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Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation

Continual Domain Adaptation through Pruning-aided Domain-spe...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Prasanna B Sunandini Sanyal R. Venkatesh Babu Vision and AI Lab Indian Institute of Science Bengaluru

In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL). The goal is to update the model on continually changing domains while preserving domain-specific knowledge to prevent catastrophic forgetting of past-seen domains. To this end, we build a framework for preserving domain-specific features utilizing the inherent model capacity via pruning. We also perform effective inference using a novel batch-norm based metric to predict the final model parameters to be used accurately. Our approach achieves not only state-of-the-art performance but also prevents catastrophic forgetting of past domains significantly. Our code is made publicly available. 1 .

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A Joint Spatial and Magnification Based Attention Framework for Large Scale Histopathology Classification

A Joint Spatial and Magnification Based Attention Framework ...

引用

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

作者： Zhang, Jingwei Ma, Ke Van Arnam, John Gupta, Rajarsi Saltz, Joel Vakalopoulou, Maria Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Univ Paris Saclay Cent Supelec Paris France

ISBN: (纸本)9781665448994

Deep learning has achieved great success in processing large size medical images such as histopathology slides. However, conventional deep learning methods cannot handle the enormous image sizes;instead, they split the image into patches which are exhaustively processed, usually through multi-instance learning approaches. Moreover and especially in histopathology, determining the most appropriate magnification to generate these patches is also exhaustive: a model needs to traverse all the possible magnifications to select the optimal one. These limitations make the application of deep learning on large medical images and in particular histopathological images markedly inefficient. To tackle these problems, we propose a novel spatial and magnification based attention sampling strategy. First, we use a down-sampled large size image to estimate an attention map that represents a spatial probability distribution of informative patches at different magnifications. Then a small number of patches are cropped from the large size medical image at certain magnifications based on the obtained attention. The final label of the large size image is predicted solely by these patches using an end-to-end training strategy. Our experiments on two different histopathology datasets, the publicly available BACH and a subset of the TCGA-PRAD dataset, demonstrate that the proposed method runs 2.5 times faster with automatic magnification selection in training and at least 1.6 times faster than using all patches in inference as the most of state-of-the-art methods do, without loosing in performance.

关键词： Training Deep learning Histopathology Microscopy Tools Probability distribution pattern recognition

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End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving

End-to-End Interactive Prediction and Planning with Optical ...

引用

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

作者： Wang, Hengli Cai, Peide Fan, Rui Sun, Yuxiang Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Calif San Diego La Jolla CA 92093 USA Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665448994

With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance. Nevertheless, most of these approaches follow a non-interactive prediction and planning paradigm, hypothesizing that a vehicle's behaviors do not affect others. The approaches based on such a non-interactive philosophy typically perform acceptably in sparse traffic scenarios but can easily fail in dense traffic scenarios. Therefore, we propose an end-to-end interactive neural motion planner (INMP) for autonomous driving in this paper. Given a set of past surrounding-view images and a high definition map, our INMP first generates a feature map in bird's-eye-view space, which is then processed to detect other agents and perform interactive prediction and planning jointly. Also, we adopt an optical flow distillation paradigm, which can effectively improve the network performance while still maintaining its real-time inference speed. Extensive experiments on the nuScenes dataset and in the closed-loop Carla simulation environment demonstrate the effectiveness and efficiency of our INMP for the detection, prediction, and planning tasks. Our project page is at ***/view/inmp-ofd.

关键词： Philosophical considerations Predictive models Feature extraction Real-time systems Planning pattern recognition Task analysis

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Is Multimodal vision Supervision Beneficial to Language?

Is Multimodal Vision Supervision Beneficial to Language?

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Avinash Madasu Vasudev Lal Department of Computer Science UNC Chapel Hill USA Cognitive Computing Research Intel Labs USA

vision (image & video) - Language (VL) pre-training is the recent popular paradigm that achieved state-of-the-art results on multi-modal tasks like image-retrieval, video-retrieval, visual question answering etc. These models are trained in an unsupervised way and greatly benefit from the complementary modality supervision. In this paper, we explore if the language representations trained using vision supervision perform better than vanilla language representations on Natural Language Understanding and commonsense reasoning benchmarks. We experiment with a diverse set of image-text models such as ALBEF, BLIP, METER and video-text models like ALPRO, Frozen in Time, VIOLET. We compare the performance of language representations of stand-alone text encoders of these models to the language representations of text encoders learnt through vision supervision. Our experiments suggest that vanilla language representations show superior performance on most of the tasks. These results shed light on the current drawbacks of the vision-language models. The code is available at https://***/avinashsai/MML

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Improving Parkinson Detection using Dynamic Features from Evoked Expressions in Video

Improving Parkinson Detection using Dynamic Features from Ev...

