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Unsupervised Change Detection Based on Image Reconstruction Loss

Unsupervised Change Detection Based on Image Reconstruction ...

引用

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

作者： Noh, Hyeoncheol Ju, Jingi Seo, Minseok Park, Jongchan Choi, Dong-Geol Hanbat Natl Univ Daejeon South Korea SI Analyt Inc Mainz Germany Lunit Inc Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

To train a change detector, bi-temporal images taken at different times in the same area are used. However, collecting labeled bi-temporal images is expensive and time consuming. To solve this problem, various unsupervised change detection methods have been proposed, but they still require unlabeled bi-temporal images. In this paper, we propose an unsupervised change detection method based on image reconstruction loss, which uses only a single-temporal unlabeled image. The image reconstruction model was trained to reconstruct the original source image by receiving the source image and photometrically transformed source image as a pair. During inference, the model receives bi-temporal images as input and aims to reconstruct one of the inputs. The changed region between bi-temporal images shows high reconstruction loss. Our change detector demonstrated significant performance on various change detection benchmark datasets even though only a single-temporal source image was used. The code and trained models are available in https://***/cjf8899/CDRL

关键词： computer vision Codes conferences Semantics Detectors Benchmark testing pattern recognition

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S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction

S2F2: Single-Stage Flow Forecasting for Future Multiple Traj...

引用

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

作者： Chen, Yu-Wen Yang, Hsuan-Kung Chiu, Chu-Chi Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this work, we present a single-stage framework, named S2F2, for forecasting multiple human trajectories from raw video images by predicting future optical flows. S2F2 differs from the previous two-stage approaches in that it performs detection, Re-ID, and forecasting of multiple pedestrians at the same time. Unlike the prior approaches, the computational burden of S2F2 remains consistent even as the number of pedestrians grows. The experimental results demonstrate that S2F2 is able to outperform two conventional forecasting algorithms and a recent learning-based two-stage model [1], while maintaining its tracking performance on par with the contemporary MOT models.

关键词： computer vision Computational modeling conferences Predictive models Prediction algorithms Trajectory pattern recognition

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vision DiffMask: Faithful Interpretation of vision Transformers with Differentiable Patch Masking

Vision DiffMask: Faithful Interpretation of Vision Transform...

引用

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

作者： Nalmpantis, Angelos Panagiotopoulos, Apostolos Gkountouras, John Papakostas, Konstantinos Aziz, Wilker University of Amsterdam Netherlands

ISBN: (纸本)9798350302493

The lack of interpretability of the vision Transformer may hinder its use in critical real-world applications despite its effectiveness. To overcome this issue, we propose a post-hoc interpretability method called vision DiffMask, which uses the activations of the model's hidden layers to predict the relevant parts of the input that contribute to its final predictions. Our approach uses a gating mechanism to identify the minimal subset of the original input that preserves the predicted distribution over classes. We demonstrate the faithfulness of our method, by introducing a faithfulness task, and comparing it to other state-of-the-art attribution methods on CIFAR-10 and ImageNet-1K, achieving compelling results. To aid reproducibility and further extension of our work, we open source our implementation here. © 2023 ieee.

关键词： computer vision

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Lidar Positioning for Indoor Precision Navigation

Lidar Positioning for Indoor Precision Navigation

引用

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

作者： Holmberg, Max Karlsson, Oskar Tulldahl, Michael Swedish Def Res Agcy FOI Linkoping Sweden

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Lidar based simultaneous localization and mapping methods can be adapted for deployment on small autonomous vehicles operating in unmapped indoor environments. For this purpose, we propose a method which combines inertial data, low-drift lidar odometry, planar primitives, and loop closing in a graph-based structure. The accuracy of our method is experimentally evaluated, using a high-resolution lidar, and compared to the state-of-the-art methods LIO-SAM and Cartographer. We specifically address the lateral positioning accuracy when passing through narrow openings, where high accuracy is a prerequisite for safe operation of autonomous vehicles. The test cases include doorways, slightly wider reference passages, and a larger corridor environment. We observe a reduced lateral accuracy for all three methods when passing through the narrow openings compared to operation in larger spaces. Compared to state-of-the-art, our method shows better results in the narrow passages, and comparable results in the other environments with reasonably low usage of CPU and memory resources.

