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Video Class Agnostic Segmentation Benchmark for Autonomous Driving

Video Class Agnostic Segmentation Benchmark for Autonomous D...

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

作者： Siam, Mennatullah Kendall, Alex Jagersand, Martin Univ Alberta Edmonton AB Canada Wayve London England

ISBN: (纸本)9781665448994

Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical robotics applications, especially autonomous driving, it is important to segment all objects, including those unknown at training time. We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects. Video class agnostic segmentation can be formulated as an open-set or a motion segmentation problem. We discuss both formulations and provide datasets and benchmark different baseline approaches for both tracks. In the motion-segmentation track we benchmark real-time joint panoptic and motion instance segmentation, and evaluate the effect of ego-flow suppression. In the open-set segmentation track we evaluate baseline methods that combine appearance, and geometry to learn prototypes per semantic class. We then compare it to a model that uses an auxiliary contrastive loss to improve the discrimination between known and unknown objects. Datasets and models are publicly released at https://***/vca/.

关键词： Training computer vision Tracking Motion segmentation Semantics Video sequences Benchmark testing

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SRKTDN: Applying Super Resolution Method to Dehazing Task

SRKTDN: Applying Super Resolution Method to Dehazing Task

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

作者： Chen, Tianyi Fu, Jiahui Jiang, Wentao Gao, Chen Liu, Si Beihang Univ Shenyuan Honors Coll Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781665448994

Nonhomogeneous haze removal is a challenging problem, which does not follow the physical scattering model of haze. Numerous existing methods focus on homogeneous haze removal by generating transmission map of the image, which is not suitable for nonhomogeneous dehazing tasks. Some methods use end-to-end model but are also designed for homogeneous haze. Inspired by Knowledge Transfer Dehazing Network and Trident Dehazing Network, we propose a model with super-resolution method and knowledge transfer method. Our model consists of a teacher network, a dehaze network and a super-resolution network. The teacher network provides the dehaze network with reliable prior, the dehaze network focuses primarily on haze removal, and the super-resolution network is used to capture details in the hazy image. Ablation study shows that the super-resolution network has significant benefit to image quality. And comparison shows that our model outperforms previous state-of-the-art methods in terms of perceptual quality on NTIRE2021 NonHomogeneous Dehazing Challenge dataset, and also performs well on other datasets.

关键词： Knowledge engineering Image quality Computational modeling conferences Superresolution Scattering pattern recognition

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Layer Importance Estimation with Imprinting for Neural Network Quantization

Layer Importance Estimation with Imprinting for Neural Netwo...

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

作者： Liu, Hongyang Elkerdawy, Sara Ray, Nilanjan Elhoushi, Mostafa Univ Alberta Edmonton AB Canada Huawei Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Neural network quantization has achieved a high compression rate using fixed low bit-width representation of weights and activations while maintaining the accuracy of the high-precision original network. However, mixed precision (per-layer bit-width precision) quantization requires careful tuning to maintain accuracy while achieving further compression and higher granularity than fixed-precision quantization. We propose an accuracy-aware criterion to quantify the layer's importance rank. Our method applies imprinting per layer which acts as a proxy module for accuracy estimation in an efficient way. We rank the layers based on the accuracy gain from previous modules and iteratively quantize first those with less accuracy gain. Previous mixed-precision methods either rely on expensive search techniques such as reinforcement learning (RL) or end-to-end optimization with a lack of interpretation to the quantization configuration scheme. Our method is a one-shot, efficient, accuracy-aware information estimation and thus draws better interpretability to the selected bit-width configuration.

关键词： Training Quantization (signal) Search methods Neural networks Estimation Reinforcement learning pattern recognition

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Assistive Signals for Deep Neural Network Classifiers

Assistive Signals for Deep Neural Network Classifiers

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

作者： Pestana, Camilo Liu, Wei Glance, David Owens, Robyn Mian, Ajmal Univ Western Australia 35 Stirling Hwy Crawley WA 6009 Australia

ISBN: (纸本)9781665448994

Deep Neural Networks are brittle in that small changes in the input can drastically affect their prediction outcome and confidence. Consequently, research in this area mainly focus on adversarial attacks and defenses. In this paper, we take an alternative stance and introduce the concept of Assistive Signals, which are perturbations optimized to improve a model's confidence score regardless if it's under attack or not. We analyze some interesting properties of these assistive perturbations and extend the idea to optimize them in the 3D space simulating different lighting conditions and viewing angles. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models more than those generated by conventional methods that work in the 2D space. 'Assistive Signals' also illustrate bias of ML models towards certain patterns in real-life objects.

关键词： Deep learning computer vision Three-dimensional displays Perturbation methods conferences Optimization methods Lighting

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Micro-Expression Classification based on Landmark Relations with Graph Attention Convolutional Network

Micro-Expression Classification based on Landmark Relations ...

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

作者： Kumar, Ankith Jain Rakesh Bhanu, Bir Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

ISBN: (纸本)9781665448994

Facial micro-expressions are brief, rapid, spontaneous gestures of the facial muscles that express an individual's genuine emotions. Because of their short duration and subtlety, detecting and classifying these micro-expressions by humans and machines is difficult. In this paper, a novel approach is proposed that exploits relationships between landmark points and the optical flow patch for the given landmark points. It consists of a two-stream graph attention convolutional network that extracts the relationships between the landmark points and local texture using an optical flow patch. A graph structure is built to draw-out temporal information using the triplet of frames. One stream is for node feature location, and the other one is for a patch of optical-flow information. These two streams (node location stream and optical flow stream) are fused for classification. The results are shown on, CASME II and SAMM, publicly available datasets, for three classes and five classes of micro-expressions. The proposed approach outperforms the state-of-the-art methods for 3 and 5 categories of expressions.

