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Segment Anything in Food Images

Segment Anything in Food Images

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Saeed S. Alahmari Michael Gardner Tawfiq Salem Najran University Saudi Arabia King Faisal University Saudi Arabia Purdue University USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a new approach for food image segmentation utilizing the Segment Anything Model (SAM), with the additional refinement achieved through fine-tuning with Low-Rank Adaptation layers (LoRA). The segmentation task involves generating a binary mask for food in RGB images, with pixels categorized as background or food. We conduct various experiments to assess and compare the performance of our proposed method with previous approaches. Our findings indicate that our method consistently outperforms other techniques, achieving an accuracy of 94.14%. The improved accuracy of our approach highlights its potential for various applications in food image analysis, contributing to the advancement of computer vision techniques in the realm of food recognition and segmentation.

关键词： Image segmentation computer vision Adaptation models Technological innovation Accuracy Image recognition Image analysis

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Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment

Enhancing Multi-Camera People Tracking with Anchor-Guided Cl...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Huang, Hsiang-Wei Yang, Cheng-Yen Jiang, Zhongyu Kim, Pyong-Kun Lee, Kyoungoh Kim, Kwangju Ramkumar, Samartha Mullapudi, Chaitanya Jang, In-Su Huang, Chung-I Hwang, Jenq-Neng University of Washington Information Processing Lab United States Electronics and Telecommunications Research Institute Korea Republic of National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

Multi-camera multiple people tracking has become an increasingly important area of research due to the growing demand for accurate and efficient indoor people tracking systems, particularly in settings such as retail, healthcare centers, and transit hubs. We proposed a novel multi-camera multiple people tracking method that uses anchor-guided clustering for cross-camera re-identification and spatio-temporal consistency for geometry-based cross-camera ID reassigning. Our approach aims to improve the accuracy of tracking by identifying key features that are unique to every individual and utilizing the overlap of views between cameras to predict accurate trajectories without needing the actual camera parameters. The method has demonstrated robustness and effectiveness in handling both synthetic and real-world data. The proposed method is evaluated on CVPR AI City Challenge 2023 dataset, achieving IDF1 of 95.36% with the first-place ranking in the challenge. The code is available at: https://***/ipl-uw/AIC23-Track1-UWIPL-ETRI. © 2023 ieee.

关键词： computer vision

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Underwater Light Field Retention : Neural Rendering for Underwater Imaging

Underwater Light Field Retention : Neural Rendering for Unde...

引用

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

作者： Ye, Tian Chen, Sixiang Liu, Yun Ye, Yi Chen, Erkang Li, Yuche Jimei Univ Sch Ocean Informat Engn Xiamen Peoples R China Southwest Univ Coll Artificial Intelligence Chongqing Peoples R China China Univ Petr Coll Geosci Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Underwater Image Rendering aims to generate a true-to-life underwater image from a given clean one, which could be applied to various practical applications such as underwater image enhancement, camera filter, and virtual gaming. We explore two less-touched but challenging problems in underwater image rendering, namely, i) how to render diverse underwater scenes by a single neural network? ii) how to adaptively learn the underwater light fields from natural exemplars, i,e., realistic underwater images? To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering). Specifically, UWNR is a data-driven neural network that implicitly learns the natural degenerated model from authentic underwater images, avoiding introducing erroneous biases by hand-craft imaging models. Compared with existing underwater image generation methods, UWNR utilizes the natural light field to simulate the main characteristics of the underwater scene. Thus, it is able to synthesize a wide variety of underwater images from one clean image with various realistic underwater images. Extensive experiments demonstrate that our approach achieves better visual effects and quantitative metrics over previous methods. Moreover, we adopt UWNR to build an open Large Neural Rendering Underwater Dataset containing various types of water quality, dubbed LNRUD. The source code and LNRUD are available at https://***/Ephemeral182/UWNR. [GRAPHICS] .

关键词： Measurement Image synthesis conferences Neural networks Water quality Rendering (computer graphics) Visual effects

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Learning to See in Nighttime Driving Scenes with Inter-frequency Priors

Learning to See in Nighttime Driving Scenes with Inter-frequ...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Fan, Zhentao Wu, Xianhao Chen, Xiang Li, Yufeng Shenyang Aerospace University College of Electronic and Information Engineering China Nanjing University of Science and Technology School of Computer Science and Engineering China

ISBN: (纸本)9798350302493

Currently, image-to-image translation methods have achieved significant performance with the help of deep CNNs and GANs, but most existing models are not suitable for autonomous driving scenarios due to their interpretability. In this paper, we develop a high-quality image translator (i.e. night → day), N2D-LPNet, to facilitate nighttime driving scene dperception. Instead of pursuing a complex network structure, we first attempt to explore the inter-frequency relation knowledge to simplify image translation process. Specifically, the lightweight Laplace pyramid is introduced as the backbone architecture to decompose the feature maps of nighttime image into high- and low-frequency components. Considering the similar morphological properties of different frequency components, we design a flexible inter-frequency guiding strategy which utilizes each lower frequency information to refine the higher frequency feature in a progressive manner. Benefiting from the advantage of constraint from inter-frequency priors, our method can process the nighttime image better under the practical driving system while still persevere competitive results. We also discuss the potential value of N2D-LPNet for other high-level vision tasks. © 2023 ieee.

