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Is Neuron Coverage Needed to Make Person Detection More Robust?

Is Neuron Coverage Needed to Make Person Detection More Robu...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Svetlana Pavlitskaya Ş iyar Yı kmı ş J. Marius Zö llner FZI Research Center for Information Technology Karlsruhe Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The growing use of deep neural networks (DNNs) in safety- and security-critical areas like autonomous driving raises the need for their systematic testing. Coverage-guided testing (CGT) is an approach that applies mutation or fuzzing according to a predefined coverage metric to find inputs that cause misbehavior. With the introduction of a neuron coverage metric, CGT has also recently been applied to DNNs. In this work, we apply CGT to the task of person detection in crowded scenes. The proposed pipeline uses YOLOv3 for person detection and includes finding DNN bugs via sampling and mutation, and subsequent DNN retraining on the updated training set. To be a bug, we require a mutated image to cause a significant performance drop compared to a clean input. In accordance with the CGT, we also consider an additional requirement of increased coverage in the bug definition. In order to explore several types of robustness, our approach includes natural image transformations, corruptions, and adversarial examples generated with the Daedalus attack. The proposed framework has uncovered several thousand cases of incorrect DNN behavior. The relative change in mAP performance of the retrained models reached on average between 26.21% and 64.24% for different robustness types. However, we have found no evidence that the investigated coverage metrics can be advantageously used to improve robustness.

关键词： Measurement Deep learning computer vision computer bugs Neurons Neural networks Training data

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Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-World

Cloud-Device Collaborative Adaptation to Continual Changing ...

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conference on computer vision and pattern recognition (CVPR)

作者： Yulu Gan Mingjie Pan Rongyu Zhang Zijian Ling Lingran Zhao Jiaming Liu Shanghang Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University The Chinese University of Hong Kong Shenzhen Imperial College London

When facing changing environments in the real world, the lightweight model on client devices suffers from severe performance drops under distribution shifts. The main limitations of the existing device model lie in (1) unable to update due to the computation limit of the device, (2) the limited generalization ability of the lightweight model. Meanwhile, recent large models have shown strong generalization capability on the cloud while they can not be deployed on client devices due to poor computation constraints. To enable the device model to deal with changing environments, we propose a new learning paradigm of Cloud-Device Collaborative Continual Adaptation, which encourages collaboration between cloud and device and improves the generalization of the device model. Based on this paradigm, we further propose an Uncertainty-based Visual Prompt Adapted (U-VPA) teacher-student model to transfer the generalization capability of the large model on the cloud to the device model. Specifically, we first design the Uncertainty Guided Sampling (UGS) to screen out challenging data continuously and transmit the most out-of-distribution samples from the device to the cloud. Then we propose a Visual Prompt Learning Strategy with Uncertainty guided updating (VPLU) to specifically deal with the selected samples with more distribution shifts. We transmit the visual prompts to the device and concatenate them with the incoming data to pull the device testing distribution closer to the cloud training distribution. We conduct extensive experiments on two object detection datasets with continually changing environments. Our proposed U-VPA teacher-student framework outperforms previous state-of-the-art test time adaptation and device-cloud collaboration methods. The code and datasets will be released.

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Continual Learning with Transformers for Image Classification

Continual Learning with Transformers for Image Classificatio...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Beyza Ermis Giovanni Zappella Martin Wistuba Aditya Rawal Cé dric Archambeau AWS Berlin AWS Santa Clara

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn new concepts without forgetting what has been learnt in the past. This phenomenon is known as catastrophic forgetting and it is often difficult to prevent due to practical constraints, such as the amount of data that can be stored or the limited computation sources that can be used. Moreover, training large neural networks, such as Transformers, from scratch is very costly and requires a vast amount of training data, which might not be available in the application domain of interest. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning, but this needs complex tuning to balance the growing number of parameters and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we validate in the computer vision domain a recent solution called Adaptive Distillation of Adapters (ADA), which is developed to perform continual learning using pre-trained Transformers and Adapters on text classification tasks. We empirically demonstrate on different classification tasks that this method maintains a good predictive performance without retraining the model or increasing the number of model parameters over the time. Besides it is significantly faster at inference time compared to the state-of-the-art methods.

