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Cross Transferring Activity recognition to Word Level Sign Language Detection

Cross Transferring Activity Recognition to Word Level Sign L...

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

作者： Radhakrishnan, Srijith Mohan, Nikhil C. Varma, Manisimha Varma, Jaithra Pai, Smitha N. Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Data Sci & Comp Applicat Manipal 576104 Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The lack of large scale labelled datasets in word-level sign language recognition (WSLR) poses a challenge to detecting sign language from videos. Most WSLR approaches operate on datasets that do not model real-world settings very well, as they do not have a high degree of variability in terms of signers, background, lighting and inter signer variation. We chose the MS-ASL dataset to overcome these limitations as they model open-world settings very well. This paper benchmarks successful action recognition architectures on the MS-ASL dataset using transfer learning. We have achieved new state-of-the-art accuracy (92.35%) with an improvement of 7.03% over the previous state-of-the-art introduced by the MS-ASL paper. We have analyzed how action-recognition architectures fair in the task of WSLR, and we propose SlowFast 8x8 ResNet 101 as a robust and suitable architecture for the task of WSLR.

关键词： computer vision conferences Transfer learning Lighting Gesture recognition computer architecture Assistive technologies

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Adversarial Robustness through the Lens of Convolutional Filters

Adversarial Robustness through the Lens of Convolutional Fil...

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

作者： Gavrikov, Paul Keuper, Janis Offenburg Univ IMLA Offenburg Germany Fraunhofer ITWM CC HPC Kaiserslautern Germany Fraunhofer Res Ctr ML Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep learning models are intrinsically sensitive to distribution shifts in the input data. In particular, small, barely perceivable perturbations to the input data can force models to make wrong predictions with high confidence. An common defense mechanism is regularization through adversarial training which injects worst-case perturbations back into training to strengthen the decision boundaries, and to reduce overfitting. In this context, we perform an investigation of 3 x 3 convolution filters that form in adversarially-trained models. Filters are extracted from 71 public models of the l(infinity)-RobustBench CIFAR-10/100 and ImageNet1k leaderboard and compared to filters extracted from models built on the same architectures but trained without robust regularization. We observe that adversarially-robust models appear to form more diverse, less sparse, and more orthogonal convolution filters than their normal counterparts. The largest differences between robust and normal models are found in the deepest layers, and the very first convolution layer, which consistently and predominantly forms filters that can partially eliminate perturbations, irrespective of the architecture.

关键词： Training Convolution Perturbation methods computer architecture Predictive models Robustness Data models

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Z-Domain Entropy Adaptable Flex for Semi-supervised Action recognition in the Dark

Z-Domain Entropy Adaptable Flex for Semi-supervised Action R...

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

作者： Chen, Zhi Fan, Zijun Li, Yongjie Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co 101 Waihuan Xi Rd Guangzhou 510006 Guangdong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The subtask of Human Action recognition (AR) in the dark is gaining a lot of traction nowadays, which takes a significant place in the field of computer vision. The implementation of its application includes self-driving at night, human-pose estimation, night surveillance, etc. Currently, solutions such as DLN for AR have emerged. However, due to the poor accuracy even when leveraging on large amounts of datasets and complex architectures, the development of AR in the dark has been slow to progress. In this paper, we propose a novel and straightforward method: Z-Domain Entropy Adaptable Flex. This constructs a neural network architecture R(2+1)D, including (i) a self-attention mechanism, which combines and extracts corresponding and complementary features from the dual pathways;(ii) Zero-DCE low light image enhancement, which improves enhanced quality;and (iii) FlexMatch method, which can generates the pseudo-labels flexibly. With the help of pseudo-labels from FlexMatch, our proposed Z-DEAF method facilitates the process of gaining desired classification boundaries. This works by repeating Expanding Entropy and Shrinking Entropy. It aims to solve the problem of unclear classification boundaries between the categories. Our model obtains superior performance in experiments, and achieves state-of-the-art results on ARID.

关键词： computer vision Flexible printed circuits Surveillance Neural networks Estimation computer architecture Feature extraction

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Doubling down: sparse grounding with an additional, almost-matching caption for detection-oriented multimodal pretraining

Doubling down: sparse grounding with an additional, almost-m...

