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BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

BCI: Breast Cancer Immunohistochemical Image Generation thro...

引用

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

作者： Liu, Shengjie Zhu, Chuang Xu, Feng Jia, Xinyu Shi, Zhongyue Jin, Mulan Beijing Univ Posts & Telecommun Beijing Peoples R China Capital Med Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels. Based on BCI, as a minor contribution, we further build a pyramid pix2pix image generation method, which achieves better HE to IHC translation results than the other current popular algorithms. Extensive experiments demonstrate that BCI poses new challenges to the existing image translation research. Besides, BCI also opens the door for future pathology studies in HER2 expression evaluation based on the synthesized IHC images. BCI dataset can be downloaded from https://***/BCI.

关键词： Pathology computer vision Image synthesis conferences Breast tissue Epidermis Breast cancer

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Delta Sampling R-BERT for limited data and low-light action recognition

Delta Sampling R-BERT for limited data and low-light action ...

引用

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

作者： Hira, Sanchit Das, Ritwik Modi, Abhinav Pakhomov, Daniil Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset[60]. Most previous works only evaluate performance on large, well illuminated datasets like Kinetics and HMDB51. We demonstrate that our work is able to achieve a very low error rate while being trained on a much smaller dataset of dark videos. We also explore a variety of training and inference strategies including domain transfer methodologies and also propose a simple but useful frame selection strategy. Our empirical results demonstrate that we beat previously published baseline models by 11%.

关键词： Training Deep learning Technological innovation Schedules computer vision conferences pattern recognition

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TinyOps: ImageNet Scale Deep Learning on Microcontrollers

TinyOps: ImageNet Scale Deep Learning on Microcontrollers

引用

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

作者： Sadiq, Sulaiman Hare, Jonathon Maji, Partha Craske, Simon Merrett, Geoff, V Univ Southampton Southampton Hants England ARM Ltd Cambridge England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep Learning on microcontroller (MCU) based IoT devices is extremely challenging due to memory constraints. Prior approaches focus on using internal memory or external memories exclusively which limit either accuracy or latency. We find that a hybrid method using internal and external MCU memories outperforms both approaches in accuracy and latency. We develop TinyOps, an inference engine which accelerates inference latency of models in slow external memory, using a partitioning and overlaying scheme via the available Direct Memory Access (DMA) peripheral to combine the advantages of external memory (size) and internal memory (speed). Experimental results show that architectures deployed with TinyOps significantly outperform models designed for internal memory with up to 6% higher accuracy and importantly, 1.3-2.2x faster inference latency to set the state-of-the-art in TinyML ImageNet classification. Our work shows that the TinyOps space is more efficient compared to the internal or external memory design spaces and should be explored further for TinyML applications.

关键词： Deep learning computer vision Microcontrollers conferences Memory management pattern recognition Internet of Things

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Nonuniformly Dehaze Network for Visible Remote Sensing Images

Nonuniformly Dehaze Network for Visible Remote Sensing Image...

引用

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

作者： Chen, Zhaojie Li, Qi Feng, Huajun Xu, Zhihai Chen, Yueting Zhejiang Univ Coll Opt Sci & Engn Hangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Nonuniform haze on remote sensing images degrades image quality and hinders many high-level tasks. In this paper, we propose a Nonuniformly Dehaze Network towards nonuniform haze on visible remote sensing images. To extract robust haze-aware features, we propose Nonuniformly Excite (NE) module. Inspired by the well-known gather-excite attention module, NE module works in a map-excite manner. In the map operation, we utilize a proposed Dual Attention Dehaze block to extract local enhanced features. In the gather operation, we utilize a strided deformable convolution to nonuniformly process features and extract nonlocal haze-aware features. In the excite operation, we employ a pixel-wise attention between local enhanced features and nonlocal haze-aware features, to gain finer haze-aware features. Moreover, we recursively embed NE modules in a multi-scale framework. It helps not only significantly reduce network's parameters, but also recursively deliver and fuse haze-aware features from higher levels, which makes learning more efficient. Experiments demonstrate that the proposed network performs favorably against the state-of-the-art methods on both synthetic and real-world images.

关键词： Image quality computer vision Fuses Convolution conferences Feature extraction pattern recognition

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Continual Learning with Transformers for Image Classification

Continual Learning with Transformers for Image Classificatio...

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

作者： Ermis, Beyza Zappella, Giovanni Wistuba, Martin Rawal, Aditya Archambeau, Cedric AWS Berlin Germany AWS Santa Clara CA USA

ISBN: (纸本)9781665487399

In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn new concepts without forgetting what has been learnt in the past. This phenomenon is known as catastrophic forgetting and it is often difficult to prevent due to practical constraints, such as the amount of data that can be stored or the limited computation sources that can be used. Moreover, training large neural networks, such as Transformers, from scratch is very costly and requires a vast amount of training data, which might not be available in the application domain of interest. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning, but this needs complex tuning to balance the growing number of parameters and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we validate in the computer vision domain a recent solution called Adaptive Distillation of Adapters (ADA), which is developed to perform continual learning using pre-trained Transformers and Adapters on text classification tasks. We empirically demonstrate on different classification tasks that this method maintains a good predictive performance without retraining the model or increasing the number of model parameters over the time. Besides it is significantly faster at inference time compared to the state-of-the-art methods.