关键词： computer vision conferences Feature extraction Facial muscles pattern recognition Optical flow

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Comparing Representations in Tracking for Event Camera-based SLAM

Comparing Representations in Tracking for Event Camera-based...

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

作者： Jiao, Jianhao Huang, Huaiyang Li, Liang He, Zhijian Zhu, Yilong Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665448994

This paper investigates two typical image-type representations for event camera-based tracking: time surface (TS) and event map (EM). Based on the original TS-based tracker, we make use of these two representations' complementary strengths to develop an enhanced version. The proposed tracker consists of a general strategy to evaluate the optimization problem's degeneracy online and then switch proper representations. Both TS and EM are motion- and scene-dependent, and thus it is important to figure out their limitations in tracking. We develop six tracker variations and conduct a thorough comparison of them on sequences covering various scenarios and motion complexities. We release our implementations and detailed results to benefit the research community on event cameras: https://***/gogojjh/ESVO_extension.

关键词： Tracking loops computer vision Simultaneous localization and mapping Tracking conferences Switches Trajectory

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MTUNet: Few-shot Image Classification with Visual Explanations

MTUNet: Few-shot Image Classification with Visual Explanatio...

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

作者： Wang, Bowen Li, Liangzhi Verma, Manisha Nakashima, Yuta Kawasaki, Ryo Nagahara, Hajime Osaka Univ Inst Databil Sci IDS Suita Osaka Japan Osaka Univ Grad Sch Med Suita Osaka Japan

ISBN: (纸本)9781665448994

Few-shot learning (FSL) approaches, mostly neural network-based, are assuming that the pre-trained knowledge can be obtained from base (seen) categories and transferred to novel (unseen) categories. However, the black-box nature of neural networks makes it difficult to understand what is actually transferred, which may hamper its application in some risk-sensitive areas. In this paper, we reveal a new way to perform explainable FSL for image classification, using discriminative patterns and pairwise matching. Experimental results prove that the proposed method can achieve satisfactory explainability on two mainstream datasets. Code is available*.

关键词： Knowledge engineering Visualization computer vision conferences Computational modeling Neural networks Benchmark testing

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End-to-End Learned Image Compression with Augmented Normalizing Flows

End-to-End Learned Image Compression with Augmented Normaliz...

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

作者： Ho, Yung-Han Chan, Chih-Chun Peng, Wen-Hsiao Hang, Hsueh-Ming Natl Chiao Tung Univ Comp Sci Dept Hsinchu Taiwan Natl Chiao Tung Univ Elect Engn Dept Hsinchu Taiwan Natl Chiao Tung Univ Pervas AI Res PAIR Labs Hsinchu Taiwan

ISBN: (纸本)9781665448994

This paper presents a new attempt at using augmented normalizing flows (ANF) for lossy image compression. ANF is a specific type of normalizing flow models that augment the input with an independent noise, allowing a smoother transformation from the augmented input space to the latent space. Inspired by the fact that ANF can offer greater expressivity by stacking multiple variational autoencoders (VAE), we generalize the popular VAE-based compression framework by the autoencoding transforms of ANF. When evaluated on Kodak dataset, our ANF-based model provides 3.4% higher BD-rate saving as compared with a VAE-based baseline that implements hyper-prior with mean prediction. Interestingly, it benefits even more from the incorporation of a post-processing network, showing 11.8% rate saving as compared to 6.0% with the baseline plus post-processing.

关键词： computer vision Image coding conferences Stacking Low-pass filters Transforms Predictive models

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Single image HDR synthesis using a Densely Connected Dilated ConvNet

Single image HDR synthesis using a Densely Connected Dilated...

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

作者： Akhil, K. A. Jiji, C., V Coll Engn Trivandrum Trivandrum Kerala India

ISBN: (纸本)9781665448994

Visual representations using high dynamic range (HDR) images have become increasingly popular because of their high quality and expressive ability. HDR images are expected to be used in a broad range of applications, including digital cinema, photography, and broadcast. The generation of a HDR image from a single exposure Low Dynamic Range (LDR) image is a challenging task where one must make up for missing data due to underexposure or overexposure and color quantization. In this paper, we propose a deep convolutional neural network (CNN) model with a stack of dilated convolutional blocks for reconstructing a HDR image from a single LDR image. Within each dilation block, the dilation rate of the convolution layer is three and progressively decreases to one. Multiple dilation convolution blocks are further connected densely to improve the representation capacity of the network. As the network is trained in a supervised manner, the additional information is reconstructed from learned features. Our experimental results show that the model effectively captures missing information that was lost from the original image.

关键词： Photography Visualization Quantization (signal) Convolution Dynamic range pattern recognition Convolutional neural networks

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Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner

Training Domain-invariant Object Detector Faster with Featur...

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

作者： Lee, Chaehyeon Seo, Junghoon Jung, Heechul Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea SI Analyt Co Ltd Daejeon South Korea SI Analyt Daejeon South Korea

ISBN: (纸本)9781665448994

In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because they cause domain changes. Previously, nuisance disentangled feature transformation (NDFT) was proposed to build domain-invariant feature extractor with with knowledge of nuisance factors. However, NDFT requires enormous time in a training phase, so it has been impractical. In this paper, we introduce our proposed method, A-NDFT, which is an improvement to NDFT. A-NDFT utilizes two acceleration techniques, feature replay and slow learner. Consequently, on a large-scale UAVDT benchmark, it is shown that our framework can reduce the training time of NDFT from 31 hours to 3 hours while still maintaining the performance. The code will be made publicly available online(1).

关键词： Training computer vision Vehicle detection conferences Object detection Detectors Benchmark testing

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