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SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries

SplineCam: Exact Visualization and Characterization of Deep ...

引用

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

作者： Humayun, Ahmed Imtiaz Balestriero, Randall Ralakrishnan, Ouha Raraniuk, Richard Rice Univ Houston TX 77005 USA Meta AI FAIR New York NY USA

ISBN: (纸本)9798350301298

Current Deep Network (DN) visualization and interpretability methods rely heavily on data space visualizations such as scoring which dimensions of the data are responsible for their associated prediction or generating new data features or samples that best match a given DN unit or representation. In this paper, we go one step further by developing the first provably exact method for computing the geometry of a DN's mapping - including its decision boundary - over a specified region of the data space. By leveraging the theory of Continuous Piece-Wise Linear (CPWL) spline DNs, SplineCam exactly computes a DN's geometry without resorting to approximations such as sampling or architecture simplification. SplineCam applies to any DN architecture based on CPWL activation nonlinearities, including (leaky) ReLU, absolute value, maxout, and max-pooling and can also be applied to regression DNs such as implicit neural representations. Beyond decision boundary visualization and characterization, SplineCam enables one to compare architectures, measure generalizability, and sample from the decision boundary on or off the data manifold. Project website: ***/splinecam.

关键词： computer vision theory

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Edges to Shapes to Concepts: Adversarial Augmentation for Robust vision

Edges to Shapes to Concepts: Adversarial Augmentation for Ro...

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

作者： Tripathi, Aditay Singh, Rishubh Chakraborty, Anirban Shenoy, Pradeep Indian Inst Sci CDS Bangalore Karnataka India Google Res India Bangalore Karnataka India

ISBN: (纸本)9798350301298

Recent work has shown that deep vision models tend to be overly dependent on low-level or "texture" features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes for accurate prediction in an object classification setting. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion, with the image label of the edgemap image. To classify these augmented images, the model needs to not only detect and focus on edges but distinguish between relevant and spurious edges. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures. As an example, for ViT-S, We obtain absolute gains on classification accuracy gains up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViTB) and ImageNet-R (for ViT-S), respectively. Analysis using a range of probe datasets shows substantially increased shape sensitivity in our trained models, explaining the observed improvement in robustness and classification accuracy.

关键词： Deep learning architectures and techniques

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A Practical Upper Bound for the Worst-Case Attribution Deviations

A Practical Upper Bound for the Worst-Case Attribution Devia...

引用

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

作者： Wang, Fan Kong, Adams Wai-Kin Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Rapid Rich Object Search ROSE Lab IGP Singapore Singapore

ISBN: (纸本)9798350301298

Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against those attacks;however, none of them explicitly quantifies the actual deviations of attributions. In this work, for the first time, a constrained optimization problem is formulated to derive an upper bound that measures the largest dissimilarity of attributions after the samples are perturbed by any noises within a certain region while the classification results remain the same. Based on the formulation, different practical approaches are introduced to bound the attributions above using Euclidean distance and cosine similarity under both l(2) and l(infinity)-norm perturbations constraints. The bounds developed by our theoretical study are validated on various datasets and two different types of attacks (PGD attack and IFIA attribution attack). Over 10 million attacks in the experiments indicate that the proposed upper bounds effectively quantify the robustness of models based on the worst-case attribution dissimilarities.

关键词： Explainable computer vision

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Nighttime Image Dehazing Based on Variational Decomposition Model

Nighttime Image Dehazing Based on Variational Decomposition ...

引用

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

作者： Liu, Yun Yan, Zhongsheng Wu, Aimin Ye, Tian Li, Yuche Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Chongqing Coll Int Business & Econ Coll Big Data & Intelligent Engn Chongqing 401520 Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen 361021 Peoples R China China Univ Petr Coll Geosci Beijing 102249 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Most of existing dehazing algorithms are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light and glows. To obtain high-quality image under nighttime haze imaging conditions, we present an effective single nighttime image dehazing framework based on a variational decomposition model to simultaneously address these undesirable issues. First, a variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ l(1) norm to constrain the structure component, adopt l(0) sparsity term to enforce the piece-wise continuous of the detail layer, and use l(2) norm to separate the noise layer. Next, the structure layer is recovered by means of inversing the physical model and the detail layers are revealed in a multi-scale gradient enhancement manner. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experimental results show that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with several state-of-the-art dehazing techniques.

