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Large-scale Dataset Pruning with Dynamic Uncertainty

Large-scale Dataset Pruning with Dynamic Uncertainty

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Muyang He Shuo Yang Tiejun Huang Bo Zhao Beijing Academy of Artificial Intelligence Peking University University of Technology Sydney

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The state of the art of many learning tasks, e.g., image classification, is advanced by collecting larger datasets and then training larger models on them. As the outcome, the increasing computational cost is becoming unaffordable. In this paper, we investigate how to prune the large-scale datasets, and thus produce an informative subset for training sophisticated deep models with negligible performance drop. We propose a simple yet effective dataset pruning method by exploring both the prediction uncertainty and training dynamics. We study dataset pruning by measuring the variation of predictions during the whole training process on large-scale datasets, i.e., ImageNet-1K and ImageNet-21K, and advanced models, i.e., Swin Transformer and ConvNeXt. Extensive experimental results indicate that our method outperforms the state of the art and achieves 25% lossless pruning ratio on both ImageNet1K and ImageNet-21K. The code and pruned datasets are available at https://***/BAAI-DCAI/Dataset-Pruning.

关键词： Training computer vision Uncertainty Computational modeling conferences Predictive models Transformers

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Focusing on What Matters: Fine-grained Medical Activity recognition for Trauma Resuscitation via Actor Tracking

Focusing on What Matters: Fine-grained Medical Activity Reco...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wenjin Zhang Keyi Li Sen Yang Sifan Yuan Ivan Marsic Genevieve J. Sippel Mary S. Kim Randall S. Burd Rutgers University Waymo Children’s National Hospital

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Trauma is a leading cause of mortality worldwide, with about 20% of these deaths being preventable. Most of these preventable deaths result from errors during the initial resuscitation of injured patients. Decision support has been evaluated as an approach to support teams during this phase to reduce errors. Existing systems require manual data entry and monitoring, which makes tasks challenging to accomplish in a time-critical setting. This paper identified the specific challenges of achieving effective decision support in trauma resuscitation based on computer vision techniques, including complex backgrounds, crowded scenes, fine-grained activities, and a scarcity of labeled data. To address the first three challenges, the proposed system involved an actor tracker that identifies individuals, allowing the system to focus on actor-specific features. Video Masked Autoencoder (Video-MAE) was used to overcome the issue of insufficient labeled data. This approach enables self-supervised learning using unlabeled video content, improving feature representation for medical activities. For more reliable performance, an ensemble fusion method was introduced. This technique combines predictions from consecutive video clips and different actors. Our method outperformed existing approaches in identifying fine-grained activities, providing a solution for activity recognition in trauma resuscitation and similar complex domains.

关键词： computer vision conferences Focusing Self-supervised learning Manuals Activity recognition Time factors

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Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models

Sensor Equivariance: A Framework for Semantic Segmentation w...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hannes Reichert Manuel Hetzel Andreas Hubert Konrad Doll Bernhard Sick University of Applied Sciences Aschaffenburg Germany University of Kassel Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data processing depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Furthermore, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable using projection properties in convolutional neural networks. To that end, we propose an architecture for using the SensorConv layer in the Detectron2 [21] framework. We collected a dataset of equirectangular images for our experiments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an augmentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our experiment, we show that our method can generalize better across different camera sensors.

关键词： computer vision Convolution Semantic segmentation conferences Cameras Distortion Data processing

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Contour-Based Structure from Reflection

Contour-Based Structure from Reflection

引用

conference on computer vision and pattern recognition (CVPR)

作者： Po-Hao Huang Shang-Hon Lai Department of Computer Science National Tsing Hua University Hsinchu Taiwan