关键词： Complex networks

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A System for Dense Monocular Mapping with a Fisheye Camera

A System for Dense Monocular Mapping with a Fisheye Camera

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Gallagher, Louis Sistu, Ganesh Horgan, Jonathan McDonald, John B. Maynooth University Department of Computer Science Co Kildare Ireland Valeo Vision Systems Tuam Ireland

ISBN: (纸本)9798350302493

We introduce a novel dense mapping system that uses a single monocular fisheye camera as the sole input sensor and incrementally builds a dense surfel representations of the scene's 3D geometry. We extend an existing hybrid sparse-dense monocular SLAM system, reformulating the mapping pipeline in terms of the Kannala-Brandt fisheye camera model. Each frame is processed in its original undistorted fisheye form, with no attempt to remove distortion. To estimate depth, we introduce a new version of the PackNet depth estimation neural network adapted for fisheye inputs. We reformulate PackNet's multi-view stereo self-supervised loss in terms of the Kannala-Brandt fisheye camera model. To encourage the network to learn metric depth during training, the pose network is weakly supervised with the camera's ground-truth inter-frame velocity. To improve overall performance, we additionally provide sparse depth supervision from dataset LiDAR and SICK laser scanners. We demonstrate our system's performance on the realworld KITTI-360 benchmark dataset. Our experimental results show that our system is capable of accurate, metric camera tracking and dense surface reconstruction within local windows. Our system operates within real-time processing rates and in challenging conditions. We direct the reader to the following video where the system can be seen in operation: https://***/Y-9q-wfqocs. © 2023 ieee.

关键词： Mapping

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Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Liu, Ajian Tan, Zichang Liang, Yanyan Wan, Jun Institute of Automation Chinese Academy of Sciences Mais Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350302493

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack pattern Generation (Adv-APG) module and a Supervised learning-based Attack pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets. © 2023 ieee.

关键词： Face recognition

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Evaluating the Integration of Morph Attack Detection in Automated Face recognition Systems

Evaluating the Integration of Morph Attack Detection in Auto...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Andrea Panzino Simone Maurizio La Cava Giulia Orrù Gian Luca Marcialis University of Cagliari Cagliari Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Due to the possibility of automatically verifying an individual’s identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with digital manipulation detectors. Morphed facial images can be considered a threat among other manipulations because they are visually indistinguishable from authentic facial photos. They can have characteristics of many possible subjects due to the nature of the attack. Thus, morphing attack detection methods (MADs) must be integrated into automated face recognition. Following the recent advances in MADs, we investigate their effectiveness by proposing an integrated system simulator of real application contexts, moving from known to never-seen-before attacks.

关键词： computer vision Face recognition conferences Employment Process control Detectors Market research

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Consistency and Accuracy of CelebA Attribute Values

Consistency and Accuracy of CelebA Attribute Values

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wu, Haiyu Bezold, Grace Günther, Manuel Boult, Terrance King, Michael C. Bowyer, Kevin W. University of Notre Dame United States University of Zurich Switzerland University of Colorado Colorado Springs United States Florida Institute of Technology United States

ISBN: (纸本)9798350302493

We report the first systematic analysis of the experimental foundations of facial attribute classification. Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with ≥ 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency. Of 5,068 duplicate face appearances in CelebA, attributes have contradicting values on from 10 to 860 of the 5,068 duplicates. Manual audit of a subset of CelebA estimates error rates as high as 40% for (no beard=false), even though the labeling consistency experiment indicates that no beard could be assigned with ≥ 95% consistency. Selecting the mouth slightly open (MSO) for deeper analysis, we estimate the error rate for (MSO=true) at about 20% and (MSO=false) at about 2%. A corrected version of the MSO attribute values enables learning a model that achieves higher accuracy than previously reported for MSO. Corrected values for CelebA MSO are available at https://***/HaiyuWu/CelebAMSO. © 2023 ieee.

关键词： computer vision

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Multimodal Understanding of Memes with Fair Explanations

Multimodal Understanding of Memes with Fair Explanations

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yang Zhong Bhiman Kumar Baghel Department of Computer Science University of Pittsburgh PA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Digital Memes have been widely utilized in people’s daily lives over social media platforms. Composed of images and descriptive texts, memes are often distributed with the flair of sarcasm or humor, yet can also spread harmful content or biases from social and cultural factors. Aside from mainstream tasks such as meme generation and classification, generating explanations for memes has become more vital and poses challenges in avoiding propagating already embedded biases. Our work studied whether recent advanced vision Language Models (VL models) can fairly explain meme contents from different domains/topics, contributing to a unified benchmark for meme explanation. With the dataset, we semi-automatically and manually evaluate the quality of VL model-generated explanations, identifying the major categories of biases in meme explanations.

关键词： computer vision Social networking (online) conferences Computational modeling Benchmark testing pattern recognition Cultural differences

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CAT-NeRF: Constancy-Aware Tx2Former for Dynamic Body Modeling

CAT-NeRF: Constancy-Aware Tx2Former for Dynamic Body Modelin...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhu, Haidong Zheng, Zhaoheng Zheng, Wanrong Nevatia, Ram University of Southern California United States

ISBN: (纸本)9798350302493

This paper addresses the problem of human rendering in the video with temporal appearance constancy. Reconstructing dynamic body shapes with volumetric neural rendering methods, such as NeRF, requires finding the correspondence of the points in the canonical and observation space, which demands understanding human body shape and motion. Some methods use rigid transformation, such as SE(3), which cannot precisely model each frame's unique motion and muscle movements. Others generate the transformation for each frame with a trainable network, such as neural blend weight field or translation vector field, which does not consider the appearance constancy of general body shape. In this paper, we propose CAT-NeRF for self-awareness of appearance constancy with Tx2Former, a novel way to combine two Transformer layers, to separate appearance constancy and uniqueness. Appearance constancy models the general shape across the video, and uniqueness models the unique patterns for each frame. We further introduce a novel Covariance Loss to limit the correlation between each pair of appearance uniquenesses to ensure the frame-unique pattern is maximally captured in appearance uniqueness. We assess our method on H36M and ZJU-MoCap and show state-of-the-art performance. © 2023 ieee.

关键词： computer vision

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