关键词： computer vision Laser radar Simultaneous localization and mapping Navigation conferences Random access memory pattern recognition

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FairCLIP: Harnessing Fairness in vision-Language Learning

FairCLIP: Harnessing Fairness in Vision-Language Learning

引用

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

作者： Luol, Yan Shil, Min Khan, Muhammad Osama Afzal, Muhammad Muneeb Huang, Hao Yuan, Shuaihang Tian, Yu Song, Luo Kouhana, Ava Elze, Tobias Fang, Yi Wang, Mengyu Harvard Univ Harvard Ophthalmol AI Lab Cambridge MA 02138 USA NYU Tandon Sch Engn New York NY USA New York Univ Abu Dhabi Multimedia & Visual Comp Lab Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350353006

Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions. Although fairness has been investigated in the vision-only domain, the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of medical VL datasets for studying fairness. To bridge this research gap, we introduce the first fair vision-language medical dataset (Harvard-FairVLMed) that provides detailed demographic attributes, ground-truth labels, and clinical notes to facilitate an in-depth examination of fairness within VL foundation models. Using Harvard-FairVLMed, we conduct a comprehensive fairness analysis of two widely-used VL models (CLIP and BLIP2), pre-trained on both natural and medical domains, across four different protected attributes. Our results highlight significant biases in all VL models, with Asian, Male, Non-Hispanic, and Spanish being the preferred subgroups across the protected attributes of race, gender, ethnicity, and language, respectively. In order to alleviate these biases, we propose FairCLIP, an optimal-transport-based approach that achieves a favorable trade-off between performance and fairness by reducing the Sinkhorn distance between the overall sample distribution and the distributions corresponding to each demographic group. As the first VL dataset of its kind, Harvard-FairVLMed holds the potential to catalyze advancements in the development of machine learning models that are both ethically aware and clinically effective. Our dataset and code are available at https://***/datasets/

关键词： AI for Medicine Equitable Deep Learning Fairness Learning Large Language Models Medical AI Ophthalmology Optimal Transport vision-Language Learning

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Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

Searching for Efficient Neural Architectures for On-Device M...

引用

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

作者： Akin, Berkin Gupta, Suyog Long, Yun Spiridonov, Anton Wang, Zhuo White, Marie Xu, Hao Zhou, Ping Zhou, Yanqi

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. However, existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms. In this work, we provide a two-pronged approach to this challenge: (i) a NAS-enabling infrastructure that decouples model cost evaluation, search space design, and the NAS algorithm to rapidly target various on-device ML tasks, and (ii) search spaces crafted from group convolution based inverted bottleneck (IBN) variants that provide flexible quality/performance trade-offs on ML accelerators, complementing the existing full and depthwise convolution based IBNs. Using this approach we target a state-of-the-art mobile platform, Google Tensor SoC, and demonstrate neural architectures1 that improve the quality-performance pareto frontier for various computer vision (classification, detection, segmentation) as well as natural language processing tasks.

关键词： computer vision Tensors Costs Convolution Image edge detection computer architecture Throughput

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Cascaded Siamese Self-supervised Audio to Video GAN

Cascaded Siamese Self-supervised Audio to Video GAN

引用

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

作者： Aldausari, Nuha Sowmya, Arcot Marcus, Nadine Mohammadi, Gelareh Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generating meaningful videos that are synchronised to audio signals is a complex synthesis task that requires generation of not only realistic videos but also coherent video motions that conform to the provided audio signals. While tremendous effort has been expended on audio-to-video generative models, these models rely heavily on supervised signals such as face/body key points or 3D meshes. However, key point annotation requires time and effort. Besides, some dataset domains do not have predictable structure, which makes the extraction of points of interest infeasible. Our proposed model consists of a cascaded generator-discriminator architecture that works at the pixel level to generate videos according to the associated soundtracks. It adopts a new self-supervised temporal augmentation technique to optimise the correlation between the audio signal and the generated video instead of relying on supervised signals. The proposed architecture has proven its effectiveness in extensive experiments that compared different models across two datasets.

