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SafeSO: Interpretable and Explainable Deep Learning Approach for Seat Occupancy Classification in Vehicle Interior

SafeSO: Interpretable and Explainable Deep Learning Approach...

引用

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

作者： Jaworek-Korjakowska, Joanna Kostuch, Aleksander Skruch, Pawel AGH Univ Sci & Technol Dept Automat Control & Robot Krakow Poland

ISBN: (纸本)9781665448994

Classification of seat occupancy in in-vehicle interior remains a significant challenge and is a promising area in the functionality of new generation cars. As majority of accidents are related to the driver errors the consequences of not wearing, or improperly wearing, a seat belt are clear. The NHTSA reports that 47% of the 22,215 passenger vehicle occupants killed in 2019 were not wearing seat belts. To address this problem we propose a deep learning based framework to classify seat occupancy into seven most important categories. In this study, we present an interpretable and explainable AI approach that takes advantage of pre-trained networks including ResNet152V2, DenseNet121 and the most recent EfficientNetB0-B5-B7 to calculate the feature vectors followed by an adjusted densely-connected classifier. Our model provides an interpretation of its results through the identification of object parts without direct supervision and their contribution towards classification. We explore and propose two new statistical metrics including HGD(score) and HGDA(score) which are based on the multivariate Gaussian distribution for assessing heatmaps without using human-annotated object parts to quantify the interpretability of our network. We demonstrate that the calculated statistical metrics lead to an interpretable model that correlates with the framework accuracy and can flexibly analyze heatmaps at any resolution for different user needs. Furthermore, extensive experiments have been performed on the SVIRO database [7] including 7,500 sceneries for BMW X5 model which confirm the ability of the developed framework based on the EfficientNetB5 architecture to classify seat occupancy into seven main categories with 79.87% overall accuracy as well as 95.92% recall and 90.32% specificity for empty seats recognition, which is a state-of-the-art result in this domain.

关键词： Measurement Heating systems Deep learning Visualization computer architecture Belts Object recognition

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GAN-based vision Transformer for High-Quality Thermal Image Enhancement

GAN-based Vision Transformer for High-Quality Thermal Image ...

引用

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

作者： Mohamed Amine Marnissi Abir Fathallah Ecole Nationale d’Ingénieurs de Sfax Université de Sfax Sfax Tunisie Samovar CNRS Télécom SudParis Institut Polytechnique de Paris Evry Cedex France

Generative Adversarial Networks (GANs) have shown an outstanding ability to generate high-quality images with visual realism and similarity to real images. This paper presents a new architecture for thermal image enhancement. Precisely, the strengths of architecture-based vision transformers and generative adversarial networks are exploited. The thermal loss feature introduced in our approach is specifically used to produce high-quality images. Thermal image enhancement also relies on fine-tuning based on visible images, resulting in an overall improvement in image quality. A visual quality metric was used to evaluate the performance of the proposed architecture. Significant improvements were found over the original thermal images and other enhancement methods established on a subset of the KAIST dataset. The performance of the proposed enhancement architecture is also verified on the detection results by obtaining better performance with a considerable margin regarding different versions of the YOLO detector.

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Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models

Strategies to Improve Real-World Applicability of Laparoscop...

引用

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

作者： Fiona R. Kolbinger Jiangpeng He Jinge Ma Fengqing Zhu Purdue University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support. Segmentation performance of such models is traditionally reported using metrics of overlap such as IoU. However, imbalanced and unrealistic representation of classes in the training data and suboptimal selection of reported metrics have the potential to skew nominal segmentation performance and thereby ultimately limit clinical translation. In this work, we systematically analyze the impact of class characteristics (i.e., organ size differences), training and test data composition (i.e., representation of positive and negative examples), and modeling parameters (i.e., foreground-to-background class weight) on eight segmentation metrics: accuracy, precision, recall, IoU, F1 score (Dice Similarity Coefficient), specificity, Hausdorff Distance, and Average Symmetric Surface Distance. Our findings support two adjustments to account for data biases in surgical data science: First, training on datasets that are similar to the clinical real-world scenarios in terms of class distribution, and second, class weight adjustments to optimize segmentation model performance with regard to metrics of particular relevance in the respective clinical setting.

