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Robustness of Visual Explanations to Common Data Augmentation Methods

Robustness of Visual Explanations to Common Data Augmentatio...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Tětková, Lenka Hansen, Lars Kai Technical University of Denmark Department of Applied Mathematics and Computer Science Richard Petersens Plads 321 Lyngby2800 Kgs. Denmark

ISBN: (纸本)9798350302493

As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but their reliability is being called into question. Our research investigates the response of post-hoc visual explanations to naturally occurring transformations, often referred to as augmentations. We anticipate explanations to be invariant under certain transformations, such as changes to the colour map while responding in an equivariant manner to transformations like translation, object scaling, and rotation. We have found remarkable differences in robustness depending on the type of transformation, with some explainability methods (such as LRP composites and Guided Backprop) being more stable than others. We also explore the role of training with data augmentation. We provide evidence that explanations are typically less robust to augmentation than classification performance, regardless of whether data augmentation is used in training or not. © 2023 ieee.

关键词： Deep neural networks

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Exploring Motion Information for Distractor Suppression in Visual Tracking

Exploring Motion Information for Distractor Suppression in V...

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

作者： Liu, Kaiwen Gao, Jin Liu, Haowei Li, Liang Li, Bing Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch AI Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China Beijing Inst Basic Med Sci Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In the past few years, Siamese networks have achieved outstanding improvements in visual object tracking. However, visual distractors with similar semantics can be easily misclassified as the target by Siamese networks and may consequently result in the drift problem. Besides, the Hanning window penalty, which is generally used to suppress distractors, could fail in many challengeable scenes. Notably, most failures violate the assumption of motion continuity. Thus, in this work, we explore motion information to mitigate the drift problem in visual tracking. First, we introduce a simple linear Kalman filter to predict the bounding box of the target in the current frame, which acts as a reference for decisions. Second, an IoU-Guided penalty is assembled in the post-processing to suppress distractors effectively. It's worth mentioning that our method is almost cost-free. We conduct numerous experimental validations and analyses of our approach on several challenging sequences and datasets. Our tracker runs at approximately 40 fps and performs well on those sequences which include the Background Clutter attribute. Finally, by simultaneously integrating the IoU-Guidedpenalty and the Hanning window penalty with a strong baseline tracker TransT [7], our method achieves favorable gains by 69.1 -> 71.5, 65.7 -> 66.7, 64.9 -> 65.9 success on OTB-100 [32], LaSOT [12], NFS[18].

关键词： Training Visualization computer vision Target tracking Statistical analysis conferences Semantics

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Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Liu, Ajian Tan, Zichang Liang, Yanyan Wan, Jun Institute of Automation Chinese Academy of Sciences Mais Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350302493

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack pattern Generation (Adv-APG) module and a Supervised learning-based Attack pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets. © 2023 ieee.

关键词： Face recognition

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3D Face Modeling via Weakly-supervised Disentanglement Network joint Identity-consistency Prior 18

3D Face Modeling via Weakly-supervised Disentanglement Netwo...

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18th International conference on Automatic Face and Gesture recognition (FG)

作者： Li, Guohao Yang, Hongyu Huang, Di Wang, Yunhong Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350394948;9798350394955

Generative 3D face models featuring disentangled controlling factors hold immense potential for diverse applications in computer vision and computer graphics. However, previous 3D face modeling methods face a challenge as they demand specific labels to effectively disentangle these factors. This becomes particularly problematic when integrating multiple 3D face datasets to improve the generalization of the model. Addressing this issue, this paper introduces a Weakly-Supervised Disentanglement Framework, denoted as WSDF, to facilitate the training of controllable 3D face models without an overly stringent labeling requirement. Adhering to the paradigm of Variational Autoencoders (VAEs), the proposed model achieves disentanglement of identity and expression controlling factors through a two-branch encoder equipped with dedicated identity-consistency prior. It then faithfully re-entangles these factors via a tensor-based combination mechanism. Notably, the introduction of the Neutral Bank allows precise acquisition of subject-specific information using only identity labels, thereby averting degeneration due to insufficient supervision. Additionally, the framework incorporates a label-free second-order loss function for the expression factor to regulate deformation space and eliminate extraneous information, resulting in enhanced disentanglement. Extensive experiments have been conducted to substantiate the superior performance of WSDF.

