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Selective Multi-View Deep Model for 3D Object Classification

Selective Multi-View Deep Model for 3D Object Classification

引用

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

作者： Mona Alzahrani Muhammad Usman Saeed Anwar Tarek Helmy Department of Information & Computer Science KFUPM Dhahran Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran Saudi Arabia Center for Intelligent Secure Systems KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

3D object classification has emerged as a practical technology with applications in various domains, such as medical image analysis, automated driving, intelligent robots, and crowd surveillance. Among the different approaches, multi-view representations for 3D object classification have shown the most promising results, achieving state-of-the-art performance. However, there are certain limitations in current view-based 3D object classification methods. One observation is that using all captured views for classification can confuse the classifier and lead to misleading results for certain classes. Additionally, some views may contain more discriminative information for object classification than others. These observations motivate the development of smarter and more efficient selective multi-view classification models. In this work, we propose a Selective Multi-View Deep Model that extracts multi-view images from 3D data representations and selects the most influential view by assigning importance scores using the cosine similarity method based on visual features detected by a pre-trained CNN. The proposed method is evaluated on the ModelNet40 dataset for the task of 3D classification. The results demonstrate that the proposed model achieves an overall accuracy of 88.13% using only a single view when employing a shading technique for rendering the views, pre-trained ResNet-152 as the backbone CNN for feature extraction, and a Fully Connected Network (FCN) as the classifier.

关键词： Training Solid modeling Visualization Three-dimensional displays Image analysis Surveillance Feature extraction

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vision-language models for decoding provider attention during neonatal resuscitation

Vision-language models for decoding provider attention durin...

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

作者： Felipe Parodi Jordan K. Matelsky Alejandra Regla-Vargas Elizabeth E. Foglia Charis Lim Danielle Weinberg Konrad P. Kording Heidi M. Herrick Michael L. Platt Department of Neuroscience University of Pennsylvania Department of Bioengineering University of Pennsylvania Johns Hopkins University Applied Physics Laboratory Department of Sociology University of Pennsylvania Department of Pediatrics Division of Neonatology University of Pennsylvania Perelman School of Medicine Department of Pediatrics Division of Neonatology Children’s Hospital of Philadelphia Department of Marketing University of Pennsylvania Department of Psychology University of Pennsylvania

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously. Gaze strongly influences decision making; thus, understanding where a provider is looking during neonatal resuscitations could inform provider training, enhance real-time decision support, and improve the design of delivery rooms and neonatal intensive care units (NICUs). Current approaches to quantifying neonatal providers’ gaze rely on manual coding or simulations, which limit scalability and utility. Here, we introduce an automated, real-time, deep learning approach capable of decoding provider gaze into semantic classes directly from first-person point-of-view videos recorded during live resuscitations. Combining state-of-the-art, real-time segmentation with vision-language models, our low-shot pipeline attains 91% classification accuracy in identifying gaze targets without training. Upon fine-tuning, the performance of our gaze-guided vision transformer exceeds 98% accuracy in semantic gaze analysis, approaching human-level precision. This system, capable of real-time inference, enables objective quantification of provider attention dynamics during live neonatal resuscitation. Our approach offers a scalable solution that seamlessly integrates with existing infrastructure for data-scarce gaze analysis, thereby offering new opportunities for understanding and refining clinical decision making.

关键词： Training Pediatrics computer vision Accuracy Semantics Decision making Transformers

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Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge

Energy-Efficient Uncertainty-Aware Biomass Composition Predi...

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

作者： Muhammad Zawish Paul Albert Flavio Esposito Steven Davy Lizy Abraham Walton Institute South East Technological University Ireland Center for Augmented Reasoning Australian Institute of Machine Learning Australia Department of Computer Science Saint Louis University USA Centre for Sustainable Digital Technologies at Technological University Dublin Ireland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Clover fixates nitrogen from the atmosphere to the ground, making grass-clover mixtures highly desirable to reduce external nitrogen fertilization. Herbage containing clover additionally promotes higher food intake, resulting in higher milk production. Herbage probing however remains largely unused as it requires a time-intensive manual laboratory analysis. Without this information, farmers are unable to perform localized clover sowing or take targeted fertilization decisions. Deep learning algorithms have been proposed with the goal to estimate the dry biomass composition from images of the grass directly in the fields. The energy-intensive nature of deep learning however limits deployment to practical edge devices such as smartphones. This paper proposes to fill this gap by applying filter pruning to reduce the energy requirement of existing deep learning solutions. We report that although pruned networks are accurate on controlled, high-quality images of the grass, they struggle to generalize to real-world smartphone images that are blurry or taken from challenging angles. We address this challenge by training filter-pruned models using a variance attenuation loss so they can predict the uncertainty of their predictions. When the uncertainty exceeds a threshold, we re-infer using a more accurate unpruned model. This hybrid approach allows us to reduce energy-consumption while retaining a high accuracy. We evaluate our algorithm on two datasets: the GrassClover and the Irish clover using an NVIDIA Jetson Nano edge device. We find that we reduce energy reduction with respect to state-of-the-art solutions by 50% on average with only 4% accuracy loss.

