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A Universal Encoder Rate Distortion Optimization Framework for Learned Compression

A Universal Encoder Rate Distortion Optimization Framework f...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Jing Li, Bin Li, Jiahao Xiong, Ruiqin Lu, Yan Peking Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665448994

Learning-based image compression has drawn increasing attention in recent years. Despite impressive progress has been made, it still lacks a universal encoder optimization method to seek efficient representation for different images. In this paper, we develop a universal rate distortion optimization framework for learning-based compression, which adaptively optimizes latents and side information together for each image. The proposed framework is independent of network architecture and can be flexibly applied to existing and potential future compression networks. Experimental results demonstrate that we can achieve 6.6% bit rate saving against the latest traditional codec, i.e., VVC, yielding the state-of-the-art compression ratio. Moreover, with the proposed optimization framework, we win the first place in CLIC validation phase for all the three different bit rates in terms of PSNR.

关键词： computer vision Image coding Codecs conferences Bit rate Rate-distortion Optimization methods

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Semantically Grounded Visual Embeddings for Zero-Shot Learning

Semantically Grounded Visual Embeddings for Zero-Shot Learni...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Nawaz, Shah Cavazza, Jacopo Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol IIT Visual Geometry & Modelling VGM Genoa Italy Deutsch Elektronen Synchrotron DESY Hamburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Zero-shot learning methods rely on fixed visual and semantic embeddings, extracted from independent vision and language models, both pre-trained for other large-scale tasks. This is a weakness of current zero-shot learning frameworks as such disjoint embeddings fail to adequately associate visual and textual information to their shared semantic content. Therefore, we propose to learn semantically grounded and enriched visual information by computing a joint image and text model with a two-stream network on a proxy task. To improve this alignment between image and textual representations, provided by attributes, we leverage ancillary captions to provide grounded semantic information. Our method, dubbed joint embeddings for zero-shot learning is evaluated on several benchmark datasets, improving the performance of existing state-of-the-art methods in both standard (+1.6% on aPY, +2.6% on FLO) and generalized (+2.1% on AWA2, +2.2% on CUB) zero-shot recognition.

关键词： Learning systems Visualization Computational modeling Soft sensors conferences Semantics Benchmark testing

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Manipulation Detection in Satellite Images Using vision Transformer

Manipulation Detection in Satellite Images Using Vision Tran...

引用

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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Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression

Adaptive Bitrate Quantization Scheme Without Codebook for Le...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Loehdefink, Jonas Sitzmann, Jonas Baer, Andreas Fingscheidt, Tim Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (纸本)9781665487399

We propose a generic approach to quantization without codebook in learned image compression called onehot max (OHM, O) quantization. It reorganizes the feature space resulting in an additional dimension, along which vector quantization yields one-hot vectors by comparing activations. Furthermore, we show how to integrate Omega quantization into a compression system with bitrate adaptation, i.e., full control over bitrate during inference. We perform experiments on both MNIST and Kodak and report on rate-distortion trade-offs comparing with the integer rounding reference. For low bitrates (< 0.4 bpp), our proposed quantizer yields better performance while exhibiting also other advantageous training and inference properties. Code is available at https://***/ifnspaml/OHMQ.

关键词： Training computer vision Image coding Codes Vector quantization conferences Bit rate

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v2e: From Video Frames to Realistic DVS Events

v2e: From Video Frames to Realistic DVS Events

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hu, Yuhuang Liu, Shih-Chii Delbruck, Tobi Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

To help meet the increasing need for dynamic vision sensor (DVS) event camera data, this paper proposes the v2e toolbox that generates realistic synthetic DVS events from intensity frames. It also clarifies incorrect claims about DVS motion blur and latency characteristics in recent literature. Unlike other toolboxes, v2e includes pixel-level Gaussian event threshold mismatch, finite intensity-dependent bandwidth, and intensity-dependent noise. Realistic DVS events are useful in training networks for uncontrolled lighting conditions. The use of v2e synthetic events is demonstrated in two experiments. The first experiment is object recognition with N-Caltech 101 dataset. Results show that pretraining on various v2e lighting conditions improves generalization when transferred on real DVS data for a ResNet model. The second experiment shows that for night driving, a car detector trained with v2e events shows an average accuracy improvement of 40% compared to the YOLOv3 trained on intensity frames.

关键词： Training Visualization Lighting vision sensors Tools Cameras pattern recognition

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Hierarchical Image Classification using Entailment Cone Embeddings

Hierarchical Image Classification using Entailment Cone Embe...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dhall, Ankit Makarova, Anastasia Ganea, Octavian Pavllo, Dario Greeff, Michael Krause, Andreas Swiss Fed Inst Technol Zurich Switzerland MIT Cambridge MA 02139 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Image classification has been studied extensively, but there has been limited work in using unconventional, external guidance other than traditional image-label pairs for training. We present a set of methods for leveraging information about the semantic hierarchy embedded in class labels. We first inject label-hierarchy knowledge into an arbitrary CNN-based classifier and empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance. Taking a step further in this direction, we model more explicitly the label-label and label-image interactions using order-preserving embeddings governed by both Euclidean and hyperbolic geometries, prevalent in natural language, and tailor them to hierarchical image classification and representation learning. We empirically validate all the models on the hierarchical ETHEC dataset.