关键词： Training computer vision Adaptation models Computational modeling Neural networks Training data Predictive models

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Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis

Memory-Friendly Scalable Super-Resolution via Rewinding Lott...

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conference on computer vision and pattern recognition (CVPR)

作者： Jin Lin Xiaotong Luo Ming Hong Yanyun Qu Yuan Xie Zongze Wu School of Informatics Xiamen University Fujian China School of Computer Science and Technology East China Normal University Shanghai China School of Mechatronics and Control Engineering Shenzhen University Shenzhen China

Scalable deep Super-Resolution (SR) models are increasingly in demand, whose memory can be customized and tuned to the computational recourse of the platform. The existing dynamic scalable SR methods are not memory-friendly enough because multi-scale models have to be saved with a fixed size for each model. Inspired by the success of Lottery Tickets Hypothesis (LTH) on image classification, we explore the existence of unstructured scalable SR deep models, that is, we find gradual shrinkage subnetworks of extreme sparsity named winning tickets. In this paper, we propose a Memory-friendly Scalable SR framework (MSSR). The advantage is that only a single scalable model covers multiple SR models with different sizes, instead of reloading SR models of different sizes. Concretely, MSSR consists of the forward and backward stages, the former for model compression and the latter for model expansion. In the forward stage, we take advantage of LTH with rewinding weights to progressively shrink the SR model and the pruning-out masks that form nested sets. Moreover, stochastic self-distillation (SSD) is conducted to boost the performance of sub-networks. By stochastically selecting multiple depths, the current model inputs the selected features into the corresponding parts in the larger model and improves the performance of the current model based on the feedback results of the larger model. In the backward stage, the smaller SR model could be expanded by recovering and fine-tuning the pruned parameters according to the pruning-out masks obtained in the forward. Extensive experiments show the effectiveness of MMSR. The smallest-scale sub-network could achieve the sparsity of 94% and outperforms the compared lightweight SR methods.

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Where did I leave my keys? — Episodic-Memory-Based Question Answering on Egocentric Videos

Where did I leave my keys? — Episodic-Memory-Based Question...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Leonard Bä rmann Alex Waibel Interactive Systems Lab Karlsruhe Institute of Technology Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Humans have a remarkable ability to organize, compress and retrieve episodic memories throughout their daily life. Current AI systems, however, lack comparable capabilities as they are mostly constrained to an analysis with access to the raw input sequence, assuming an unlimited amount of data storage which is not feasible in realistic deployment scenarios. For instance, existing Video Question Answering (VideoQA) models typically reason over the video while already being aware of the question, thus requiring to store the complete video in case the question is not known in *** this paper, we address this challenge with three main contributions: First, we propose the Episodic Memory Question Answering (EMQA) task as a specialization of VideoQA. Specifically, EMQA models are constrained to keep only a constant-sized representation of the video input, thus automatically limiting the computation requirements at query time. Second, we introduce a new egocentric VideoQA dataset called QaEgo4D, far larger than existing egocentric VideoQA datasets and featuring video length unprecedented in VideoQA datasets in general. Third, we present extensive experiments on the new dataset, comparing various baseline models in both the VideoQA and the EMQA setting. To facilitate future research on egocentric VideoQA as well as episodic memory representation and retrieval, we publish our code and dataset.

关键词： computer vision Limiting Codes Computational modeling conferences Memory management Question answering (information retrieval)

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SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds

SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal ...

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conference on computer vision and pattern recognition (CVPR)

作者： Qing Li Huifang Feng Kanle Shi Yue Gao Yi Fang Yu-Shen Liu Zhizhong Han BNRist School of Software Tsinghua University Beijing China School of Informatics Xiamen University Xiamen China Kuaishou Technology Beijing China Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi UAE Department of Computer Science Wayne State University Detroit USA

We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientation, and each step is implemented by a separate algorithm. However, previous methods are sensitive to parameter settings, resulting in poor results from point clouds with noise, density variations and complex geometries. In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. Specifically, we introduce a patch encoding module and a shape encoding module to encode a 3D point cloud into a local latent code and a global latent code, respectively. Then, an attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals. Experimental results show that our SHS-Net outperforms the state-of-the-art methods in both unoriented and oriented normal estimation on the widely used benchmarks. The code, data and pretrained models are available at https://***/LeoQLi/SHS-Net.

