicts the structure of a indoor scene and then uses it to guide the reconstruction of an empty - background only - representation of the same scene. We train and compare against other state-of-the-art methods on a version of the Structured3D dataset [47] modified for DR, showing superior results in both quantitative metrics and qualitative results, but more interestingly, our approach exhibits a much faster convergence rate. Code and models are available at ***/VCL3D/PanoDR/

关键词： Measurement Image segmentation computer vision conferences Predictive models Planning pattern recognition

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Quad-DIP for X-ray cargo image decomposition

Quad-DIP for X-ray cargo image decomposition

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

作者： Hu, Zheng Li, Qiang Fu, Gang Xing, Yuxiang Zhang, Li Nuctech Co Ltd AI Ctr Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781665448994

To identify different cargoes on vehicles accurately in scanned image is a tough issue. An unsupervised image decomposition method, based on a novel dual-stage double-DIP (DDIP) network, named as Quad-DIP, was proposed for the decomposition of X-ray scanned image of a cargo vehicle into vehicle and goods separately without ground truth data. The model could be effectively trained based on the fact that, firstly, the structure contents of same type vehicles were similar in the images, and secondly, the contents of goods on different vehicles were different and independent to each other. Our work focus on the content-wise correlation between them. The vehicle structure could be identified from two inputs containing the same type of vehicles, and the image could be decomposed into two components of vehicle structure and cargo information accurately after the training of Quad-DIP. We examine the accuracy of this method on the collected X-ray cargo vehicle dataset. The decomposition of Quad-DIP was more accurate than those of other published methods in literature.

关键词： Training Deep learning computer vision Correlation conferences Generative adversarial networks Image decomposition

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Variational AutoEncoder for Reference based Image Super-Resolution

Variational AutoEncoder for Reference based Image Super-Reso...

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

作者： Liu, Zhi-Song Siu, Wan-Chi Wang, Li-Wen Caritas Inst Higher Educ Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose a novel reference based image super-resolution approach via Variational AutoEncoder (RefVAE). Existing state-of-the-art methods mainly focus on single image super-resolution which cannot perform well on large upsampling factors, e.g., 8x. We propose a reference based image super-resolution, for which any arbitrary image can act as a reference for super-resolution. Even using random map or low-resolution image itself the proposed RefVAE can transfer the knowledge from the reference to the super-resolved images. Depending upon different references, the proposed method can generate different versions of super-resolved images from a hidden super-resolution space. Besides using different datasets for some standard evaluations with PSNR and SSIM, we also took part in the NTIRE2021 SR Space challenge [21] and have provided results of the randomness evaluation of our approach. Compared to other state-of-the-art methods, our approach achieves higher diverse scores.

关键词： computer vision Quantization (signal) conferences Computational modeling Superresolution Space exploration pattern recognition

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Editing like Humans: A Contextual, Multimodal Framework for Automated Video Editing

Editing like Humans: A Contextual, Multimodal Framework for ...

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

作者： Koorathota, Sharath Adelman, Patrick Cotton, Kelly Sajda, Paul Columbia Univ Dept Biomed Engn New York NY 10027 USA Fovea Inc New York NY 10001 USA CUNY Grad Ctr Dept Psychol New York NY USA

ISBN: (纸本)9781665448994

We propose an automated video editing model, which we term contextual and multimodal video editing (CMVE). The model leverages visual and textual metadata describing videos, integrating essential information from both modalities, and uses a learned editing style from a single example video to coherently combine clips. The editing model is useful for tasks such as generating news clip montages and highlight reels given a text query that describes the video storyline. The model exploits the perceptual similarity between video frames, objects in videos and text descriptions to emulate coherent video editing. Amazon Mechanical Turk participants made judgements comparing CMVE to expert human editing. Experimental results showed no significant difference in the CMVE vs human edited video in terms of matching the text query and the level of interest each generates, suggesting CMVE is able to effectively integrate semantic information across visual and textual modalities and create perceptually coherent quality videos typical of human video editors. We publicly release an online demonstration of our method.

关键词： Training Visualization computer vision conferences Semantics Metadata pattern recognition

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Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings

Enriching ImageNet with Human Similarity Judgments and Psych...

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

作者： Roads, Brett D. Love, Bradley C. UCL Dept Expt Psychol London England

ISBN: (纸本)9781665445092

Advances in supervised learning approaches to object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. However, these benchmarks-such as ILSVRC-are relatively task-specific, focusing predominately on predicting class labels. We introduce a publicly-available dataset that embodies the task-general capabilities of human perception and reasoning. The Human Similarity Judgments extension to ImageNet (ImageNet-HSJ) is composed of a large set of human similarity judgments that supplements the existing ILSVRC validation set. The new dataset supports a range of task and performance metrics, including evaluation of unsupervised algorithms. We demonstrate two methods of assessment: using the similarity judgments directly and using a psychological embedding trained on the similarity judgments. This embedding space contains an order of magnitude more points (i.e., images) than previous efforts based on human judgments. We were able to scale to the MI 50,000 image ILSVRC validation set through a selective sampling process that used variational Bayesian inference and model ensembles to sample aspects of the embedding space that were most uncertain. To demonstrate the utility of ImageNet-HSJ, we used the similarity ratings and the embedding space to evaluate how well several popular models conform to human similarity judgments. One finding is that the more complex models that perform better on task-specific benchmarks do not better conform to human semantic judgments. In addition to the human similarity judgments, pre-trained psychological embeddings and code for inferring variational embeddings are made publicly available. ImageNet-HSJ supports the appraisal of internal representations and the development of more humanlike models.

关键词： Measurement computer vision Supervised learning Semantics Psychology Focusing Benchmark testing

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Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay

Dual-Teacher Class-Incremental Learning With Data-Free Gener...

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

作者： Choi, Yoojin El-Khamy, Mostafa Lee, Jungwon Samsung Semicond Inc SoC R&D San Diego CA 92121 USA Samsung Elect Syst LSI Suwon South Korea

ISBN: (纸本)9781665448994

This paper proposes two novel knowledge transfer techniques for class-incremental learning (CIL). First, we propose data-free generative replay (DF-GR) to mitigate catastrophic forgetting in CIL by using synthetic samples from a generative model. In the conventional generative replay, the generative model is pre-trained for old data and shared in extra memory for later incremental learning. In our proposed DF-GR, we train a generative model from scratch without using any training data, based on the pre-trained classification model from the past, so we curtail the cost of sharing pre-trained generative models. Second, we introduce dual-teacher information distillation (DT-ID) for knowledge distillation from two teachers to one student. In CIL, we use DT-ID to learn new classes incrementally based on the pre-trained model for old classes and another model (pre-)trained on the new data for new classes. We implemented the proposed schemes on top of one of the state-of-the-art CIL methods and showed the performance improvement on CIFAR-100 and ImageNet datasets.

关键词： Data privacy computer vision conferences Training data Data models pattern recognition Knowledge transfer

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