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

作者： Gomez, Luis F. Morales, Aythami Orozco-Arroyave, Juan R. Daza, Roberto Fierrez, Julian Univ Autonoma Madrid Biometr & Data Pattern Analyt Lab Madrid Spain Univ Antioquia Medellin Colombia

ISBN: (纸本)9781665448994

Hypomimia, also known as "facial masking", is a common symptom of Parkinson's Disease (PD). PD is a neurological disorder characterized by non-motor and motor impairments. Hypomimia is the reduction of facial expressiveness, including the emotion expressions. In this work, we explore the use of static and dynamic features for the analysis of evoked facial gestures in PD patients. The main contributions of this work are: (1) We propose a multimodal PD detection system based on both static and dynamic features obtained from evoked face gestures;(2) we propose a novel set of 17 dynamic features to characterize the facial expressiveness and demonstrate that facial dynamics features can be used to improve PD detection;and (3) we analyze different evoked facial expressions and its performance for PD detection. Different expressions activate different Action Units (AUs) and we analyze to what extent each of these AUs contribute to PD detection. The results show that the use of static features generated by pre-trained deep architectures yield up to 77.36% of accuracy for PD detection and the combination with dynamic features improves PD detection by up to 13.46% (from 75.00% to 88.46%). Our experiments also suggest differences in the performance of evoked face gestures in this PD detection task.

关键词： Neurological diseases Deep learning Databases Face recognition conferences Muscles Feature extraction

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Mitigating Bias Using Model-Agnostic Data Attribution

Mitigating Bias Using Model-Agnostic Data Attribution

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sander De Coninck Sam Leroux Pieter Simoens Department of Information Technology IDLab Ghent University - Imec Ghent Belgium

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Mitigating bias in machine learning models is a critical endeavor for ensuring fairness and equity. In this paper, we propose a novel approach to address bias by leveraging pixel image attributions to identify and regularize regions of images containing significant information about bias attributes. Our method utilizes a model-agnostic approach to extract pixel attributions by employing a convolutional neural network (CNN) classifier trained on small image patches. By training the classifier to predict a property of the entire image using only a single patch, we achieve region-based attributions that provide insights into the distribution of important information across the image. We propose utilizing these attributions to introduce targeted noise into datasets with confounding attributes that bias the data, thereby constraining neural networks from learning these biases and emphasizing the primary attributes. Our approach demonstrates its efficacy in enabling the training of unbiased classifiers on heavily biased datasets.

关键词： Training computer vision conferences Noise Neural networks Machine learning Data models

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DeepShift: Towards Multiplication-Less Neural Networks

DeepShift: Towards Multiplication-Less Neural Networks

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

作者： Elhoushi, Mostafa Chen, Zihao Shafiq, Farhan Tian, Ye Henry Li, Joey Yiwei Huawei Technol Markham ON Canada Univ Toronto Toronto ON Canada

ISBN: (纸本)9781665448994

The high computation, memory, and power budgets of inferring convolutional neural networks (CNNs) are major bottlenecks of model deployment to edge computing platforms, e.g., mobile devices and IoT. Moreover, training CNNs is time and energy-intensive even on high-grade servers. Convolution layers and fully connected layers, because of their intense use of multiplications, are the dominant contributor to this computation budget. We propose to alleviate this problem by introducing two new operations: convolutional shifts and fully-connected shifts which replace multiplications with bitwise shift and sign flipping during both training and inference. During inference, both approaches require only 5 bits (or less) to represent the weights. This family of neural network architectures (that use convolutional shifts and fully connected shifts) is referred to as DeepShift models. We propose two methods to train DeepShift models: DeepShift-Q which trains regular weights constrained to powers of 2, and DeepShift-PS that trains the values of the shifts and sign flips directly. Very close accuracy, and in some cases higher accuracy, to baselines are achieved. Converting pre-trained 32bit floating-point baseline models of ResNet18, ResNet50, VGG16, and GoogleNet to DeepShift and training them for 15 to 30 epochs, resulted in Top-1/Top-5 accuracies higher than that of the original model. Last but not least, we implemented the convolutional shifts and fully connected shift GPU kernels and showed a reduction in latency time of 25% when inferring ResNet18 compared to unoptimized multiplication-based GPU kernels. The code can be found at https://***/mostafaelhoushi/DeepShift.

