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deepPIC: Deep Perceptual Image Clustering For Identifying Bias In vision Datasets

deepPIC: Deep Perceptual Image Clustering For Identifying Bi...

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

作者： Jaipuria, Nikita Stevo, Katherine Zhang, Xianling Gaopande, Meghana L. Garcia, Ian Calle Jain, Jinesh Murali, Vidya N. Ford Greenfield Labs Palo Alto CA 94304 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dataset bias in manually collected datasets is a known problem in computer vision. In safety-critical applications such as autonomous driving, these biases can lead to catastrophic errors from models trained on such datasets, jeopardizing the safety of users and their surroundings. Being able to unpuzzle the bias in a given dataset, and across datasets, is an essential tool for building safe and responsible AI. In this paper, we present deepPIC: deep Perceptual Image Clustering, a novel hierarchical clustering pipeline that leverages deep perceptual features to visualize and understand bias in unstructured and unlabeled datasets. It does so by effectively highlighting nuanced subcategories of information embedded within the data (such as multiple but repetitive shadow types) that typically are hard and/or expensive to annotate. Through experiments on a variety of image datasets, both open-source and internal, we demonstrate the effectiveness of deepPIC in (i) singling out errors in metadata from open-source datasets such as BDD100K;(ii) automatic nuanced metadata annotation;(iii) mining for edge cases;(iv) visualizing inherent bias both within and across multiple datasets;and (v) capturing synthetic data limitations;thus highlighting the wide variety of applications this pipeline can be applied to. All clustering results included here have been uploaded with image thumbnails on our project website - https://***/unpuzzle_dataset_bias/. We recommend zooming in for best impact.

关键词： Analytical models computer vision Visualization Annotations Pipelines Buildings Data visualization

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What Should Be Equivariant In Self-Supervised Learning

What Should Be Equivariant In Self-Supervised Learning

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

作者： Xie, Yuyang Wen, Jianhong Lau, Kin Wai Rehman, Yasar Abbas Ur Shen, Jiajun TCL AI Lab Hong Kong Peoples R China Fuzhou Univ Fuzhou Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Self-supervised learning (SSL) aims to learn feature representation without human-annotated data. Existing methods approach this goal by encouraging the feature representations to be invariant under a set of task-irrelevant transformations and distortions defined a priori. However, multiple studies have shown that such an assumption often limits the expressive power of the representations and model would perform poorly when downstream tasks violate this assumption. For example, being invariant to rotations would prevent features from retaining enough information to estimate object rotation angles. This suggests additional manual work and domain knowledge are required for selecting augmentation types during SSL. In this work, we relax the transformation-invariance assumption by introducing a SSL framework that encourages the feature representations to preserve the order of transformation scale in embedding space for some transformations while maintaining invariance to other transformations. This allows the learned feature representations to retain information about task-relevant transformations. In addition, this framework gives rise to a handy mechanism to determine the augmentation types to which the features representations should be invariant and equivariant during SSL. We demonstrate the effectiveness of our method on various datasets such as Fruits 360, Caltech-UCSD Birds 200, and Blood cells dataset.

关键词： computer vision conferences Self-supervised learning Cells (biology) Feature extraction Distortion Birds

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BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

BCI: Breast Cancer Immunohistochemical Image Generation thro...

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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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Video-based multimodal spontaneous emotion recognition using facial expressions and physiological signals

Video-based multimodal spontaneous emotion recognition using...

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

作者： Ouzar, Yassine Bousefsaf, Frederic Djeldjli, Djamaleddine Maaoui, Choubeila Univ Lorraine LCOMS F-57000 Metz France

ISBN: (纸本)9781665487399

Human's affective state recognition remains a challenging topic due to the complexity of emotions, which involves experiential, behavioral, and physiological elements. Since it is difficult to comprehensively describe emotion in terms of single modalities, recent studies have focused on fusion strategy to exploit the complementarity of multimodal signals. In this article, we study the feasibility of fusing facial expressions with physiological cues on human emotion recognition accuracy. The contributions of this work are threefold: 1) We propose a new spatiotemporal network for facial expression recognition using a 3D squeeze and exitation based 3D Xception architecture (squeeze and exitation Xception network). 2) We adopt the first multiple modalities fusion using single input source which, to the best of our knowledge, no existing multimodal emotion recognition system has attempted to identify emotional state from only facial videos using facial expressions and physiological signals features. 3) We compare the performance of the unimodal approach using only facial expressions or physiological data, to multimodal systems fusing facial expressions with video-based physiological cues. In our experiments, physiological signals such as the iPPG signal and features of heart rate variability measured remotely using the imaging photoplethysmography (iPPG) method are used. The preliminary results show that the multimodal fusion model improves the accuracy of emotion recognition, and merging facial expressions features with iPPG signal gives the best accuracy with 71.90 %.

关键词： Emotion recognition Three-dimensional displays Face recognition Photoplethysmography Feature extraction Physiology Spatiotemporal phenomena

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Deep density estimation based on multi-spectral remote sensing data for in-field crop yield forecasting

Deep density estimation based on multi-spectral remote sensi...

