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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22908 条记录，以下是4301-4310 订阅

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How Much You Ate? Food Portion Estimation on Spoons

How Much You Ate? Food Portion Estimation on Spoons

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Aaryam Sharma Chris Czarnecki Yuhao Chen Pengcheng Xi Linlin Xu Alexander Wong Vision and Image Processing Lab University of Waterloo National Research Council Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil’s surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool.

关键词： computer vision Image segmentation Accuracy Pipelines Estimation Image capture Cameras

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IronMask: Modular Architecture for Protecting Deep Face Template

IronMask: Modular Architecture for Protecting Deep Face Temp...

引用

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

作者： Kim, Sunpill Jeong, Yunseong Kim, Jinsu Kim, Jungkon Lee, Hyung Tae Seo, Jae Hong Hanyang Univ Dept Math Seoul South Korea Hanyang Univ Res Inst Nat Sci Seoul South Korea Jeonbuk Natl Univ Coll Engn Div Comp Sci & Engn Jeonju South Korea Samsung Elect Samsung Res Secur Team Suwon South Korea

ISBN: (纸本)9781665445092

Convolutional neural networks have made remarkable progress in the face recognition field. The more the technology of face recognition advances, the greater discriminative features into a face template. However, this increases the threat to user privacy in case the template is exposed. In this paper, we present a modular architecture for face template protection, called IronMask, that can be combined with any face recognition system using angular distance metric. We circumvent the need for binarization, which is the main cause of performance degradation in most existing face template protections, by proposing a new real-valued error-correcting-code that is compatible with real-valued templates and can therefore, minimize performance degradation. We evaluate the efficacy of IronMask by extensive experiments on two face recognitions, ArcFace and Cos-Face with three datasets, CMU-Multi-PIE, FEI, and Color-FERET. According to our experimental results, IronMask achieves a true accept rate (TAR) of 99.79% at a false accept rate (FAR) of 0.0005% when combined with ArcFace, and 95.78% TAR at 0% FAR with CosFace, while providing at least 115-bit security against known attacks.

关键词： Degradation Measurement Privacy computer vision Face recognition computer architecture Security

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

ZInD-Tell: Towards Translating Indoor Panoramas into Descrip...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Tonmoay Deb Lichen Wang Zachary Bessinger Naji Khosravan Eric Penner Sing Bing Kang Northwestern University Zillow Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper focuses on bridging the gap between natural language descriptions, 360° panoramas, room shapes, and layouts/floorplans of indoor spaces. To enable new multimodal (image, geometry, language) research directions in indoor environment understanding, we propose a novel extension to the Zillow Indoor Dataset (ZInD) which we call ZInD-Tell 1 . We first introduce an effective technique for extracting geometric information from ZInD’s raw structural data, which facilitates the generation of accurate ground truth descriptions using GPT-4. A human-in-the-loop approach is then employed to ensure the quality of these descriptions. To demonstrate the vast potential of our dataset, we introduce the ZInD-Tell benchmark, focusing on two exemplary tasks: language-based home retrieval and indoor description generation. Furthermore, we propose an end-to-end, zero-shot baseline model, ZInD-Agent, designed to process an unordered set of panorama images and generate home descriptions. ZInD-Agent outperforms naïve methods in both tasks, hence, can be considered as a complement to the naïve to show potential use of the data and impact of geometry. We believe this work initiates new trajectories in leveraging computer vision techniques to analyze indoor panorama images descriptively by learning the latent relation between vision, geometry, and language modalities.

关键词： Geometry computer vision Shape Focusing Benchmark testing Information retrieval Human in the loop

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Automated Log-Scale Quantization for Low-Cost Deep Neural Networks

Automated Log-Scale Quantization for Low-Cost Deep Neural Ne...