关键词： Three dimensional computer graphics

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Dynamic Cross Attention for Audio-Visual Person Verification 18

Dynamic Cross Attention for Audio-Visual Person Verification

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18th International conference on Automatic Face and Gesture recognition (FG)

作者： Praveen, R. Gnana Alam, Jahangir Comp Res Inst Montreal Montreal PQ Canada

ISBN: (纸本)9798350394948;9798350394955

Although person or identity verification has been predominantly explored using individual modalities such as face and voice, audio-visual fusion has recently shown immense potential to outperform unimodal approaches. Audio and visual modalities are often expected to pose strong complementary relationships, which plays a crucial role for effective audio-visual fusion. However, they may not always strongly complement each other, they may also exhibit weak complementary relationships, resulting in poor audio-visual feature representations. In this paper, we propose a Dynamic Cross Attention (DCA) model that can dynamically select the cross-attended or unattended features on the fly based on the strong or weak complementary relationships, respectively, across audio and visual modalities. In particular, a conditional gating layer is designed to evaluate the contribution of the cross-attention mechanism and choose cross-attended features only when they exhibit strong complementary relationships, otherwise unattended features. Extensive experiments are conducted on the Voxceleb1 dataset to demonstrate the robustness of the proposed model. Results indicate that the proposed model consistently improves the performance on multiple variants of cross-attention while outperforming the state-of-the-art methods. Code is available at https://***/praveena2j/DCAforPersonVerification

关键词： computer vision

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Flow cytometry with event-based vision and spiking neuromorphic hardware

Flow cytometry with event-based vision and spiking neuromorp...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Abreu, Steven Gouda, Muhammed Lugnan, Alessio Bienstman, Peter University of Groningen Groningen Netherlands Ghent University - Imec Ghent Belgium

ISBN: (纸本)9798350302493

Imaging flow cytometry systems play a critical role in the identification and characterization of large populations of cells or micro-particles. Such systems typically leverage deep artificial neural networks to classify samples. Here we show that an event-based camera and neuromorphic processor can be used in a flow cytometry setup to solve a binary particle classification task with less memory usage, and promising improvements in latency and energy scaling. To reduce the complexity of the spiking neural network, we combine the event-based camera with a free-space optical setup which acts as a non-linear high-dimensional feature map that is computed at the speed of light before the event-based camera receives the signal. We demonstrate, for the first time, a spiking neural network running on neuromorphic hardware for a fully event-based flow cytometry pipeline with 98.45% testing accuracy. Our best artificial neural network on frames of the same data reaches only 97.51%, establishing a neuromorphic advantage also in classification accuracy. We further show that our system will scale favorably to more complex classification tasks. We pave the way for real-time classification with throughput of up to 1,000 samples per second and open up new possibilities for online and on-chip learning in flow cytometry applications. © 2023 ieee.

关键词： Cameras

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OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields

OReX: Object Reconstruction from Planar Cross-sections Using...

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

作者： Sawdayee, Haim Vaxman, Amir Bermano, Amit H. Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland

ISBN: (纸本)9798350301298

Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the entire volume. Previous works addressing this sparse and ill-posed problem either produce low quality results, or rely on additional priors such as target topology, appearance information, or input normal directions. In this paper, we present OReX, a method for 3D shape reconstruction from slices alone, featuring a Neural Field as the interpolation prior. A modest neural network is trained on the input planes to return an inside/outside estimate for a given 3D coordinate, yielding a powerful prior that induces smoothness and self-similarities. The main challenge for this approach is high-frequency details, as the neural prior is overly smoothing. To alleviate this, we offer an iterative estimation architecture and a hierarchical input sampling scheme that encourage coarse-to-fine training, allowing the training process to focus on high frequencies at later stages. In addition, we identify and analyze a ripple-like effect stemming from the mesh extraction step. We mitigate it by regularizing the spatial gradients of the indicator function around input in/out boundaries during network training, tackling the problem at the root. Through extensive qualitative and quantitative experimentation, we demonstrate our method is robust, accurate, and scales well with the size of the input. We report state-of-the-art results compared to previous approaches and recent potential solutions, and demonstrate the benefit of our individual contributions through analysis and ablation studies.