关键词： Measurement Training Laparoscopes Image segmentation computer vision Computational modeling Surgery

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PromptSync: Bridging Domain Gaps in vision-Language Models through Class-Aware Prototype Alignment and Discrimination

PromptSync: Bridging Domain Gaps in Vision-Language Models t...

引用

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

作者： Anant Khandelwal Glance AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The potential for zero-shot generalization in vision-language (V-L) models such as CLIP has spurred their widespread adoption in addressing numerous downstream tasks. Previous methods have employed test-time prompt tuning to adapt the model to unseen domains, but they overlooked the issue of imbalanced class distributions. In this study, we explicitly address this problem by employing class-aware prototype alignment weighted by mean class probabilities obtained for the test sample and filtered augmented views. Additionally, we ensure that the class probabilities are as accurate as possible by performing prototype discrimination using contrastive learning. The combination of alignment and discriminative loss serves as a geometric regularizer, preventing the prompt representation from collapsing onto a single class and effectively bridging the distribution gap between the source and test domains. Our method, named PromptSync, synchronizes the prompts for each test sample on both the text and vision branches of the V-L model. In empirical evaluations on the domain generalization benchmark, our method outperforms previous best methods by 2.33% in overall performance, by 1% in base-to-novel generalization, and by 2.84% in cross-dataset transfer tasks.

关键词： Adaptation models Computational modeling conferences Prototypes Contrastive learning Benchmark testing Robustness

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Towards Evaluating Explanations of vision Transformers for Medical Imaging

Towards Evaluating Explanations of Vision Transformers for M...

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

作者： Piotr Komorowski Hubert Baniecki Przemysław Biecek University of Warsaw Warsaw University of Technology

As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount. Many explainability methods provide insights into how these models make predictions by attributing importance to input features. As vision Transformer (ViT) becomes a promising alternative to convolutional neural networks for image classification, its interpretability remains an open research question. This paper investigates the performance of various interpretation methods on a ViT applied to classify chest X-ray images. We introduce the notion of evaluating faithfulness, sensitivity, and complexity of ViT explanations. The obtained results indicate that Layerwise relevance propagation for transformers outperforms Local interpretable model-agnostic explanations and Attention visualization, providing a more accurate and reliable representation of what a ViT has actually learned. Our findings provide insights into the applicability of ViT explanations in medical imaging and highlight the importance of using appropriate evaluation criteria for comparing them.

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LatentMan : Generating Consistent Animated Characters using Image Diffusion Models

LatentMan : Generating Consistent Animated Characters using ...

引用

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

作者： Abdelrahman Eldesokey Peter Wonka KAUST Saudi Arabia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a zero-shot approach for generating consistent videos of animated characters based on Text-to-Image (T2I) diffusion models. Existing Text-to-Video (T2V) methods are expensive to train and require large-scale video datasets to produce diverse characters and motions. At the same time, their zero-shot alternatives fail to produce temporally consistent videos with continuous motion. We strive to bridge this gap, and we introduce LATENTMAN that leverages existing text-based motion diffusion models to generate diverse continuous motions to guide the T2I model. To boost the temporal consistency, we introduce the Spatial Latent Alignment module that exploits cross-frame dense correspondences that we compute to align the latents of the video frames. Furthermore, we propose Pixel-Wise Guidance to steer the diffusion process in a direction that minimizes visual discrepancies between frames. Our proposed approach outperforms existing zero-shot T2V approaches in generating videos of animated characters in terms of pixel-wise consistency and user preference.

关键词： Bridges Visualization computer vision conferences Computational modeling Text to image Diffusion processes

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Gaze Scanpath Transformer: Predicting Visual Search Target by Spatiotemporal Semantic Modeling of Gaze Scanpath

Gaze Scanpath Transformer: Predicting Visual Search Target b...

引用

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

作者： Takumi Nishiyasu Yoichi Sato The University of Tokyo Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce a new method, called the Gaze Scanpath Transformer, for predicting a search target category during a visual search task. Previous methods for estimating visual search targets focus solely on the image features at gaze fixation positions. As a result, the previous methods are unable to take into account the spatiotemporal information of gaze scanpaths and lack consideration of the semantic interrelationships between objects on gaze fixations. In contrast, our method can estimate a visual search target based on the spatiotemporal information of a gaze scanpath and interrelationships among image semantic features at gaze fixation positions. This is achieved by embedding the position and the duration of each fixation, and the order of fixations into the image semantic features, and by using the model’s attention mechanism to facilitate information exchange and emphasis on image semantic features at gaze fixation positions. Evaluation using the COCO-Search18 dataset demonstrates that our proposed method achieves significant performance improvements over other state-ofthe-art baseline models for search target prediction.