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ViewNet: A Novel Projection-Based Backbone with View Pooling for Few-shot Point Cloud Classification

ViewNet: A Novel Projection-Based Backbone with View Pooling...

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conference on computer vision and pattern recognition (CVPR)

作者： Jiajing Chen Minmin Yang Senem Velipasalar Electrical Engineering and Computer Science Dept. Syracuse University Syracuse NY USA

Although different approaches have been proposed for 3D point cloud-related tasks, few-shot learning (FSL) of 3D point clouds still remains under-explored. In FSL, un-like traditional supervised learning, the classes of training and test data do not overlap, and a model needs to rec-ognize unseen classes from only a few samples. Existing FSL methods for 3D point clouds employ point-based models as their backbone. Yet, based on our extensive experiments and analysis, we first show that using a point-based backbone is not the most suitable FSL approach, since (i) a large number of points' features are discarded by the max pooling operation used in 3D point-based backbones, decreasing the ability of representing shape information; (ii) point-based backbones are sensitive to occlusion. To address these issues, we propose employing a projection-and 2D Convolutional Neural Network-based backbone, referred to as the ViewNet, for FSL from 3D point clouds. Our approach first projects a 3D point cloud onto six different views to alleviate the issue of missing points. Also, to generate more descriptive and distinguishing features, we propose View Pooling, which combines different projected plane combinations into five groups and performs max-pooling on each of them. The experiments performed on the ModelNet40, ScanObjectNN and ModelNet40-C datasets, with cross validation, show that our method consistently outperforms the state-of-the-art baselines. Moreover, compared to traditional image classification backbones, such as ResNet, the proposed ViewNet can extract more distinguishing features from multiple views of a point cloud. We also show that ViewNet can be used as a backbone with different FSL heads and provides improved performance compared to traditionally used backbones.

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BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks

BEV-SAN: Accurate BEV 3D Object Detection via Slice Attentio...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaowei Chi Jiaming Liu Ming Lu Rongyu Zhang Zhaoqing Wang Yandong Guo Shanghang Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University The Chinese University of Hong Kong The University of Sydney AI2Robotics

Bird'View (BEV) 3D Object Detection is a crucial multi-view technique for autonomous driving systems. Recently, plenty of works are proposed, following a similar paradigm consisting of three essential components, i.e., camera feature extraction, BEV feature construction, and task heads. Among the three components, BEV feature construction is BEV-specific compared with 2D tasks. Existing methods aggregate the multi-view camera features to the flattened grid in order to construct the BEV feature. However, flattening the BEV space along the height dimension fails to emphasize the informative features of different heights. For example, the barrier is located at a low height while the truck is located at a high height. In this paper, we propose a novel method named BEV Slice Attention Network (BEV-SAN) for exploiting the intrinsic characteristics of different heights. Instead of flattening the BEV space, we first sample along the height dimension to build the global and local BEV slices. Then, the features of BEV slices are aggregated from the camera features and merged by the attention mechanism. Finally, we fuse the merged local and global BEV features by a transformer to generate the final feature map for task heads. The purpose of local BEV slices is to emphasize informative heights. In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices. Compared with uniform sampling, LiDAR-guided sampling can determine more informative heights. We conduct detailed experiments to demonstrate the effectiveness of BEV-SAN. Code will be released.

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Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

Detecting Backdoors During the Inference Stage Based on Corr...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaogeng Liu Minghui Li Haoyu Wang Shengshan Hu Dengpan Ye Hai Jin Libing Wu Chaowei Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Software Engineering Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Arizona State University

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo) 1 1 https://***/CGCL-codes/TeCo, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.

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Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR S...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ekaterina Kalinicheva Loic Landrieu Clé ment Mallet Nesrine Chehata ENSG IGN LASTIG Universit&#x00E9 Gustave Eiffel Marne-la-Vallee France INRAE UMR 1202 BIOGECO Universit&#x00E9 de Bordeaux France Bordeaux INP Universit&#x00E9 Bordeaux Montaigne France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m 2 with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep network architecture predicting for the first time both 3D point-wise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://***/ekalinicheva/multi_layer_vegetation.

关键词： computer vision Solid modeling Three-dimensional displays Laser radar Annotations Atmospheric modeling Time series analysis

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