In this paper, we propose a novel contour-based algorithm for 3D object reconstruction from a single uncalibrated image acquired under the setting of two plane mirrors. With the epipolar geometry recovered from the image and the properties of mirror reflection, metric reconstruction of an arbitrary rigid object is accomplished without knowing the camera parameters and the mirror poses. For this mirror setup, the epipoles can be estimated from the correspondences between the object and its reflection, which can be established automatically from the tangent lines of their contours. By using the property of mirror reflection as well as the relationship between the mirror plane normal with the epipole and camera intrinsic, we can estimate the camera intrinsic, plane normals and the orientation of virtual cameras. The positions of the virtual cameras are determined by minimizing the distance between the object contours and the projected visual cone for a reference view. After the camera parameters are determined, the 3D object model is constructed via the image-based visual hulls (IBVH) technique. The 3D model can be refined by integrating the multiple models reconstructed from different views. The main advantage of the proposed contour-based Structure from Reflection (SfR) algorithm is that it can achieve metric reconstruction from an uncalibrated image without feature point correspondences. Experimental results on synthetic and real images are presented to show its performance.

关键词： Reflection Mirrors Cameras Image reconstruction computer vision Reconstruction algorithms Shape computer science computer Society pattern recognition

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Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models

Investigating the Effectiveness of Cross-Attention to Unlock...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Saman Motamed Wouter Van Gansbeke Luc Van Gool INSAIT Sofia University Bulgaria ETH Zurich KU Leuven

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content. In particular, manipulating the cross-attention layers of Text-to-Image (T2I) diffusion models has shown great promise in controlling the shape and location of objects in the scene. Transferring image-editing techniques to the video domain, however, is extremely challenging as object motion and temporal consistency are difficult to capture accurately. In this work, we take a first look at the role of cross-attention in Text-to-Video (T2V) diffusion models for zero-shot video editing. While one-shot models have shown potential in controlling motion and camera movement, we demonstrate zero-shot control over object shape, position and movement in T2V models. We show that despite the limitations of current T2V models, cross-attention guidance can be a promising approach for editing videos. Code: https://***/sam-motamed/***

关键词： computer vision Codes Shape Computational modeling conferences Text to image Diffusion models

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FineRehab: A Multi-modality and Multi-task Dataset for Rehabilitation Analysis

FineRehab: A Multi-modality and Multi-task Dataset for Rehab...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jianwei Li Jun Xue Rui Cao Xiaoxia Du Siyu Mo Kehao Ran Zeyan Zhang School of Sports Engineering Beijing Sport University China Department of Neurorehabilitation Rehabilitation Research Center China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The assessment of rehabilitation exercises for neurological and musculoskeletal disorders are crucial for recovery. Traditionally, assessment methods have been subjective, with inherent uncertainty and limitations. This paper introduces a novel multi-modality dataset named FineRehab § to prompt the study of rehabilitation movement analysis, leveraging advancements in sensor technology and artificial intelligence. FineRehab collects 16 actions from 50 participants, including both patients with musculoskeletal disorders and healthy individuals, and consists of 4,215 action samples captured by two Kinect cameras and 17 IMUs. To benchmark FineRehab, we present a reliable approach to analyze rehabilitation exercises, and make experiments to evaluate the comprehensive movement quality from across multi-dimensions. Comparative experimental analyses have verified the validity of our dataset in distinguishing between the movement of the normal population and patients, which can offer a quantifiable basis for personalized rehabilitation feedback. The introduction of FineRehab will encourage researchers to apply, develop and adapt various methods for rehabilitation exercise analysis.

关键词： Deep learning Musculoskeletal system computer vision Uncertainty conferences Multitasking Cameras

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Medium level scene representation using a VLSI smart hexagonal sensor with multiresolution edge extraction capability and scale space integration processing

Medium level scene representation using a VLSI smart hexagon...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tremblay Savard Poussart Computer Vision and Digital Systems Laboratory Department of Electrical Engineering Laval University QUE Canada

This paper presents a sensing approach where photo-transduction, multi-resolution feature extraction, scale-space integration and edge tracking are performed on a mixed (digital-analog) VLSI architecture in order to generate medium-level scene description. The proposed system is mainly targeted for robot vision applications where feature description is preferred to a set of raw or raster 2D images and edge maps. The Multiport Access photo-Receptor (MAR) is a CMOS sensor and represents the main sensory part of this integrated image acquisition system. VLSI also provides means to integrate analog computing, digital controller and DSP co-processor modules which define a powerful sensory chip set for focal plane image processing. A current version of the MAR sensor which implements 256/spl times/256 pixels includes 16 analog spatial filters which simultaneously compute multiresolution edge maps. This unique 2D hexagonal smart sensor approach which performs up to 8.5/spl times/10/sup 9/ arithmetic Op/sec during the acquisition/filtering phase and 25/spl times/10/sup 9/ Logical Op/sec for scale-space integration allows high resolution image capability. It represents a significant improvement for passive sensory units in a compact assembly for computer vision applications.< >