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

作者： Baghdasaryan, Liana Melikbekyan, Razmik Dolmajain, Arthur Hobbs, Jennifer Intelinair Inc Yerevan Armenia Intelinair Inc Chicago IL USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Yield forecasting has been a central task in computational agriculture because of its impact on agricultural management from the individual farmer to the government level. With advances in remote sensing technology, computational processing power, and machine learning, the ability to forecast yield has improved substantially over the past years. However, most previous work has been done leveraging low-resolution satellite imagery and forecasting yield at the region, county, or occasionally farm-level. In this work, we use high-resolution aerial imagery and output from high-precision harvesters to predict in-field harvest values for corn-raising farms in the US Midwest. By using the harvester information, we are able to cast the problem of yield-forecasting as a density estimation problem and predict a harvest rate, in bushels/acre, at every pixel in the field image. This approach provides the farmer with a detailed view of which areas of the farm may be performing poorly so he can make the appropriate management decisions in addition to providing an improved prediction of total yield. We evaluate both traditional machine learning approaches with hand-crafted features alongside deep learning methods. We demonstrate the superiority of our pixel-level approach based on an encoder-decoder framework which produces a 5.41% MAPE at the field-level.

关键词： Deep learning Satellites conferences Government Estimation Crops pattern recognition

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Contrastive Regularization for Semi-Supervised Learning

Contrastive Regularization for Semi-Supervised Learning

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

作者： Lee, Doyup Kim, Sungwoong Kim, Ildoo Cheon, Yeongjae Cho, Minsu Han, Wook-Shin POSTECH Pohang South Korea Kakao Brain Seongnam South Korea Kakao Enterprise Seongnam South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance. In this study, we analyze that the consistency regularization restricts the propagation of labeling information due to the exclusion of samples with unconfident pseudo-labels in the model updates. Then, we propose contrastive regularization to improve both efficiency and accuracy of the consistency regularization by well-clustered features of unlabeled data. In specific, after strongly augmented samples are assigned to clusters by their pseudolabels, our contrastive regularization updates the model so that the features with confident pseudo-labels aggregate the features in the same cluster, while pushing away features in different clusters. As a result, the information of confident pseudo-labels can be effectively propagated into more unlabeled samples during training by the well-clustered features. On benchmarks of semi-supervised learning tasks, our contrastive regularization improves the previous consistency-based methods and achieves state-of-the-art results, especially with fewer training iterations. Our method also shows robust performance on open-set semi-supervised learning where unlabeled data includes out-of-distribution samples.

关键词： Training computer vision Analytical models conferences Memory management Semisupervised learning Benchmark testing

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SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos

SoccerNet-Tracking: Multiple Object Tracking Dataset and Ben...

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

作者： Cioppa, Anthony Giancola, Silvio Deliege, Adrien Kang, Le Zhou, Xin Cheng, Zhiyu Ghanem, Bernard Van Droogenbroeck, Marc Univ Liege Liege Belgium KAUST Thuwal Saudi Arabia Baidu Res Beijing Peoples R China

ISBN: (纸本)9781665487399

Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. The dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.

关键词： Training computer vision conferences Benchmark testing Object tracking Data mining Sports

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Unsupervised Continual Learning for Gradually Varying Domains

Unsupervised Continual Learning for Gradually Varying Domain...

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

作者： Taufique, Abu Md Niamul Jahan, Chowdhury Sadman Savakis, Andreas Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In Unsupervised Domain Adaptation (UDA), a network is trained on a source domain and adapted on a target domain where no labeled data is available. Existing UDA techniques consider having the entire target domain available at once, which may not be feasible during deployment in realistic settings where batches of target data are acquired over time. Continual Learning (CL) has been dealing with data constrained paradigms in a supervised manner, where batches of labeled samples are sequentially presented to the network and the network continually learns from the new data without forgetting what was previously learned. Our method for unsupervised continual learning serves as a bridge between the UDA and CL paradigms. This research addresses a gradually evolving target domain fragmented into multiple sequential batches where the model continually adapts to the gradually varying stream of data in an unsupervised manner. To tackle this challenge, we propose a source free method based on episodic memory replay with buffer management. A contrastive loss is incorporated for better alignment of the buffer samples and the continual stream of batches. Our experiments on the rotating MNIST and CORe50 datasets confirm the benefits of our unsupervised continual learning method for gradually varying domains.

关键词： Learning systems Bridges computer vision Adaptation models Codes conferences Memory management

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Consistency-based Active Learning for Object Detection

Consistency-based Active Learning for Object Detection

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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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Towards Open-Set Object Detection and Discovery

Towards Open-Set Object Detection and Discovery

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

作者： Zheng, Jiyang Li, Weihao Hong, Jie Petersson, Lars Barnes, Nick Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the human pursuit of knowledge, open-set object detection (OSOD) has been designed to identify unknown objects in a dynamic world. However, an issue with the current setting is that all the predicted unknown objects share the same category as "unknown", which require incremental learning via a human-in-the-loop approach to label novel classes. In order to address this problem, we present a new task, namely Open-Set Object Detection and Discovery (OSODD). This new task aims to extend the ability of open-set object detectors to further discover the categories of unknown objects based on their visual appearance without human effort. We propose a two-stage method that first uses an open-set object detector to predict both known and unknown objects. Then, we study the representation of predicted objects in an unsupervised manner and discover new categories from the set of unknown objects. With this method, a detector is able to detect objects belonging to known classes and define novel categories for objects of unknown classes with minimal supervision. We show the performance of our model on the MS-COCO dataset under a thorough evaluation protocol. We hope that our work will promote further research towards a more robust real-world detection system.

关键词： Visualization computer vision Protocols conferences Object detection Detectors Human in the loop

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