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Motion Aware Double Attention Network for Dynamic Scene Deblurring

Motion Aware Double Attention Network for Dynamic Scene Debl...

引用

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

作者： Yang, Dan Yamac, Mehmet Huawei Technol Oy Finland Co Ltd Helsinki Finland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Motion deblurring in dynamic scenes is a challenging task when the blurring is caused by one or a combination of various reasons such as moving objects, camera movement, etc. Since event cameras can detect changes in intensity with a low latency, necessary motion information is inherently captured in event data, which could be quite useful for deblurring standard camera images. The degradation intensity does not show homogeneity across an image due to factors like object depth, speed, etc. We propose a two-branch network structure, Motion Aware Double Attention Network (MADANet), that pays special attention to areas with high blur. As part of the network, event data is first used by the high blur region segmentation module that creates a probability-like score for areas exhibiting high relative motion to the camera. Then, the event data is also injected to feature maps in the main body, where there is a second attention mechanism available for each branch. The effective usage of event data and two-level attention mechanisms makes the network very compact. During the experiment, it was shown that the proposed network could achieve state-of-the-art performance not only on the benchmark dataset from GoPro, but also on two newly collected datasets, one of which contains real event data.

关键词： Degradation Image segmentation Motion segmentation Dynamics Data visualization Cameras pattern recognition

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TDT: Teaching Detectors to Track without Fully Annotated Videos

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

引用

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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Multi-Camera Vehicle Tracking Based on Occlusion-aware and Inter-vehicle Information

Multi-Camera Vehicle Tracking Based on Occlusion-aware and I...

引用

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

作者： Liu, Yuming Zhang, Xiaochun Zhang, Bingzhen Zhang, Xiaoyong Wang, Sen Xu, Jianrong Shenzhen Urban Transport Planning Ctr Co Ltd Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the demands of analyzing and predicting traffic flow for applications in smart cities, Multi-Target Multi-Camera vehicle Tracking(MTMCT) at the city scale has become a fundamental problem. The MTMCT is challenging due to the view variations, frequent occlusions, and similar vehicle models in the same camera. This work proposes an MTMCT framework based on occlusion-aware and intervehicle information that can effectively match vehicle tracklets. The occlusion-aware module segments the tracklets of an occluded and occluding vehicle pair. It recalculates the similarity of the complete tracklets, which can handle the occlusions and suppress false detections. This work proposes an inter-vehicle information module to improve the matching accuracy. The module can enhance the ability to distinguish similar vehicles under the same camera at different times. The proposed whole framework consists of four modules: (1) vehicle detection and feature extraction by re-identification models, (2) single-camera tracking (SCT) to produce initial tracklets with an occlusion-aware module, (3) tracklets similarity by inter-vehicle association, (4) clustering in adjacent cameras for multi-camera tracklets matching. The proposed method obtains IDF1 score of 0.8285 on the Track-1 multi-camera vehicle tracking task of the 2022 AI City Challenge.

关键词： computer vision Smart cities Vehicle detection conferences Cameras Feature extraction 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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Searching for Energy-Efficient Hybrid Adder-Convolution Neural Networks

Searching for Energy-Efficient Hybrid Adder-Convolution Neur...

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

作者： Li, Wenshuo Chen, Xinghao Bai, Jinyu Ning, Xuefei Wang, Yunhe Huawei Noahs Ark Lab Hong Kong Peoples R China Huawei TCS Lab Hong Kong Peoples R China Beihang Univ Sch Integrated Circuit Sci & Engn Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

As convolutional neural networks (CNNs) are more and more widely used in computer vision area, the energy consumption of CNNs has become the focus of researchers. For edge devices, both the battery life and the inference latency are critical and directly affect user experience. Recently, great progress has been made in the design of neural architectures and new operators. The emergence of neural architecture search technology has improved the performance of network step by step, and liberated the productivity of engineers to a certain extent. New operators, such as AdderNets, make it possible to further improve the energy efficiency of neural networks. In this paper, we explore the fusion of new adder operators and common convolution operators into state-of-the-art light-weight networks, GhostNet, to search for models with better energy efficiency and performance. Our proposed search equilibrium strategy ensures that the adder and convolution operators can be treated fairly in the search, and the resulting model achieves the same accuracy of 73.9% with GhostNet on the ImageNet dataset at an extremely low power consumption of 0.612 mJ. When keeping the same energy consumption, the accuracy reaches 74.3% which is 0.4% higher than original GhostNet.

关键词： Training Energy consumption computer vision Convolution Computational modeling Neural networks computer architecture

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Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity

Revisiting Vicinal Risk Minimization for Partially Supervise...