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

作者： Nebbia, Giacomo Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A common paradigm in deep learning applications for computer vision is self-supervised pretraining followed by supervised fine-tuning on a target task. In the self-supervision step, a model is trained in a supervised fashion, but the source of supervision needs to be implicitly defined by the data. Image-caption alignment is often used as such a source of implicit supervision in multimodal pretraining, and grounding (i.e., matching word tokens with visual tokens) is one way to exploit it. We introduce a strategy to take advantage of an underexplored structure in image-caption datasets: the relationship between captions matched with different images but mentioning the same objects. Given an image-caption pair, we find an additional caption that mentions one of the objects the first caption mentions, and we impose a sparse grounding between the image and the second caption so that only a few word tokens are grounded in the image. Our goal is to learn a better feature representation for the objects mentioned by both captions, encouraging grounding between the additional caption and the image to focus on the common objects only. We report superior grounding performance when comparing our approach with a previously-published pretraining strategy, and we show the benefit of our proposed double-caption grounding on two downstream detection tasks: supervised detection and open-vocabulary detection.

关键词： Deep learning computer vision Visualization Grounding conferences Computational modeling Data models

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Can the Mathematical Correctness of Object Configurations Affect the Accuracy of Their Perception?

Can the Mathematical Correctness of Object Configurations Af...

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

作者： Jiang, Han Li, Zeqian Whitehill, Jacob Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We investigate a new type of dataset bias based on the mathematical correctness of object configurations in visual scenes, and how this bias can affect the accuracy of computer vision models. Our experiments demonstrate how CNNs trained to detect and recognize individual objects are capable of implicitly learning simple mathematical relationships between them directly from pixel data;moreover, models that are trained with a dataset bias (e.g., all examples are mathematically correct) can suffer in performance when evaluated on test data without this bias. We found evidence for this effect in two settings: (1) object detection of math symbols in images of arithmetic expressions, and (2) object detection of moving particles from images produced by a physics simulator. Importantly, the semantic bias that we study is based not just on simple co-occurrence patterns in each image, but rather on higher-order semantic rules that generalize to unique combinations of objects not seen during training. While the magnitude of the effect was small, the accuracy difference was statistically reliable.

关键词： Training computer vision Computational modeling Semantics Symbols Object detection Mathematical models

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Long-term Action Forecasting Using Multi-headed Attention-based Variational Recurrent Neural Networks

Long-term Action Forecasting Using Multi-headed Attention-ba...

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

作者： Loh, Siyuan Brandon Roy, Debaditya Fernando, Basura ASTAR IHPC SCC Singapore Singapore ASTAR IHPC CFAR Singapore Singapore

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Systems developed for predicting both the action and the amount of time someone might take to perform that action need to be aware of the inherent uncertainty in what humans do. Here, we present a novel hybrid generative model for action anticipation that attempts to capture the uncertainty in human actions. Our model uses a multi-headed attention-based variational generative model for action prediction (MAVAP), and Gaussian log-likelihood maximization to predict the corresponding action's duration. During training, we optimise three losses: a variational loss, a negative log-likelihood loss, and a discriminative cross-entropy loss. We evaluate our model on benchmark datasets (i.e., Breakfast and 50Salads) for action forecasting tasks and demonstrate improvements over prior methods using both ground truth observations and predicted features from an action segmentation network (i.e., MS-TCN++). We also show that factorizing the latent space across multiple Gaussian heads predicts better plausible future action sequences compared to a single Gaussian.

关键词： Training computer vision Uncertainty Recurrent neural networks conferences Predictive models Hybrid power systems

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TDT: Teaching Detectors to Track without Fully Annotated Videos

TDT: Teaching Detectors to Track without Fully Annotated Vid...