关键词： Visualization computer vision conferences Computational modeling Semantics Predictive models Transformers

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Efficient Light Field Image Super-Resolution via Progressive Disentangling

Efficient Light Field Image Super-Resolution via Progressive...

引用

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

作者： Gaosheng Liu Huanjing Yue Jingyu Yang School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The performance of light field (LF) image super-resolution (SR) has been significantly improved with the development of deep learning techniques. In recent state-of-the-art methods, increasingly deeper and wider networks with a massive number of layers are employed to improve SR performance. However, these approaches often incur heavy computational costs, hindering efficient inference and practical applications. In this paper, we address the problem by introducing an efficient network for LF image SR. Specifically, we propose an efficient progressive disentangling block (PDistgB), where the intermediate LF feature is progressively channel-wise split and selectively domain-specific disentangled. The PDistgB can well incorporate the LF structure prior while requiring fewer computational costs compared with existing disentangling strategies. In addition, we apply Transformer on the angular domain to incorporate angular correlations from all views for further improving the SR accuracy. Experimental results on public datasets demonstrate that our method achieves state-of-the-art performance with high efficiency. Codes and models are available at https://***/GaoshengLiu/PDistgNet.

关键词： Deep learning computer vision Correlation conferences Computational modeling Superresolution Transformers

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VMRNN: Integrating vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting

VMRNN: Integrating Vision Mamba and LSTM for Efficient and A...

引用

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

作者： Yujin Tang Peijie Dong Zhenheng Tang Xiaowen Chu Junwei Liang AI Thrust The Hong Kong University of Science and Technology (Guangzhou) DSA Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Computer Science Hong Kong Baptist University Department of Computer Science and Engineering The Hong Kong University of Science and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Combining Convolutional Neural Networks (CNNs) or vision Transformers(ViTs) with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded unparalleled results in predicting temporal and spatial dynamics. However, modeling extensive global information remains a formidable challenge; CNNs are limited by their narrow receptive fields, and ViTs struggle with the intensive computational demands of their attention mechanisms. The emergence of recent Mamba-based architectures has been met with enthusiasm for their exceptional long-sequence modeling capabilities, surpassing established vision models in efficiency and accuracy, which motivates us to develop an innovative architecture tailored for spatiotemporal forecasting. In this paper, we propose the VMRNN cell, a new recurrent unit that integrates the strengths of vision Mamba blocks with LSTM. We construct a network centered on VMRNN cells to tackle spatiotemporal prediction tasks effectively. Our extensive evaluations show that our proposed approach secures competitive results on a variety of tasks while maintaining a smaller model size. Our code is available at https://***/yyyujintang/VMRNN-PyTorch.

关键词： Recurrent neural networks Accuracy Computational modeling Microprocessors computer architecture Predictive models Transformers

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VLM-PL: Advanced Pseudo Labeling approach for Class Incremental Object Detection via vision-Language Model

VLM-PL: Advanced Pseudo Labeling approach for Class Incremen...

引用

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

作者： Junsu Kim Yunhoe Ku Jihyeon Kim Junuk Cha Seungryul Baek UNIST MODULABS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of Class Incremental Object Detection (CIOD), creating models that can continuously learn like humans is a major challenge. Pseudo-labeling methods, although initially powerful, struggle with multi-scenario incremental learning due to their tendency to forget past knowledge. To overcome this, we introduce a new approach called vision-Language Model assisted Pseudo-Labeling (VLM-PL). This technique uses vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training. VLM-PL starts by deriving pseudo GTs from a pre-trained detector. Then, we generate custom queries for each pseudo GT using carefully designed prompt templates that combine image and text features. This allows the VLM to classify the correctness through its responses. Furthermore, VLM-PL integrates refined pseudo and real GTs from upcoming training, effectively combining new and old knowledge. Extensive experiments conducted on the Pascal VOC and MS COCO datasets not only highlight VLM-PL’s exceptional performance in multi-scenario but also illuminate its effectiveness in dual-scenario by achieving state-of-the-art results in both.

关键词： Training computer vision Incremental learning conferences Computational modeling Object detection Detectors

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