关键词： Deep learning Accuracy Uncertainty Image edge detection Filtering algorithms Predictive models Prediction algorithms

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An Iterative Quantum Approach for Transformation Estimation from Point Sets

An Iterative Quantum Approach for Transformation Estimation ...

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conference on computer vision and pattern recognition (CVPR)

作者： Natacha Luete Meli Florian Mannel Jan Lellmann Institute of Mathematics and Image Computing University of Luebeck

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio.

关键词： computer vision Three-dimensional displays Annealing Estimation Quantum annealing pattern recognition Iterative methods

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Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

Learning Low-rank Deep Neural Networks via Singular Vector O...

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

作者： Huanrui Yang Minxue Tang Wei Wen Feng Yan Daniel Hu Ang Li Hai Li Yiran Chen Department of Electrical and Computer Engineering Duke University Department of Electronic Engineering Tsinghua University Computer Science and Engineering Department University of Nevada Reno Newport High School Bellevue WA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Modern deep neural networks (DNNs) often require high memory consumption and large computational loads. In order to deploy DNN algorithms efficiently on edge or mobile devices, a series of DNN compression algorithms have been explored, including factorization methods. Factorization methods approximate the weight matrix of a DNN layer with the multiplication of two or multiple low-rank matrices. However, it is hard to measure the ranks of DNN layers during the training process. Previous works mainly induce low-rank through implicit approximations or via costly singular value decomposition (SVD) process on every training step. The former approach usually induces a high accuracy loss while the latter has a low efficiency. In this work, we propose SVD training, the first method to explicitly achieve low-rank DNNs during training without applying SVD on every step. SVD training first decomposes each layer into the form of its full-rank SVD, then performs training directly on the decomposed weights. We add orthogonality regularization to the singular vectors, which ensure the valid form of SVD and avoid gradient vanishing/exploding. Low-rank is encouraged by applying sparsity-inducing regularizers on the singular values of each layer. Singular value pruning is applied at the end to explicitly reach a low-rank model. We empirically show that SVD training can significantly reduce the rank of DNN layers and achieve higher reduction on computation load under the same accuracy, comparing to not only previous factorization methods but also state-of-the-art filter pruning methods.

关键词： Training Matrix decomposition Convolution Computational modeling Kernel Load modeling Tensile stress

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Multi-Explainable TemporalNet: An Interpretable Multimodal Approach using Temporal Convolutional Network for User-level Depression Detection

Multi-Explainable TemporalNet: An Interpretable Multimodal A...

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

作者： Anas Zafar Danyal Aftab Rizwan Qureshi Yaofeng Wang Hong Yan Fast School of Computing National University of Computer and Emerging Sciences Karachi Pakistan Technological University Dublin Dublin Ireland Center for Regenerative Medicine and Health Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong SAR China Department of Electrical Engineering City University of Hong Kong Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal depression detection through internet-based data such as social media platforms has been an important problem in the research community, aiming to predict human mental states for ensuring wellbeing of the society. Recently, attention-based networks have gained significant popularity for depression detection. However, existing multimodal methods primarily rely on images and text assuming no correlation between temporal aspects such as relative time of different posts or tweets, which is a crucial factor in deriving depression related behavior patterns. Moreover, they lack model interpretability resulting in limited understanding of how different features are contributing to the model’s final prediction. In this paper, we propose MultiExplainable TemporalNet (METN), a Temporal Convolution Network (TCN) based multi-modal transformer network with relative timestamp embeddings. We leverage pretrained foundation models for text and image embeddings and attention maps for model interpretability. We perform extensive experiments and ablation studies to validate the performance of METN for user-level depression detection task. Our model shows state-of-the-art results on various benchmarks, such as 0.945 F1 score on multimodal Twitter dataset, and 0.913 F1 score on multimodal Reddit dataset. We further demonstrate that our model enhances the accuracy of identifying depression in individuals who publicly post messages on social media platforms with enhanced interpretable compatibility. Code and models are available at Github. 1

关键词： Training Attention mechanisms Accuracy Social networking (online) Computational modeling Blogs Predictive models

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NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

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

作者： Taehoon Kim Pyunghwan Ahn Sangyun Kim Sihaeng Lee Mark Marsden Alessandra Sala Seung Hwan Kim Bohyung Han Kyoung Mu Lee Honglak Lee Kyounghoon Bae Xiangyu Wu Yi Gao Hailiang Zhang Yang Yang Weili Guo Jianfeng Lu Youngtaek Oh Jae Won Cho Dong-Jin Kim In So Kweon Junmo Kim Wooyoung Kang Won Young Jhoo Byungseok Roh Jonghwan Mun Solgil Oh Kenan Emir Ak Gwang-Gook Lee Yan Xu Mingwei Shen Kyomin Hwang Wonsik Shin Kamin Lee Wonhark Park Dongkwan Lee Nojun Kwak Yujin Wang Yimu Wang Tiancheng Gu Xingchang Lv Mingmao Sun

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project 1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.