关键词： Physics-based vision shape-from-X

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LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising

LG-BPN: Local and Global Blind-Patch Network for Self-Superv...

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

作者： Wang, Zichun Fu, Ying Liu, Ji Zhang, Yulun Beijing Inst Technol Beijing Peoples R China Baidu Inc Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798350301298

Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blindspot networks (BSNs). For recent methods targeting real-world denoising, they either suffer from ignoring this spatial correlation, or are limited by the destruction of fine textures for under-considering the correlation. In this paper, we present a novel method called LG-BPN for self-supervised real-world denoising, which takes the spatial correlation statistic into our network design for local detail restoration, and also brings the long-range dependencies modeling ability to previously CNN-based BSN methods. First, based on the correlation statistic, we propose a densely-sampled patch-masked convolution module. By taking more neighbor pixels with low noise correlation into account, we enable a denser local receptive field, preserving more useful information for enhanced fine structure recovery. Second, we propose a dilated Transformer block to allow distant context exploitation in BSN. This global perception addresses the intrinsic deficiency of BSN, whose receptive field is constrained by the blind spot requirement, which can not be fully resolved by the previous CNN-based BSNs. These two designs enable LG-BPN to fully exploit both the detailed structure and the global interaction in a blind manner. Extensive results on real-world datasets demonstrate the superior performance of our method. https://***/Wang-XIaoDingdd/LGBPN

关键词： Low-level vision

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RangeViT: Towards vision Transformers for 3D Semantic Segmentation in Autonomous Driving

RangeViT: Towards Vision Transformers for 3D Semantic Segmen...

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

作者： Ando, Angelika Gidaris, Spyros Bursuc, Andrei Puy, Gilles Boulch, Alexandre Marlet, Renaud Valeo Ai Paris France Univ PSL Mines Paris Ctr Robot Paris France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9798350301298

Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach. These projection-based methods usually benefit from fast computations and, when combined with techniques which use other point cloud representations, achieve state-of-the-art results. Today, projection-based methods leverage 2D CNNs but recent advances in computer vision show that vision transformers (ViTs) have achieved state-of-the-art results in many image-based benchmarks. In this work, we question if projection-based methods for 3D semantic segmentation can benefit from these latest improvements on ViTs. We answer positively but only after combining them with three key ingredients: (a) ViTs are notoriously hard to train and require a lot of training data to learn powerful representations. By preserving the same backbone architecture as for RGB images, we can exploit the knowledge from long training on large image collections that are much cheaper to acquire and annotate than point clouds. We reach our best results with pre-trained ViTs on large image datasets. (b) We compensate ViTs' lack of inductive bias by substituting a tailored convolutional stem for the classical linear embedding layer. (c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder. With these ingredients, we show that our method, called RangeViT, outperforms existing projection-based methods on nuScenes and SemanticKITTI. The code is available at https://***/valeoai/rangevit.

关键词： Deep learning architectures and techniques

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Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction

Open-World Multi-Task Control Through Goal-Aware Representat...

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

作者： Cai, Shaofei Wang, Zihao Ma, Xiaojian Liu, Anji Liang, Yitao Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peking Univ Sch Intelligence Sci & Technol Beijing Peoples R China Univ Calif Los Angeles Dept Comp Sci Los Angeles CA USA Beijing Inst Gen Artificial Intelligence BIGAI Beijing Peoples R China

ISBN: (纸本)9798350301298

We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents. We first identify two main challenges of learning such policies: 1) the indistinguishability of tasks from the state distribution, due to the vast scene diversity, and 2) the non-stationary nature of environment dynamics caused by partial observability. To tackle the first challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage the emergence of goal-relevant visual state representations. To tackle the second challenge, the policy is further fueled by an adaptive horizon prediction module that helps alleviate the learning uncertainty brought by the non-stationary dynamics. Experiments on 20 Minecraft tasks show that our method significantly outperforms the best baseline so far;in many of them, we double the performance. Our ablation and exploratory studies then explain how our approach beat the counterparts and also unveil the surprising bonus of zero-shot generalization to new scenes (biomes). We hope our agent could help shed some light on learning goal-conditioned, multi-task agents in challenging, open-ended environments like Minecraft. The code is released at https://***/CraftJarvis/MC-Controller.

关键词： Embodied vision: Active agents simulation

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