关键词： Very-large-scale integration Image edge analysis Feature extraction Robots, vision systems Spatial filters CMOS integrated circuits Image sensors

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Analysis of Deep Fusion Strategies for Multi-Modal Gesture recognition

Analysis of Deep Fusion Strategies for Multi-Modal Gesture R...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alina Roitberg Tim Pollert Monica Haurilet Manuel Martin Rainer Stiefelhagen CV:HCI Lab Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Video-based gesture recognition has a wide spectrum of applications, ranging from sign language understanding to driver monitoring in autonomous cars. As different sensors suffer from their individual limitations, combining multiple sources has strong potential to improve the results. A number of deep architectures have been proposed to recognize gestures from e.g. both color and depth data. However, these models conventionally comprise separate networks for each modality, which are then combined in the final layer (e.g. via simple score averaging). In this work, we take a closer look at different fusion strategies for gesture recognition especially focusing on the information exchange in the intermediate layers. We compare three fusion strategies on the widely used C3D architecture: 1) late fusion, combining the streams in the final layer; 2) information exchange in an intermediate layer using an additional convolution layer; and 3) linking information at multiple layers simultaneously using the cross-stitch units, originally designed for multi-task learning. Our proposed C3D-Stitch model achieves the best recognition rate, demonstrating the effectiveness of sharing information at earlier stages.

关键词： Gesture recognition Convolution computer architecture Image color analysis Data models Videos Correlation

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Fake News Detection using Higher-order User to User Mutual-attention Progression in Propagation Paths

Fake News Detection using Higher-order User to User Mutual-a...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Rahul Mishra University of Stavanger Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Social media has become a very prominent source of news consumption. It brings forth multifaceted, multimodal and real-time information on a silver platter for the users. Fake news or rumor mongering on social media is one of the most challenging issues pertaining to present web. Previously, researchers have tried to classify news propagation paths on social media (e.g. Twitter) to detect fake news. However, they do not utilize latent relationships among users efficiently to model the influence of the users with high prestige on the other users, which is a very significant factor in information propagation. In this paper, we propose a novel Higher-order User to User Mutual-attention Progression (HiMaP) method to capture the cues related to authority or influence of the users by modelling direct and indirect (multi-hop) influence relationships among each pair of users, present in the propagation sequence. The proposed higher order attention trick is a novel contribution which can also be very effective in case of transformer architectures[30]. Our model not only outperforms the state-of-the-art methods on two publicly available Twitter datasets but also explains the propagation patterns pertaining to fake news by visualizing higher order mutual-attentions.

关键词： Feature extraction Twitter Linear programming computer vision Linguistics Time series analysis

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Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution

Training Transformer Models by Wavelet Losses Improves Quant...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Cansu Korkmaz A. Murat Tekalp College of Engineering and KUIS AI Center Koc University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transformer-based models have achieved remarkable results in low-level vision tasks including image super-resolution (SR). However, early Transformer-based approaches that rely on self-attention within non-overlapping windows encounter challenges in acquiring global information. To activate more input pixels globally, hybrid attention models have been proposed. Moreover, training by solely minimizing pixel-wise RGB losses, such as l 1 , have been found inadequate for capturing essential high-frequency details. This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field. ii) We employ wavelet losses to train Transformer models to improve quantitative and subjective performance. While wavelet losses have been explored previously, showing their power in training Transformer-based SR models is novel. Our experimental results demonstrate that the proposed model provides state-of-the-art PSNR results as well as superior visual performance across various benchmark datasets.

关键词： Wavelet transforms Training Visualization Computational modeling conferences Superresolution computer architecture

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