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Automatic recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor

Automatic Recognition of Food Ingestion Environment from the...

引用

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

作者： Huang, Yuning Hassan, M. A. He, Jiangpeng Higgins, J. McCrory, Megan Eicher-Miller, Heather Thomas, J. Graham Sazonov, Edward Zhu, Fengqing Purdue Univ W Lafayette IN 47907 USA Univ Calif Davis Davis CA 95616 USA Univ Colorado Aurora CO USA Boston Univ Boston MA 02215 USA Brown Univ Providence RI 02912 USA Univ Alabama Tuscaloosa AL USA

ISBN: (纸本)9798350365474

Detecting an ingestion environment is an important aspect of monitoring dietary intake. It provides insightful information for dietary assessment. However, it is a challenging problem where human-based reviewing can be tedious, and algorithm-based review suffers from data imbalance and perceptual aliasing problems. To address these issues, we propose a neural network-based method with a two-stage training framework that tactfully combines fine-tuning and transfer learning techniques. Our method is evaluated on a newly collected dataset called "UA Free Living Study", which uses an egocentric wearable camera, AIM-2 sensor, to simulate food consumption in free-living conditions. The proposed training framework is applied to common neural network backbones, combined with approaches in the general imbalanced classification field. Experimental results on the collected dataset show that our proposed method for automatic ingestion environment recognition successfully addresses the challenging data imbalance problem in the dataset and achieves a promising overall classification accuracy of 96.63%.

关键词： Classification Dietary Assessment Scene recognition Transfer Learning

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Neural Video Compression with Diverse Contexts

Neural Video Compression with Diverse Contexts

引用

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

作者： Li, Jiahao Li, Bin Lu, Yan Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

For any video codecs, the coding efficiency highly relies on whether the current signal to be encoded can find the relevant contexts from the previous reconstructed signals. Traditional codec has verified more contexts bring substantial coding gain, but in a time-consuming manner. However, for the emerging neural video codec (NVC), its contexts are still limited, leading to low compression ratio. To boost NVC, this paper proposes increasing the context diversity in both temporal and spatial dimensions. First, we guide the model to learn hierarchical quality patterns across frames, which enriches long-term and yet high-quality temporal contexts. Furthermore, to tap the potential of optical flow-based coding framework, we introduce a group-based offset diversity where the cross-group interaction is proposed for better context mining. In addition, this paper also adopts a quadtree-based partition to increase spatial context diversity when encoding the latent representation in parallel. Experiments show that our codec obtains 23.5% bitrate saving over previous SOTA NVC. Better yet, our codec has surpassed the under-developing next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in terms of PSNR. The codes are at https://***/microsoft/DCVC.

关键词： Low-level vision

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NIPQ: Noise proxy-based Integrated Pseudo-Quantization

NIPQ: Noise proxy-based Integrated Pseudo-Quantization

引用

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

作者： Shin, Juncheol So, Junhyuk Park, Sein Kang, Seungyeop Yoo, Sungjoo Park, Eunhyeok POSTECH Dept Comp Sci & Engn Pohang South Korea POSTECH Grad Sch Artificial Intelligence Pohang South Korea Seoul Natl Univ Dept ent Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350301298

Straight-through estimator (STE), which enables the gradient flow over the non-differentiable function via approximation, has been favored in studies related to quantization-aware training (QAT). However, STE incurs unstable convergence during QAT, resulting in notable quality degradation in low precision. Recently, pseudo-quantization training has been proposed as an alternative approach to updating the learnable parameters using the pseudo-quantization noise instead of STE. In this study, we propose a novel noise proxy-based integrated pseudo-quantization (NIPQ) that enables unified support of pseudoquantization for both activation and weight by integrating the idea of truncation on the pseudo-quantization framework. NIPQ updates all of the quantization parameters (e.g., bit-width and truncation boundary) as well as the network parameters via gradient descent without STE instability. According to our extensive experiments, NIPQ outperforms existing quantization algorithms in various vision and language applications by a large margin.

关键词： Efficient and scalable vision

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Interpreting COVID Lateral Flow Tests' Results with Foundation Models

Interpreting COVID Lateral Flow Tests' Results with Foundati...