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

作者： Yu, Shuzhi Wu, Guanhang Gu, Chunhui Fathy, Mohammed E. Duke Univ Durham NC 27706 USA Google LLC Mountain View CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recently, one-stage trackers that use a joint model to predict both detections and appearance embeddings in one forward pass received much attention and achieved state-of-the-art results on the Multi-Object Tracking (MOT) benchmarks. However, their success depends on the availability of videos that are fully annotated with tracking data, which is expensive and hard to obtain. This can limit the model generalization. In comparison, the two-stage approach, which performs detection and embedding separately, is slower but easier to train as their data are easier to annotate. We propose to combine the best of the two worlds through a data distillation approach. Specifically, we use a teacher embedder, trained on Re-ID datasets, to generate pseudo appearance embedding labels for the detection datasets. Then, we use the augmented dataset to train a detector that is also capable of regressing these pseudo-embeddings in a fully-convolutional fashion. Our proposed one-stage solution matches the two-stage counterpart in quality but is 3 times faster. Even though the teacher embedder has not seen any tracking data during training, our proposed tracker achieves competitive performance with some popular trackers (e.g. JDE) trained with fully labeled tracking data.

关键词： Training computer vision Annotations conferences Detectors Predictive models Benchmark testing

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The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark

The multi-modal universe of fast-fashion: the Visuelle 2.0 b...

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

作者： Skenderi, Geri Joppi, Christian Denitto, Matteo Scarpa, Berniero Cristani, Marco Univ Verona Verona Italy Humatics Srl Verona Italy Nuna Lie Srl Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie(1), a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using the time series in long-term forecasting scenarios, ameliorating the WAPE by 8.2% and the MAE by 7.7%. The dataset is available at : https://***/forecasting/visuelle.

关键词： computer vision conferences Clothing Time series analysis Companies Benchmark testing pattern recognition

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Transformaly - Two (Feature Spaces) Are Better Than One

Transformaly - Two (Feature Spaces) Are Better Than One

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

作者： Cohen, Matan Jacob Avidan, Shai Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel Tel Aviv Univ Sch Elect Engn Tel Aviv Israel

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Anomaly detection is a well-established research area that seeks to identify samples outside of a predetermined distribution. An anomaly detection pipeline is comprised of two main stages: (1) feature extraction and (2) normality score assignment. Recent papers used pre-trained networks for feature extraction achieving state-of-the-art results. However, the use of pre-trained networks does not fully-utilize the normal samples that are available at train time. This paper suggests taking advantage of this information by using teacher-student training. In our setting, a pretrained teacher network is used to train a student network on the normal training samples. Since the student network is trained only on normal samples, it is expected to deviate from the teacher network in abnormal cases. This difference can serve as a complementary representation to the pre-trained feature vector. Our method - Transformaly - exploits a pre-trained vision Transformer (ViT) to extract both feature vectors: the pre-trained (agnostic) features and the teacher-student (fine-tuned) features. We report state-of-the-art AUROC results in both the common unimodal setting, where one class is considered normal and the rest are considered abnormal, and the multimodal setting, where all classes but one are considered normal, and just one class is considered abnormal(1).

关键词： Training Visualization computer vision conferences Pipelines computer architecture Feature extraction

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Cell Selection-based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia

Cell Selection-based Data Reduction Pipeline for Whole Slide...

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

作者： Kockwelp, Jacqueline Thiele, Sebastian Kockwelp, Pascal Bartsch, Jannis Schliemann, Christoph Angenendt, Linus Risse, Benjamin Univ Munster Munster Germany Univ Med Ctr Munster Munster Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

computer-aided analyses of cells in Whole Slide Images (WSIs) have become an important topic in digital pathology. Despite the recent success of deep learning in biomedical research, these methods are still difficult to apply to multi-gigabyte WSIs. To overcome this difficulty, a variety of patch-based solutions have been introduced, which however all suffer from certain limitations compared to manual examinations and often fail to meet the specificities of cytological inspections. Here we introduce an alternative scheme which incorporates clinical expertise in the selection process to automatically identify the clinically relevant areas. By using a bone marrow smear dataset containing 22-gigapixel images of 153 patients, we introduce a novel pipeline combining unsupervised and supervised methodologies to gradually select the most appropriate single-cell regions, which are subsequently used in multiple medically crucial Acute Myeloid Leukemia (AML) predictions. Our approach is capable of dealing with a variety of common WSI challenges, massively limits the manual annotation effort, reduces the data by a factor of up to 99.9% and achieves super-human performance on the final cytological prediction tasks.

关键词： Deep learning Pathology computer vision Image analysis conferences Pipelines Manuals

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