关键词： Visualization Semantics Task analysis Geometry Computational modeling Apertures computer vision

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Cyclical Pruning for Sparse Neural Networks

Cyclical Pruning for Sparse Neural Networks

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Srinivas, Suraj Kuzmin, Andrey Nagel, Markus van Baalen, Mart Skliar, Andrii Blankevoort, Tijmen Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland Qualcomm AI Res Amsterdam Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Current methods for pruning neural network weights iteratively apply magnitude-based pruning on the model weights and re-train the resulting model to recover lost accuracy. In this work, we show that such strategies do not allow for the recovery of erroneously pruned weights. To enable weight recovery, we propose a simple strategy called cyclical pruning which requires the pruning schedule to be periodic and allows for weights pruned erroneously in one cycle to recover in subsequent ones. Experimental results on both linear models and large-scale deep neural networks show that cyclical pruning outperforms existing pruning algorithms, especially at high sparsity ratios. Our approach is easy to tune and can be readily incorporated into existing pruning pipelines to boost performance.

关键词： Deep learning Schedules computer vision conferences Computational modeling Neural networks Pipelines

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Teaching computer vision and its Societal Effects: A Look at Privacy and Security Issues from the Students' Perspective 30

Teaching Computer Vision and its Societal Effects: A Look at...

引用

30th IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW)

作者： Cote, Melissa Albu, Alexandra Branzan Univ Victoria Victoria BC Canada

ISBN: (纸本)9781538607336

In this paper, we look at the societal effects of computer vision technologies from the perspective of the future minds in computer vision: senior year engineering students. Engineering education has traditionally focused on technical skills and knowledge. Nowadays, the need for educating engineers in socio-technical skills and reflective thinking, especially on the bright and dark sides of the technology they develop, is being recognized. We advocate for the integration of social awareness modules into computer vision courses so that the societal effects of technology can be studied together with the technology itself, as opposed to the often more generic 'impact of technology on society' courses. Such modules provide a venue for students to reflect on the real-world consequences of technology in concrete, practical contexts. In this paper, we present qualitative results of an observational study analyzing essays of senior year engineering students, who wrote about societal impacts of computer vision technologies of their choice. Privacy and security issues ranked as the top impact topics discussed by students among 50 topics. Similar social awareness modules would apply well to other advanced technical courses of the engineering curriculum where privacy and security are a major concern, such as big data courses. We believe that such modules are highly likely to enhance the reflective abilities of engineering graduates regarding societal impacts of novel technologies.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Edge-enhanced Feature Distillation Network for Efficient Super-Resolution

Edge-enhanced Feature Distillation Network for Efficient Sup...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Yan Nankai Univ Nankai Baidu Joint Lab Tianjin Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the recently massive development in convolution neural networks, numerous lightweight CNN-based image super-resolution methods have been proposed for practical deployments on edge devices. However, most existing methods focus on one specific aspect: network or loss design, which leads to the difficulty of minimizing the model size. To address the issue, we conclude block devising, architecture searching, and loss design to obtain a more efficient SR structure. In this paper, we proposed an edge-enhanced feature distillation network, named EFDN, to preserve the high-frequency information under constrained resources. In detail, we build an edge-enhanced convolution block based on the existing reparameterization methods. Meanwhile, we propose edge-enhanced gradient loss to calibrate the reparameterized path training. Experimental results show that our edge-enhanced strategies preserve the edge and significantly improve the final restoration quality. Code is available at https://***/icandle/EFDN.

关键词： Training computer vision Convolution Image edge detection conferences Superresolution Neural networks

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Multi-view Multi-label Canonical Correlation Analysis for Cross-modal Matching and Retrieval

Multi-view Multi-label Canonical Correlation Analysis for Cr...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sanghavi, Rushil Verma, Yashaswi IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we address the problem of cross-modal retrieval in presence of multi-view and multi-label data. For this, we present Multi-view Multi-label Canonical Correlation Analysis (or MVMLCCA), which is a generalization of CCA for multi-view data that also makes use of high-level semantic information available in the form of multi-label annotations in each view. While CCA relies on explicit pairings/associations of samples between two views (or modalities), MVMLCCA uses the available multi-label annotations to establish correspondence across multiple (two or more) views without the need of explicit pairing of multi-view samples. Extensive experiments on two multi-modal datasets demonstrate that the proposed approach offers much more flexibility than the related approaches without compromising on scalability and cross-modal retrieval performance. Our code and precomputed features are available at https://***/Rushil231100/MVMLCCA.

关键词： computer vision Correlation Codes Annotations Scalability conferences Semantics

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