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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是291-300 订阅

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Continual Learning Based on OOD Detection and Task Masking

Continual Learning Based on OOD Detection and Task Masking

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kim, Gyuhak Esmaeilpour, Sepideh Xiao, Changnan Liu, Bing Univ Illinois Chicago IL 60607 USA ByteDance Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the task-id is provided for each test sample during testing for TIL, but not provided for CIL. Continual learning methods intended for one problem have limitations on the other problem. This paper proposes a novel unified approach based on out-of-distribution (OOD) detection and task masking, called CLOM, to solve both problems. The key novelty is that each task is trained as an OOD detection model rather than a traditional supervised learning model, and a task mask is trained to protect each task to prevent forgetting. Our evaluation shows that CLOM outperforms existing state-of-the-art baselines by large margins. The average TIL/CIL accuracy of CLOM over six experiments is 87.6/67.9% while that of the best baselines is only 84.4/55.0%.

关键词： Training computer vision Machine learning algorithms Codes conferences Supervised learning Data models

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Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation

Wearable ImageNet: Synthesizing Tileable Textures via Datase...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent methods for Dataset Distillation are able to take in a large set of images of a specific class (e.g., from ImageNet) and synthesize a single image, such that a classifier trained on that image could perform similarly to one trained on the original dataset. It was noticed that the resulting "distilled images" are often quite visually pleasing. In this paper, we describe a simple method for generating tileable distilled textures by sampling random crops from a toroidal canvas of synthetic pixels while enforcing that all such crops serve as effective distilled training data. Such distilled textures not only summarize a given image category in a visually interesting way, but also allow for generation of infinite texture patterns suitable for printing on fabric, clothing, etc. This paper might be just the first step in making the ImageNet dataset into a fashion statement.

关键词： Printing computer vision Wearable computers conferences Clothing Crops Training data

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Any-Width Networks

Any-Width Networks

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Vu, Thanh Eder, Marc Price, True Frahm, Jan-Michael Univ North Carolina Chapel Hill NC 27515 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Despite remarkable improvements in speed and accuracy, convolutional neural networks (CNNs) still typically operate as monolithic entities at inference time. This poses a challenge for resource-constrained practical applications, where both computational budgets and performance needs can vary with the situation. To address these constraints, we propose the Any-Width Network (AWN), an adjustable-width CNN architecture and associated training routine that allow for fine-grained control over speed and accuracy during inference. Our key innovation is the use of lower-triangular weight matrices which explicitly address width-varying batch statistics while being naturally suited for multi-width operations. We also show that this design facilitates an efficient training routine based on random width sampling. We empirically demonstrate that our proposed AWNs compare favorably to existing methods while providing maximally granular control during inference.

关键词： Training Switches computer architecture Standards Convolution Inference algorithms computer vision

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LoL-V2T: Large-Scale Esports Video Description Dataset

LoL-V2T: Large-Scale Esports Video Description Dataset

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tanaka, Tsunehiko Simo-Serra, Edgar Waseda Univ Tokyo Japan

ISBN: (纸本)9781665448994

Esports is a fastest-growing new field with a largely online-presence, and is creating a demand for automatic domain-specific captioning tools. However, at the current time, there are few approaches that tackle the esports video description problem. In this work, we propose a large-scale dataset for esports video description, focusing on the popular game "League of Legends". The dataset, which we call LoL-V2T, is the largest video description dataset in the video game domain, and includes 9,723 clips with 62,677 captions. This new dataset presents multiple new video captioning challenges such as large amounts of domain-specific vocabulary, subtle motions with large importance, and a temporal gap between most captions and the events that occurred. In order to tackle the issue of vocabulary, we propose a masking the domain-specific words and provide additional annotations for this. In our results, we show that the dataset poses a challenge to existing video captioning approaches, and the masking can significantly improve performance. Our dataset and code is publicly available(1).

关键词： Training Vocabulary computer vision Video description conferences Focusing Games

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Layer Importance Estimation with Imprinting for Neural Network Quantization

Layer Importance Estimation with Imprinting for Neural Netwo...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Hongyang Elkerdawy, Sara Ray, Nilanjan Elhoushi, Mostafa Univ Alberta Edmonton AB Canada Huawei Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Neural network quantization has achieved a high compression rate using fixed low bit-width representation of weights and activations while maintaining the accuracy of the high-precision original network. However, mixed precision (per-layer bit-width precision) quantization requires careful tuning to maintain accuracy while achieving further compression and higher granularity than fixed-precision quantization. We propose an accuracy-aware criterion to quantify the layer's importance rank. Our method applies imprinting per layer which acts as a proxy module for accuracy estimation in an efficient way. We rank the layers based on the accuracy gain from previous modules and iteratively quantize first those with less accuracy gain. Previous mixed-precision methods either rely on expensive search techniques such as reinforcement learning (RL) or end-to-end optimization with a lack of interpretation to the quantization configuration scheme. Our method is a one-shot, efficient, accuracy-aware information estimation and thus draws better interpretability to the selected bit-width configuration.