引用

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

作者： Oh, Sangyun Sim, Hyeonuk Lee, Sugil Lee, Jongeun UNIST Dept Elect Engn Ulsan South Korea UNIST Dept Comp Sci & Engn Ulsan South Korea

ISBN: (纸本)9781665445092

Quantization plays an important role in deep neural network (DNN) hardware. In particular, logarithmic quantization has multiple advantages for DNN hardware implementations, and its weakness in terms of lower performance at high precision compared with linear quantization has been recently remedied by what we call selective two-word logarithmic quantization (STLQ). However, there is a lack of training methods designed for STLQ or even logarithmic quantization in general. In this paper we propose a novel STLQ-aware training method, which significantly outperforms the previous state-of-the-art training method for STLQ. Moreover, our training results demonstrate that with our new training method, STLQ applied to weight parameters of ResNet-18 can achieve the same level of performance as state-of-the-art quantization method, APoT, at 3-bit precision. We also apply our method to various DNNs in image enhancement and semantic segmentation, showing competitive results.

关键词： Training Deep learning Image segmentation computer vision Quantization (signal) Semantics computer architecture

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VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer

VT-Former: An Exploratory Study on Vehicle Trajectory Predic...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Armin Danesh Pazho Ghazal Alinezhad Noghre Vinit Katariya Hamed Tabkhi University of North Carolina Charlotte

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Enhancing roadway safety has become an essential computer vision focus area for Intelligent Transportation Systems (ITS). As a part of ITS, Vehicle Trajectory Prediction (VTP) aims to forecast a vehicle’s future positions based on its past and current movements. VTP is a pivotal element for road safety, aiding in applications such as traffic management, accident prevention, work-zone safety, and energy optimization. While most works in this field focus on autonomous driving, with the growing number of surveillance cameras, another sub-field emerges for surveillance VTP with its own set of challenges. In this paper, we introduce VT-Former, a novel transformer-based VTP approach for highway safety and surveillance. In addition to utilizing transformers to capture long-range temporal patterns, a new Graph Attentive Tokenization (GAT) module has been proposed to capture intricate social interactions among vehicles. This study seeks to explore both the advantages and the limitations inherent in combining transformer architecture with graphs for VTP. Our investigation, conducted across three benchmark datasets from diverse surveillance viewpoints, showcases the State-of-the-Art (SotA) or comparable performance of VT-Former in predicting vehicle trajectories. This study underscores the potential of VT-Former and its architecture, opening new avenues for future research and exploration.

关键词： Road transportation computer vision Surveillance Computational modeling computer architecture Benchmark testing Transformers

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Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks

Faster Than Lies: Real-time Deepfake Detection using Binary ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Romeo Lanzino Federico Fontana Anxhelo Diko Marco Raoul Marini Luigi Cinque Department of Computer Science Sapienza University of Rome Rome Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover, our method incorporates Fast Fourier Transform (FFT) and Local Binary pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake, DFFD, and CIFAKE datasets demonstrate our method’s state-of-the-art performance in most scenarios with a significant efficiency gain of up to a 20× reduction in FLOPs during inference. Finally, by exploring BNNs in deepfake detection to balance accuracy and efficiency, this work paves the way for future research on efficient deepfake detection.

关键词： Deepfakes Accuracy Fast Fourier transforms Computational modeling Neural networks Media Real-time systems

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FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong vision-Language models

FFF: Fixing Flawed Foundations in contrastive pre-training r...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Adrian Bulat Yassine Ouali Georgios Tzimiropoulos Samsung AI Center Cambridge UK Technical University of Iasi Romania Queen Mary University of London UK

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training, in this paper, we show that the full potential of improving the training process by addressing such issues is yet to be realized. Specifically, we firstly study and analyze two issues affecting training: incorrect assignment of negative pairs, and low caption quality and diversity. Then, we devise effective solutions for addressing both problems, which essentially require training with multiple true positive pairs. Finally, we propose training with sigmoid loss to address such a requirement. We show very large gains over the current state-of-the-art for both image recognition (~ +6% on average over 11 datasets) and image retrieval (~ + 19% on Flickr30k and ~ + 15% on MSCOCO).