引用

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

作者： Pandey, Stuti Myers-Dean, Josh Reynolds, Jarek Gurari, Danna Univ Colorado Boulder CO 80309 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350365474

Lateral flow tests (LFTs) enable rapid, low-cost testing for health conditions including Covid, pregnancy, HIV, and malaria. Automated readers of LFT results can yield many benefits including empowering blind people to independently learn about their health and accelerating data entry for large-scale monitoring (e.g., for pandemics such as Covid) by using only a single photograph per LFT test. Accordingly, we explore the abilities of modern foundation vision language models (VLMs) in interpreting such tests. To enable this analysis, we first create a new labeled dataset with hierarchical segmentations of each LFT test and its nested test result window. We call this dataset LFT-Grounding. Next, we benchmark eight modern VLMs in zero-shot settings for analyzing these images. We demonstrate that current VLMs frequently fail to correctly identify the type of LFT test, interpret the test results, locate the nested result window of the LFT tests, and recognize LFT tests when they partially obfuscated. To facilitate community-wide progress towards automated LFT reading, we publicly release our dataset at https://***/ lft_grounding_foundation_models/

关键词： Accessibility Foundation vision Language Models Lateral Flow Test Prompt Engineering Zero-Shot

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FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures

FFF: Fragment-Guided Flexible Fitting for Building Complete ...

引用

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

作者： Chen, Weijie Wang, Xinyan Wang, Yuhang DP Technol Ltd Camarillo CA 93012 USA Peking Univ Ctr Data Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, recognition-based de novo building methods have shown the potential to streamline this process. However, it cannot build a complete structure due to the low signal-to-noise ratio (SNR) problem. At the same time, AlphaFold has led to a great breakthrough in predicting protein structures. This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure. In this paper, we propose a new method named FFF that bridges protein structure prediction and protein structure recognition with flexible fitting. First, a multi-level recognition network is used to capture various structural features from the input 3D cryo-EM map. Next, protein structural fragments are generated using pseudo peptide vectors and a protein sequence alignment method based on these extracted features. Finally, a complete structural model is constructed using the predicted protein fragments via flexible fitting. Based on our benchmark tests, FFF outperforms the baseline methods for building complete protein structures.

关键词： detection recognition: Categorization retrieval

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Emotic Masked Autoencoder on Dual-views with Attention Fusion for Facial Expression recognition

Emotic Masked Autoencoder on Dual-views with Attention Fusio...

引用

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

作者： Xuan-Bach Nguyen Hoang-Thien Nguyen Thanh-Huy Nguyen Nhu-Tai Do Quang Vinh Dinh Ho Chi Minh City Univ Technol Ho Chi Minh City Vietnam Posts & Telecommun Inst Technol Ho Chi Minh City Vietnam Ho Chi Minh City Univ Educ Ho Chi Minh City Vietnam Univ Econ Ho Chi Minh City UEH Vietnam Ho Chi Minh City Vietnam Vietnamese German Univ Binh Duong Vietnam

ISBN: (纸本)9798350365474

Facial Expression recognition (FER) is a critical task within computer vision with diverse applications across various domains. Addressing the challenge of limited FER datasets, which hampers the generalization capability of expression recognition models, is imperative for enhancing performance. Our paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification, particularly showcased in the 6th Affective Behavior Analysis in-the-wild (ABAW) competition. By utilizing low-level feature information from the ipsilateral view (auxiliary view) before learning the highlevel feature that emphasizes the shift in the human facial expression, our work seeks to provide a straightforward yet innovative way to improve the examined view (main view). We also suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model, focusing on pivotal local elements. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset, as observed in both training and validation contexts.

关键词： Self-supervised learning

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Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning

Gradient-based Uncertainty Attribution for Explainable Bayes...