关键词： Solid modeling Correlation Three-dimensional displays Computational modeling conferences computer architecture pattern recognition

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Joint Forecasting of Panoptic Segmentations with Difference Attention

Joint Forecasting of Panoptic Segmentations with Difference ...

引用

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

作者： Graber, Colin Jazra, Cyril Luo, Wenjie Gui, Liangyan Schwing, Alexander Univ Illinois Urbana IL 61801 USA Waymo Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665487399

Forecasting of a representation is important for safe and effective autonomy. For this, panoptic segmentations have been studied as a compelling representation in recent work. However, recent state-of-the-art on panoptic segmentation forecasting suffers from two issues: first, individual object instances are treated independently of each other;second, individual object instance forecasts are merged in a heuristic manner. To address both issues, we study a new panoptic segmentation forecasting model that jointly forecasts all object instances in a scene using a transformer model based on 'difference attention.' It further refines the predictions by taking depth estimates into account. We evaluate the proposed model on the Cityscapes and AIODrive datasets. We find difference attention to be particularly suitable for forecasting because the difference of quantities like locations enables a model to explicitly reason about velocities and acceleration. Because of this, we attain state-of-the-art on panoptic segmentation forecasting metrics.

关键词： Measurement computer vision conferences Predictive models Transformers pattern recognition Forecasting

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Unstructured Object Matching using Co-Salient Region Segmentation

Unstructured Object Matching using Co-Salient Region Segment...

引用

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

作者： Stoian, Ioana-Sabina Sandu, Ionut-Catalin Voinea, Daniel Popa, Alin-Ionut Amazon Bucharest Romania

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unstructured object matching is a less-explored and very challenging topic in the scientific literature. This includes matching scenarios where the context, appearance and the geometrical integrity of the objects to be matched changes drastically from one image to another (e.g. a pair of pyjamas which in one image is folded and in the other is worn by a person), making it impossible to determine a transformation which aligns the matched regions. Traditional approaches like keypoint-based feature matching perform poorly on this use case due to the high complexity in terms of viewpoint, scene context variety, background variations or high degrees of freedom concerning structural configurations. In this paper we propose a deep learning framework consisting of a twins based matching approach leveraging a co-salient region segmentation task and a cosine-similarity based region descriptor pairing technique. The importance of our proposed framework is demonstrated on a novel use case consisting of image pairs with various objects used by children. Additionally, we evaluate on Human3.6M and Market-1501, two datasets with humans depicting various appearances and kinematic configurations captured under different backgrounds.

关键词： Deep learning Image segmentation computer vision conferences Kinematics Search problems pattern recognition

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ViLa-MIL: Dual-scale vision-Language Multiple Instance Learning for Whole Slide Image Classification

ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learn...

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

作者： Shi, Jiangbo Li, Chen Gong, Tieliang Zheng, Yefeng Fu, Huazhu Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Jarvis Res Ctr Tencent YouTu Lab Shenzhen Peoples R China Agcy Sci Technol & Res Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9798350353006

Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI) with giga-pixel size and hierarchical image context in digital pathology. However, these methods heavily depend on a substantial number of bag-level labels and solely learn from the original slides, which are easily affected by variations in data distribution. Recently, vision language model (VLM)-based methods introduced the language prior by pre-training on large-scale pathological image-text pairs. However, the previous text prompt lacks the consideration of pathological prior knowledge, therefore does not substantially boost the model's performance. Moreover, the collection of such pairs and the pre-training process are very time-consuming and source-intensive. To solve the above problems, we propose a dual-scale vision-language multiple instance learning (ViLa-MIL) framework for whole slide image classification. Specifically, we propose a dual-scale visual descriptive text prompt based on the frozen large language model (LLM) to boost the performance of VLM effectively. To transfer the VLM to pro-cess WSI efficiently, for the image branch, we propose a prototype-guided patch decoder to aggregate the patch features progressively by grouping similar patches into the same prototype;for the text branch, we introduce a context-guided text decoder to enhance the text features by incorporating the multi-granular image contexts. Extensive studies on three multi-cancer and multi-center subtyping datasets demonstrate the superiority of ViLa-MIL.

关键词： Histopathology Multiple Instance Learning vision-Language Models Whole Slide Image Classification

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