关键词： Training Adaptation models Visualization computer vision Computational modeling conferences Training data

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DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM

DTLLM-VLT: Diverse Text Generation for Visual Language Track...

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

作者： Xuchen Li Xiaokun Feng Shiyu Hu Meiqi Wu Dailing Zhang Jing Zhang Kaiqi Huang CRISE Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Visual Language Tracking (VLT) enhances single object tracking (SOT) by integrating natural language descriptions from a video, for the precise tracking of a specified object. By leveraging high-level semantic information, VLT guides object tracking, alleviating the constraints associated with relying on a visual modality. Nevertheless, most VLT benchmarks are annotated in a single granularity and lack a coherent semantic framework to provide scientific guidance. Moreover, coordinating human annotators for high-quality annotations is laborious and time-consuming. To address these challenges, we introduce DTLLM-VLT, which automatically generates extensive and multi-granularity text to enhance environmental diversity. (1) DTLLM-VLT generates scientific and multi-granularity text descriptions using a cohesive prompt framework. Its succinct and highly adaptable design allows seamless integration into various visual tracking benchmarks. (2) We select three prominent benchmarks to deploy our approach: short-term tracking, long-term tracking, and global instance tracking. We offer four granularity combinations for these benchmarks, considering the extent and density of semantic information, thereby showcasing the practicality and versatility of DTLLM-VLT. (3) We conduct comparative experiments on VLT benchmarks with different text granularities, evaluating and analyzing the impact of diverse text on tracking performance. Conclusionally, this work leverages LLM to provide multi-granularity semantic information for VLT task from efficient and diverse perspectives, enabling fine-grained evaluation of multi-modal trackers. In the future, we believe this work can be extended to more datasets to support vision datasets understanding.

关键词： Visualization computer vision Annotations conferences Semantics Natural languages Benchmark testing

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Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise

Manifold DivideMix: A Semi-Supervised Contrastive Learning F...

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

作者： Fahimeh Fooladgar Minh Nguyen Nhat To Parvin Mousavi Purang Abolmaesumi Department of Electrical and Computer Engineering University of British Columbia CA School of Computing Queen’s University CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks have proven to be highly effective when large amounts of data with clean labels are available. However, their performance degrades when training data contains noisy labels, leading to poor generalization on the test set. Real-world datasets contain noisy label samples that either have similar visual semantics to other classes (in-distribution) or have no semantic relevance to any class (out-of-distribution) in the dataset. Most state-of-the-art methods leverage ID labeled noisy samples as unlabeled data for semi-supervised learning, but OOD labeled noisy samples cannot be used in this way because they do not belong to any class within the dataset. Hence, in this paper, we propose incorporating the information from all the training data by leveraging the benefits of self-supervised training. Our method aims to extract a meaningful and generalizable embedding space for each sample regardless of its label. Then, we employ a simple yet effective K-nearest neighbor method to remove portions of out-of-distribution samples. By discarding these samples, we propose an iterative "Manifold DivideMix" algorithm to find clean and noisy samples, and train our model in a semi-supervised way. In addition, we propose "MixEMatch", a new algorithm for the semi-supervised step that involves mixup augmentation at the input and final hidden representations of the model. This will extract better representations by interpolating both in the input and manifold spaces. Extensive experiments on multiple synthetic-noise image benchmarks and real-world web-crawled datasets demonstrate the effectiveness of our proposed framework. Code is available at https://***/Fahim-F/ManifoldDivideMix.

关键词： Manifolds Representation learning Noise Semantics Training data Clustering algorithms Contrastive learning

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Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology

Source-Free Domain Adaptation of Weakly-Supervised Object Lo...

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

作者： Alexis Guichemerre Soufiane Belharbi Tsiry Mayet Shakeeb Murtaza Pourya Shamsolmoali Luke McCaffrey Eric Granger Dept. of Systems Engineering LIVIA ETS Montreal Canada INSA LITIS UR 4108 Rouen France School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast UK Dept. of Oncology Goodman Cancer Research Centre McGill University Montreal Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by variations in staining, scanners, and cancer type. In this paper, we focus on source-free (unsupervised) domain adaptation (SFDA), a challenging problem where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. SFDA of WSOL models raises several challenges in histology, most notably because they are not intended to adapt for both classification and localization tasks. In this paper, 4 state-of-the-art SFDA methods, each one representative of a main SFDA family, are compared for WSOL in terms of classification and localization accuracy. They are the SFDA-Distribution Estimation, Source HypOthesis Transfer, Cross-Domain Contrastive Learning, and Adaptively Domain Statistics Alignment. Experimental results on the challenging Glas (smaller, breast cancer) and Came-lyon16 (larger, colon cancer) histology datasets indicate that these SFDA methods typically perform poorly for localization after adaptation when optimized for classification. Code: ***/AlexisGuichemerreCode/survey_hist_wsol_sfda

关键词： Location awareness Deep learning Adaptation models Data privacy Histopathology Estimation Contrastive learning

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