引用

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

作者： Wang, Hanjing Joshi, Dhiraj Wang, Shiqiang Ji, Qiang Rensselaer Polytech Inst Troy NY 12180 USA IBM Res Armonk NY USA

ISBN: (纸本)9798350301298

Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critical to accurately quantify the prediction uncertainties. While current efforts focus on improving uncertainty quantification accuracy and efficiency, there is a need to identify uncertainty sources and take actions to mitigate their effects on predictions. Therefore, we propose to develop explainable and actionable Bayesian deep learning methods to not only perform accurate uncertainty quantification but also explain the uncertainties, identify their sources, and propose strategies to mitigate the uncertainty impacts. Specifically, we introduce a gradient-based uncertainty attribution method to identify the most problematic regions of the input that contribute to the prediction uncertainty. Compared to existing methods, the proposed UA-Backprop has competitive accuracy, relaxed assumptions, and high efficiency. Moreover, we propose an uncertainty mitigation strategy that leverages the attribution results as attention to further improve the model performance. Both qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of our proposed methods.

关键词： Explainable computer vision

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ScaleDet: A Scalable Multi-Dataset Object Detector

ScaleDet: A Scalable Multi-Dataset Object Detector

引用

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

作者： Chen, Yanbei Wang, Manchen Mittal, Abhay Xu, Zhenlin Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350301298

Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset detector (ScaleDet) that can scale up its generalization across datasets when increasing the number of training datasets. Unlike existing multi-dataset learners that mostly rely on manual relabelling efforts or sophisticated optimizations to unify labels across datasets, we introduce a simple yet scalable formulation to derive a unified semantic label space for multi-dataset training. ScaleDet is trained by visual-textual alignment to learn the label assignment with label semantic similarities across datasets. Once trained, ScaleDet can generalize well on any given upstream and downstream datasets with seen and unseen classes. We conduct extensive experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and 13 datasets from Object Detection in the Wild (ODinW) as downstream datasets. Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the same backbone.

关键词： Efficient and scalable vision

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Regularization of polynomial networks for image recognition

Regularization of polynomial networks for image recognition

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

作者： Chrysos, Grigorios G. Wang, Bohan Deng, Jiankang Cevher, Volkan Ecole Polytech Fed Lausanne LIONS Lausanne Switzerland Huawei UKRD Cambridge England

ISBN: (纸本)9798350301298

Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising performance and improved interpretability but have yet to reach the performance of the powerful DNN baselines. In this work, we aim to close this performance gap. We introduce a class of PNs, which are able to reach the performance of ResNet across a range of six benchmarks. We demonstrate that strong regularization is critical and conduct an extensive study of the exact regularization schemes required to match performance. To further motivate the regularization schemes, we introduce D-PolyNets that achieve a higher-degree of expansion than previously proposed polynomial networks. D-PolyNets are more parameter-efficient while achieving a similar performance as other polynomial networks. We expect that our new models can lead to an understanding of the role of elementwise activation functions (which are no longer required for training PNs). The source code is available at https:// ***/grigorisg9gr/regularized_polynomials.

关键词： Deep learning architectures and techniques

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QAttn: Efficient GPU Kernels for mixed-precision vision Transformers

QAttn: Efficient GPU Kernels for mixed-precision Vision Tran...

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

作者： Kluska, Piotr Castello, Adrian Scheidegger, Florian Malossi, A. Cristiano I. Quintana-Orti, Enrique S. IBM Res Europe Ruschlikon Switzerland Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9798350365474

vision Transformers have demonstrated outstanding performance in computer vision tasks. Nevertheless, this superior performance for large models comes at the expense of increasing memory usage for storing the parameters and intermediate activations. To accelerate model inference, in this work we develop and evaluate integer and mixed-precision kernels in Triton for the efficient execution of two fundamental building blocks of transformers -linear layer and attention- on graphics processing units (GPUs). On an NVIDIA A100 GPU, our kernel implementations of vision Transformers achieve a throughput speedup of up to 7x compared with reference kernels in PyTorch floating-point single precision (FP32). Additionally, the accuracy for the ViT Large model top-1 drops by less than one percent on the ImageNet1K classification task. We also observe up to 6x increased throughput by applying our kernels to the Segment Anything Model image encoder while keeping the mIOU close to the FP32 reference on the COCO2017 dataset for static and dynamic quantization. Furthermore, our kernels demonstrate improved speed to the TensorRT INT8 linear layer, and we improve the throughput of base FP16 (half precision) Triton attention on average by up to 19 +/- 4.01%. We have open-sourced the QAtnn framework, which is tightly integrated with the PyTorch quantization workflow https://***/IBM/qattn.

关键词： compression instance segmentation object classification quantization vision transformers

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