关键词： Training computer vision Image recognition Noise Image retrieval Field-flow fractionation pattern recognition

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ANR: Articulated Neural Rendering for Virtual Avatars

ANR: Articulated Neural Rendering for Virtual Avatars

引用

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

作者： Raj, Amit Tanke, Julian Hays, James Vo, Minh Stoll, Carsten Lassner, Christoph Georgia Tech Atlanta GA 30332 USA Univ Bonn Bonn Germany Facebook Real Labs Redmond WA USA Epic Games Potomac MA USA

ISBN: (纸本)9781665445092

The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) [38] provides a compelling balance between computational complexity and realism of the resulting images. Using skinned meshes for rendering articulating objects is a natural extension for the DNR framework and would open it up to a plethora of applications. However, in this case the neural shading step must account for deformations that are possibly not captured in the mesh, as well as alignment inaccuracies and dynamics-which can confound the DNR pipeline. We present Articulated Neural Rendering (ANR), a novel framework based on DNR which explicitly addresses its limitations for virtual human avatars. We show the superiority of ANR not only with respect to DNR but also with methods specialized for avatar creation and animation. In two user studies, we observe a clear preference for our avatar model and we demonstrate state-of-the-art performance on quantitative evaluation metrics. Perceptually, we observe better temporal stability, level of detail and plausibility. More results are available at our project page: https : // anr-avatars. ***.

关键词： Measurement computer vision Avatars Computational modeling Pipelines Neural networks Rendering (computer graphics)

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Large-capacity Image Steganography Based on Invertible Neural Networks

Large-capacity Image Steganography Based on Invertible Neura...

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

作者： Lu, Shao-Ping Wang, Rong Zhong, Tao Rosin, Paul L. Nankai Univ CS TKLNDST Tianjin Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff Wales

ISBN: (纸本)9781665445092

Many attempts have been made to hide information in images, where one main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganography and the recovery of hidden images as a pair of inverse problems on image domain transformation, and then introduce the forward and backward propagation operations of a single invertible network to leverage the image embedding and extracting problems. Sharing all parameters of our single ISN architecture enables us to efficiently generate both the container image and the revealed hidden image(s) with high quality. Moreover, in our architecture the capacity of image steganography is significantly improved by naturally increasing the number of channels of the hidden image branch. Comprehensive experiments demonstrate that with this significant improvement of the steganography payload capacity, our ISN achieves state-of-the-art in both visual and quantitative comparisons.

关键词： Backpropagation Steganography Visualization computer vision Inverse problems Neural networks computer architecture

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Spatially-Adaptive Pixelwise Networks for Fast Image Translation

Spatially-Adaptive Pixelwise Networks for Fast Image Transla...

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

作者： Shaham, Tamar Rott Gharbi, Michael Zhang, Richard Shechtman, Eli Michaeli, Tomer Technion Haifa Israel Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We introduce a new generator architecture, aimed at fast and efficient high-resolution image-to-image translation. We design the generator to be an extremely lightweight function of the full-resolution image. In fact, we use pixel-wise networks;that is, each pixel is processed independently of others, through a composition of simple affine transformations and nonlinearities. We take three important steps to equip such a seemingly simple function with adequate expressivity. First, the parameters of the pixel-wise networks are spatially varying, so they can represent a broader function class than simple 1 x 1 convolutions. Second, these parameters are predicted by a fast convolutional network that processes an aggressively low-resolution representation of the input. Third, we augment the input image by concatenating a sinusoidal encoding of spatial coordinates, which provides an effective inductive bias for generating realistic novel high-frequency image content. As a result, our model is up to 18x faster than state-of-the-art baselines. We achieve this speedup while generating comparable visual quality across different image resolutions and translation domains.

关键词： Visualization computer vision Image coding Image synthesis Computational modeling computer architecture Generators

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