关键词： Degradation computer vision conferences Computational modeling Noise reduction Lighting Focusing

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TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in vision-Centric Autonomous Driving

TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Jo...

引用

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

作者： Fang, Shaoheng Wang, Zi Zhong, Yiqi Ge, Junhao Chen, Siheng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Univ Southern Calif Dept Comp Sci Los Angeles CA USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the surrounding environment from raw RGB images. However, it is still a critical challenge to synchronize features obtained at multiple camera views and timestamps due to inevitable geometric distortions and further exploit those spatial-temporal features. To address this issue, we propose a temporal bird's-eye-view pyramid transformer (TBP-Former) for vision-centric PnP, which includes two novel designs. First, a pose-synchronized BEV encoder is proposed to map raw image inputs with any camera pose at any time to a shared and synchronized BEV space for better spatial-temporal synchronization. Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial priors. Extensive experiments on nuScenes dataset show that our proposed framework overall outperforms all state-of-the-art vision-based prediction methods. Code is available at: https://***/MediaBrain-SJTU/TBP-Former

关键词： Autonomous driving

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RB-Dust - A Reference-based Dataset for vision-based Dust Removal

RB-Dust - A Reference-based Dataset for Vision-based Dust Re...

引用

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

作者： Buckel, Peter Oksanen, Timo Dietmueller, Thomas Ravensburg Germany Technical University of Munich Germany Germany

ISBN: (纸本)9798350302493

Dust in the agricultural landscape is a significant challenge and influences, for example, the environmental perception of autonomous agricultural machines. Image enhancement algorithms can be used to reduce dust. However, these require dusty and dust-free images of the same environment for validation. In fact, to date, there is no dataset that we are aware of that addresses this issue. Therefore, we present the agriscapes RB-Dust dataset, which is named after its purpose of reference-based dust removal. It is not possible to take pictures from the cabin during tillage, as this would cause shifts in the images. Because of this, we built a setup from which it is possible to take images from a stationary position close to the passing tractor. The test setup was based on a half-sided gate through which the tractor could drive. The field tests were carried out on a farm in Bavaria, Germany, during tillage. During the field tests, other parameters such as soil moisture and wind speed were controlled, as these significantly affect dust development. We validated our dataset with contrast enhancement and image dehazing algorithms and analyzed the generalizability from recordings from the moving tractor. Finally, we demonstrate the application of dust removal based on a high-level vision task, such as person classification. Our empirical study confirms the validity of RB-Dust for vision-based dust removal in agriculture. © 2023 ieee.

关键词： Image enhancement

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Meta-Personalizing vision-Language Models to Find Named Instances in Video

Meta-Personalizing Vision-Language Models to Find Named Inst...

引用

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

作者： Yeh, Chun-Hsiao Russell, Bryan Sivic, Josef Heilbron, Fabian Caba Jenni, Simon Univ Calif Berkeley Berkeley CA 94720 USA Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Adobe Res San Francisco CA 94107 USA

ISBN: (纸本)9798350301298

Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as "My dog Biscuit" appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and Deep-Fashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset.

关键词： language reasoning vision

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Deep Normalized Cross-Modal Hashing with Bi-Direction Relation Reasoning

Deep Normalized Cross-Modal Hashing with Bi-Direction Relati...