关键词： Training Convolutional codes Computational modeling Neural networks Graphics processing units Mobile handsets pattern recognition

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LogicAL: Towards logical anomaly synthesis for unsupervised anomaly localization

LogicAL: Towards logical anomaly synthesis for unsupervised ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ying Zhao Ricoh Software Research Center (Beijing) Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly localization is a practical technology for improving industrial production line efficiency. Due to anomalies are manifold and hard to be collected, existing unsupervised researches are usually equipped with anomaly synthesis methods. However, most of them are biased towards structural defects synthesis while ignoring the underlying logical constraints. To fill the gap and boost anomaly localization performance, we propose an edge manipulation based anomaly synthesis framework, named LogicAL, that produces photo-realistic both logical and structural anomalies. We introduce a logical anomaly generation strategy that is adept at breaking logical constraints and a structural anomaly generation strategy that complements to the structural defects synthesis. We further improve the anomaly localization performance by introducing edge reconstruction into the network structure. Extensive experiments on the challenge MVTecLOCO, MVTecAD, VisA and MADsim datasets verify the advantage of proposed LogicAL on both logical and structural anomaly localization.

关键词： Location awareness Manifolds computer vision Image edge detection conferences Semantics Production

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Geometric empirical Bayesian model for classification of functional data under diverse sampling regimes

Geometric empirical Bayesian model for classification of fun...

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

作者： Matuk, James Bharath, Karthik Chkrebtii, Oksana Kurtek, Sebastian Ohio State Univ Columbus OH 43210 USA Univ Nottingham Nottingham England

ISBN: (纸本)9781665448994

Functional data analysis (FDA) is focused on various statistical tasks, including inference, for observations that vary over a continuum, which are not effectively addressed by multivariate methods. A feature of these functional observations is the presence of two distinct forms of variability: amplitude that describes differences in magnitudes of features, e.g., extrema, and phase that describes differences in timings of amplitude features. One area of focus in FDA is the classification of new observations based on previously observed training data that has been split into pre-defined classes. Existing methods fail to directly account for both phase and amplitude variability, and work under the restrictive assumption that functional observations are measured on a common, fine grid over the input domain. In this work, we address these issues directly by formulating a Bayesian hierarchical model for irregular, fragmented or sparsely sampled functional observations, where training data from different classes are available. Our approach builds on a recently developed inferential framework for incomplete functional observations and the elastic FDA framework for characterizing amplitude and phase variability. The approach operates by inferring individual parameters that separately track amplitude and phase, which can be combined to infer complete functions underlying each observation, and a class parameter, which can be used to discern the class membership of an observation based on the training data. We validate the proposed framework using simulation studies and real data applications, and showcase the advantages of this perspective when both amplitude and phase variability are present in the data.

关键词： computer vision Phase measurement Computational modeling conferences Training data Data models Bayes methods

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Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap

Exploring AIGC Video Quality: A Focus on Visual Harmony, Vid...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Bowen Qu Xiaoyu Liang Shangkun Sun Wei Gao School of Electronic and Computer Engineering Peking University China Peng Cheng Laboratory China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The recent advancements in Text-to-Video Artificial Intelligence Generated Content (AIGC) have been remarkable. Compared with traditional videos, the assessment of AIGC videos encounters various challenges: visual inconsistency that defy common sense, discrepancies between content and the textual prompt, and distribution gap between various generative models, etc. Target at these challenges, in this work, we categorize the assessment of AIGC video quality into three dimensions: visual harmony, videotext consistency, and domain distribution gap. For each dimension, we design specific modules to provide a comprehensive quality assessment of AIGC videos. Furthermore, our research identifies significant variations in visual quality, fluidity, and style among videos generated by different text-to-video models. Predicting the source generative model can make the AIGC video features more discriminative, which enhances the quality assessment performance. The proposed method was used in the third-place winner of the NTIRE 2024 Quality Assessment for AI-Generated Content - Track 2 Video, demonstrating its effectiveness.

关键词： Visualization computer vision conferences Computational modeling Predictive models Quality assessment pattern recognition

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