关键词： Training computer vision Adaptation models Computational modeling Neural networks Training data Predictive models

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Consistency-based Active Learning for Object Detection

Consistency-based Active Learning for Object Detection

引用

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

作者： Yu, Weiping Zhu, Sijie Yang, Taojiannan Chen, Chen Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Active learning aims to improve the performance of the task model by selecting the most informative samples with a limited budget. Unlike most recent works that focus on applying active learning for image classification, we propose an effective Consistency-based Active Learning method for object Detection (CALD), which fully explores the consistency between the original and augmented data. CALD has three appealing benefits. (i) CALD is systematically designed by investigating the weaknesses of existing active learning methods, which do not take the unique challenges of object detection into account. (ii) CALD unifies box regression and classification with a single metric, which is not concerned with active learning methods for classification. CALD also focuses on the most informative local region rather than the whole image, which is beneficial for object detection. (iii) CALD not only gauges individual information for sample selection but also leverages mutual information to encourage a balanced data distribution. Extensive experiments show that CALD significantly outperforms existing state-of-the-art task-agnostic and detection-specific active learning methods on general object detection datasets. Based on the Faster R-CNN detector, CALD consistently surpasses the baseline method (random selection) by 2.9/2.8/0.8 mAP on average on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO.

关键词： Learning systems Measurement computer vision conferences Object detection Detectors pattern recognition

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Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices

Efficient Multi-Purpose Cross-Attention Based Image Alignmen...

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

作者： Bilecen, Bahri Batuhan Fisne, Alparslan Ayazoglu, Mustafa Aselsan Res Ankara Turkey

ISBN: (纸本)9781665487399

Image alignment, also known as image registration, is a critical block used in many computer vision problems. One of the key factors in alignment is efficiency, as inefficient aligners can cause significant overhead to the overall problem. In the literature, there are some blocks that appear to do the alignment operation, although most do not focus on efficiency. Therefore, an image alignment block which can both work in time and/or space and can work on edge devices would be beneficial for almost all networks dealing with multiple images. Given its wide usage and importance, we propose an efficient, cross-attention-based, multi-purpose image alignment block (XABA) suitable to work within edge devices. Using cross-attention, we exploit the relationships between features extracted from images. To make cross-attention feasible for real-time image alignment problems and handle large motions, we provide a pyramidal block based cross-attention scheme. This also captures local relationships besides reducing memory requirements and number of operations. Efficient XABA models achieve real-time requirements of running above 20 FPS performance on NVIDIA Jetson Xavier with 30W power consumption compared to other powerful computers. Used as a sub-block in a larger network, XABA also improves multi-image super-resolution network performance in comparison to other alignment methods.

关键词： Performance evaluation computer vision Power demand Image edge detection Superresolution Feature extraction Optical imaging

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Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

Multi-Class Multi-Movement Vehicle Counting Based on CenterT...

引用

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

作者： Kocur, Viktor Ftacnik, Milan Comenius Univ Fac Math Phys & Informat Bratislava Slovakia

ISBN: (纸本)9781665448994

In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various pre-defined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.

关键词： computer vision Tracking conferences Urban areas Neural networks Object detection computer architecture

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Self-supervised Learning for Sonar Image Classification

Self-supervised Learning for Sonar Image Classification

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

作者： Preciado-Grijalva, Alan Wehbe, Bilal Firvida, Miguel Bande Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence D-28359 Bremen Germany Bonn Rhein Sieg Univ Appl Sci D-53757 St Augustin Germany Univ Groningen Dept AI NL-9747 AG Groningen Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Autoencoders, and Jigsaw) to learn high-quality sonar image representation without the need of human labels. We present pre-training and transfer learning results on real-life sonar image datasets. Our results indicate that self-supervised pre-training yields classification performance comparable to supervised pre-training in a few-shot transfer learning setup across all three methods. Code and self-supervised pre-trained models are be available at agrija9/ssl-sonar-images.

关键词： computer vision Transfer learning Supervised learning Noise reduction Sonar Self-supervised learning Image representation

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Pseudo-label Generation for Agricultural Robotics Applications

Pseudo-label Generation for Agricultural Robotics Applicatio...

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

作者： Ciarfuglia, Thomas A. Motoi, Ionut Marian Saraceni, Leonardo Nardi, Daniele Sapienza Univ Rome Dept Comp Sci Management & Automat Engn DIAG Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In the context of table grape cultivation there is rising interest in robotic solutions for harvesting, pruning, precision spraying and other agronomic tasks. Perception algorithms at the core of these systems require large amounts of labelled data, which in this context is often not available. In this work, we propose a semi-supervised solution to reduce the data needed to get state-of-the-art detection and segmentation of fruits in orchards. We present the case of table grape vineyards in southern Lazio (Italy) since grapes are a difficult fruit to segment due to occlusion, color and general illumination conditions. We consider the concrete scenario where the source labelled data is wine grape, while the target data is table grape, with considerable covariate shift. Starting from a simple video input, our method generates first bounding box labels, leveraging the structure from motion information, then segmentation masks, using the same weakly generated bounding box labels and a refining step based on Grabcut. This system is able to produce labels that considerably reduce the covariate shift from source to target data and that requires very limited data acquisition effort. Comparisons with State-of-the-art supervised solutions show how our methods are able to train new models that achieve high performances with few labelled images, with very simple labelling.

关键词： Structure from motion Motion segmentation Pipelines Refining Spraying Lighting pattern recognition

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