关键词： Training Quantization (signal) Search methods Neural networks Estimation Reinforcement learning pattern recognition

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Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding

Self-Supervised Learning of Remote Sensing Scene Representat...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Stojnic, Vladan Risojevic, Vladimir Univ Banja Luka Fac Elect Engn Banja Luka Bosnia & Herceg

ISBN: (纸本)9781665448994

In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning. In the visual domain its applications are mostly studied in the context of images of natural scenes. However, its applicability is especially interesting in specific areas, like remote sensing and medicine, where it is hard to obtain huge amounts of labeled data. In this work, we conduct an extensive analysis of the applicability of self-supervised learning in remote sensing image classification. We analyze the influence of the number and domain of images used for self-supervised pre-training on the performance on downstream tasks. We show that, for the downstream task of remote sensing image classification, using self-supervised pre-training on remote sensing images can give better results than using supervised pre-training on images of natural scenes. Besides, we also show that self-supervised pre-training can be easily extended to multispectral images producing even better results on our downstream tasks.

关键词： Visualization Image segmentation Image coding conferences Object detection Feature extraction pattern recognition

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Single image HDR synthesis using a Densely Connected Dilated ConvNet

Single image HDR synthesis using a Densely Connected Dilated...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Akhil, K. A. Jiji, C., V Coll Engn Trivandrum Trivandrum Kerala India

ISBN: (纸本)9781665448994

Visual representations using high dynamic range (HDR) images have become increasingly popular because of their high quality and expressive ability. HDR images are expected to be used in a broad range of applications, including digital cinema, photography, and broadcast. The generation of a HDR image from a single exposure Low Dynamic Range (LDR) image is a challenging task where one must make up for missing data due to underexposure or overexposure and color quantization. In this paper, we propose a deep convolutional neural network (CNN) model with a stack of dilated convolutional blocks for reconstructing a HDR image from a single LDR image. Within each dilation block, the dilation rate of the convolution layer is three and progressively decreases to one. Multiple dilation convolution blocks are further connected densely to improve the representation capacity of the network. As the network is trained in a supervised manner, the additional information is reconstructed from learned features. Our experimental results show that the model effectively captures missing information that was lost from the original image.

关键词： Photography Visualization Quantization (signal) Convolution Dynamic range pattern recognition Convolutional neural networks

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Scaled 360 layouts: Revisiting non-central panoramas

Scaled 360 layouts: Revisiting non-central panoramas

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Berenguel-Baeta, Bruno Bermudez-Cameo, Jesus Guerrero, Jose J. Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9781665448994

From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In this work we present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We obtain the boundaries of the structural lines of the room from a non-central panorama using deep learning and exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout. We solve the problem for Manhattan environments, handling occlusions, and also for Atlanta environments in an unified method. The experiments performed improve the state-of-the-art methods for 3D layout recovery from a single panorama. Our approach is the first work using deep learning with non-central panoramas and recovering the scale of single panorama layouts.

关键词： Deep learning computer vision Three-dimensional displays Sensitivity conferences Layout Neural networks

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Multiple People Tracking using Body and Joint Detections 32

Multiple People Tracking using Body and Joint Detections

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Henschel, Roberto Zou, Yunzhe Rosenhahn, Bodo Leibniz Univ Hannover Hannover Germany

ISBN: (纸本)9781728125060

Most multiple people tracking systems compute trajectories based on the tracking-by-detection paradigm. Consequently, the performance depends to a large extent on the quality of the employed input detections. However, despite an enormous progress in recent years, partially occluded people are still often not recognized. Also, many correct detections are mistakenly discarded when the non-maximum suppression is performed. Improving the tracking performance thus requires to augment the coarse input. Well suited for this task are fine-graded body joint detections, as they allow to locate even strongly occluded persons. Thus in this work, we analyze the suitability of including joint detections for multiple people tracking. We introduce different affinities between the two detection types and evaluate their performances. Tracking is then performed within a near-online framework based on a Min cost graph labeling formulation. As a result, our framework can recover heavily occluded persons and solve the data association efficiently. We evaluate our framework on the MOT16/17 benchmark. Experimental results demonstrate that our framework achieves state-of-the-art results.

关键词： computer vision

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Is synthetic voice detection research going into the right direction?

Is synthetic voice detection research going into the right d...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Borzi, Stefano Giudice, Oliver Stanco, Filippo Allegra, Dario Univ Catania Dept Math & Comp Sci Catania Italy Banca Italia IT Dept Appl Res Team Rome Italy

ISBN: (纸本)9781665487399

Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evolution enabled the construction of effective frameworks and solutions able to be used in investigative and forensics scenarios for detection of fakes and, in general, manipulations in multimedia contents. On the other hand, can we trust these systems? Is research activity going in the right direction? Are we just taking the low-hanging fruit without taking into account many real-case-in-the-wild situations? The purpose of this paper is to raise an alert to the research community in the specific context of synthetic voice detection, where data available for training is not big enough to give sufficient trust in the techniques available in the literature. To this aim, an exploratory investigation of the most common voice spoofing dataset was carried out and it was surprisingly easy to build simple classifiers without any Deep Learning techniques. Simple considerations on bitrate were sufficient to achieve an effective detection performance.

关键词： Deep learning Training computer vision Image color analysis Forensics conferences Bit rate

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