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

作者： Dong, Nanqing Wang, Jiayi Voiculescu, Irina Univ Oxford Dept Comp Sci Oxford England Univ Oxford Math Inst Oxford England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest. Instead, it would be convenient to collect multiple small partially labeled datasets from different matching sources, where the medical images may have only been annotated for a subset of classes of interest. This paper offers an empirical understanding of an under-explored problem, namely partially supervised multi-label classification (PSMLC), where a multi-label classifier is trained with only partially labeled medical images. In contrast to the fully supervised counterpart, the partial supervision caused by medical data scarcity has non-trivial negative impacts on the model performance. A potential remedy could be augmenting the partial labels. Though vicinal risk minimization (VRM) has been a promising solution to improve the generalization ability of the model, its application to PSMLC remains an open question. To bridge the methodological gap, we provide the first VRM-based solution to PSMLC. The empirical results also provide insights into future research directions on partially supervised learning under data scarcity.

关键词： Bridges computer vision Costs conferences Supervised learning Entropy Data models

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SaR: Self-adaptive Refinement on Pseudo Labels for Multiclass-Imbalanced Semi-supervised Learning

SaR: Self-adaptive Refinement on Pseudo Labels for Multiclas...

引用

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

作者： Lai, Zhengfeng Wang, Chao Cheung, Sen-ching Chuah, Chen-Nee Univ Calif Davis Davis CA 95616 USA Southern Univ Sci & Technol Shenzhen Peoples R China Univ Kentucky Lexington KY 40506 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Class-imbalanced datasets can severely deteriorate the performance of semi-supervised learning (SSL). This is due to the confirmation bias especially when the pseudo labels are highly biased towards the majority classes. Traditional resampling or reweighting techniques may not be directly applicable when the unlabeled data distribution is unknown. Inspired by the threshold-moving method that performs well in supervised learning-based binary classification tasks, we provide a simple yet effective scheme to address the multiclass imbalance issue of SSL. This scheme, named SaR, is a Self-adaptive Refinement of soft labels before generating pseudo labels. The pseudo labels generated post-SaR will be less biased, resulting in higher quality data for training the classifier. We show that SaR can consistently improve recent consistency-based SSL algorithms on various image classification problems across different imbalanced ratios. We also show that SaR is robust to the situations where unlabeled data have different distributions as labeled data. Hence, SaR does not rely on the assumptions that unlabeled data share the same distribution as the labeled data.

关键词： Training computer vision conferences Semisupervised learning pattern recognition Classification algorithms Task analysis

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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...

引用

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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Color Invariant Skin Segmentation

Color Invariant Skin Segmentation

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

作者： Xu, Han Sarkar, Abhijit Abbott, A. Lynn Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA Virginia Tech Virginia Tech Transportat Inst Blacksburg VA 24061 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper addresses the problem of automatically detecting human skin in images without reliance on color information. A primary motivation of the work has been to achieve results that are consistent across the full range of skin tones, even while using a training dataset that is significantly biased toward lighter skin tones. Previous skin-detection methods have used color cues almost exclusively, and we present a new approach that performs well in the absence of such information. A key aspect of the work is dataset repair through augmentation that is applied strategically during training, with the goal of color invariant feature learning to enhance generalization. We have demonstrated the concept using two architectures, and experimental results show improvements in both precision and recall for most Fitzpatrick skin tones in the benchmark ECU dataset. We further tested the system with the RFW dataset to show that the proposed method performs much more consistently across different ethnicities, thereby reducing the chance of bias based on skin color. To demonstrate the effectiveness of our work, extensive experiments were performed on grayscale images as well as images obtained under unconstrained illumination and with artificial filters.

关键词： Training Representation learning computer vision Sensitivity Image color analysis Lighting Color

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An Empirical study of Data-Free Quantization's Tuning Robustness

An Empirical study of Data-Free Quantization's Tuning Robust...

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

作者： Chen, Hong Wen, Yuxuan Ding, Yifu Yang, Zhen Guo, Yufei Qin, Haotong Beihang Univ Beijing Peoples R China Shanghai Aerosp Elect Technol Inst Shanghai Peoples R China China Aerosp Sci & Ind Corp Acad 2 Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep convolutional neural networks are now performing increasingly superior in various fields, while the network parameters are getting massive as the advanced neural networks tend to be deeper. Among various model compression methods, quantization is one of the most potent approaches to compress neural networks by compacting model weights and activations to lower bit-width. The data-free quantization method is also proposed, which is specialized for some privacy and security scenarios and enables quantization without access to real data. In this work, we find that the tuning robustness of existing data-free quantization is flawed, progressing an empirical study and determining some hyperparameter settings that can converge the model stably in the data-free quantization process. Our study aims to evaluate the overall tuning robustness of the current data-free quantization system, which is existing methods are significantly affected by parameter fluctuations in tuning. We also expect data-free quantification methods with tuning robustness to appear in the future.

关键词： Data privacy Quantization (signal) Fluctuations conferences Neural networks Robustness pattern recognition

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