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

作者： Sun, Changchang Latapie, Hugo Liu, Gaowen Yan, Yan IIT Dept Comp Sci Chicago IL 60616 USA Cisco Res Emerging Technol & Incubat San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the continuous growth of large-scale multi-modal data and increasing requirements for retrieval speed, deep cross-modal hashing has gained increasing attention recently. Most of existing studies take a similarity matrix as supervision to optimize their models, and the inner product between continuous surrogates of hash codes is utilized to depict the similarity in the Hamming space. However, all of them merely consider the relevant information to build the similarity matrix, ignoring the contribution of the irrelevant one, i.e., the categories that samples do not belong to. Therefore, they cannot effectively alleviate the effect of dissimilar samples. Moreover, due to the modality distribution difference, directly utilizing continuous surrogates of hash codes to calculate similarity may induce suboptimal retrieval performance. To tackle these issues, in this paper, we propose a novel deep normalized cross-modal hashing scheme with bi-direction relation reasoning, named Bi NCMH. Specifically, we build the multi-level semantic similarity matrix by considering bi-direction relation, i.e., consistent and inconsistent relation. It hence can holistically characterize relations among instances. Besides, we execute feature normalization on continuous surrogates of hash codes to eliminate the deviation caused by modality gap, which further reduces the negative impact of binarization on retrieval performance. Extensive experiments on two cross-modal benchmark datasets demonstrate the superiority of our model over several state-of-the-art baselines.

关键词： computer vision Codes conferences Computational modeling Semantics Bidirectional control Benchmark testing

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Self-Supervised Voxel-Level Representation Rediscovers Subcellular Structures in Volume Electron Microscopy

Self-Supervised Voxel-Level Representation Rediscovers Subce...

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

作者： Han, Hongqing Dmitrieva, Mariia Sauer, Alexander Tam, Ka Ho Rittscher, Jens Univ Oxford Inst Biomed Engn Dept Engn Sci Oxford England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Making sense of large volumes of biological imaging data without human annotation often relies on unsupervised representation learning. Although efforts have been made to representing cropped-out microscopy images of single cells and single molecules, a more robust and general model that effectively maps every voxel in a whole cell volume onto a latent space is still lacking. Here, we use variational auto-encoder and metric learning to obtain a voxel-level representation, and explore using it for unsupervised segmentation. To our knowledge we are the first to present self-supervised voxel-level representation and subsequent unsupervised segmentation results for a complete cell. We improve upon earlier work by proposing an innovative approach to separate latent space into a semantic subspace and a transformational subspace, and only use the semantic representation for segmentation. We show that in the learned semantic representation the major subcellular components are visually distinguishable and the semantic subspace is more transformation-invariant than another sample latent subspace of equal dimension. For unsupervised segmentation we found that our model manages to automatically rediscover and separate the major classes with errors demonstrating spatial patterns, and further dissect the class not specified by reference segmentation into areas with consistent textures. Our segmentation outperforms a baseline by a large margin.

关键词： Representation learning Measurement Solid modeling Image segmentation computer vision conferences Biological system modeling

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Learning Generative Structure Prior for Blind Text Image Super-resolution

Learning Generative Structure Prior for Blind Text Image Sup...

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

作者： Li, Xiaoming Zuo, Wangmeng Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9798350301298

Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform character recognition in parallel to regularize the SR task, either through a loss constraint or intermediate feature condition. Nonetheless, the high-level prior could still fail when encountering severe degradation. The problem is further compounded given characters of complex structures, e.g., Chinese characters that combine multiple pictographic or ideographic symbols into a single character. In this work, we present a novel prior that focuses more on the character structure. In particular, we learn to encapsulate rich and diverse structures in a StyleGAN and exploit such generative structure priors for restoration. To restrict the generative space of StyleGAN so that it obeys the structure of characters yet remains flexible in handling different font styles, we store the discrete features for each character in a codebook. The code subsequently drives the StyleGAN to generate high-resolution structural details to aid text SR. Compared to priors based on character recognition, the proposed structure prior exerts stronger character-specific guidance to restore faithful and precise strokes of a designated character. Extensive experiments on synthetic and real datasets demonstrate the compelling performance of the proposed generative structure prior in facilitating robust text SR. Our code is available at https://***/csxmli2016/MARCONet.

关键词： Low-level vision

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