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Adaptive Deep Neural Network Inference Optimization with EENet

Adaptive Deep Neural Network Inference Optimization with EEN...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Ilhan, Fatih Chow, Ka-Ho Hu, Sihao Huang, Tiansheng Tekin, Selim Wei, Wenqi Wu, Yanzhao Lee, Myungjin Kompella, Ramana Latapie, Hugo Liu, Gaowen Liu, Ling Georgia Inst Technol Atlanta GA 30332 USA CISCO Res San Jose CA USA

ISBN: (纸本)9798350318920;9798350318937

Well-trained deep neural networks (DNNs) treat all test samples equally during prediction. Adaptive DNN inference with early exiting leverages the observation that some test examples can be easier to predict than others. This paper presents EENet, a novel early-exiting scheduling framework for multi-exit DNN models. Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit. As opposed to previous early-exiting solutions with heuristics-based methods, our EENet framework optimizes an early-exiting policy to maximize model accuracy while satisfying the given per-sample average inference budget. Extensive experiments are conducted on four computer vision datasets (CIFAR-10, CIFAR-100, ImageNet, Cityscapes) and two NLP datasets (SST-2, AgNews). The results demonstrate that the adaptive inference by EENet can outperform the representative existing early exit techniques. We also perform a detailed visualization analysis of the comparison results to interpret the benefits of EENet.

关键词： Algorithms Image recognition and understanding

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3SD: Self-Supervised Saliency Detection With No Labels

3SD: Self-Supervised Saliency Detection With No Labels

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Yasarla, Rajeev Weng, Renliang Choi, Wongun Patel, Vishal M. Sadeghian, Amir Johns Hopkins Univ Baltimore MD 21218 USA AIBEE New Delhi India

ISBN: (纸本)9798350318920;9798350318937

We present a conceptually simple self-supervised method for saliency detection. Our method generates and uses pseudo-ground truth labels for training. The generated pseudo-GT labels don't require any kind of human annotations (e.g., pixel-wise labels or weak labels like scribbles). Recent works show that features extracted from classification tasks provide important saliency cues like structure and semantic information of salient objects in the image. Our method, called 3SD, exploits this idea by adding a branch for a self-supervised classification task in parallel with salient object detection, to obtain class activation maps (CAM maps). These CAM maps along with the edges of the input image are used to generate the pseudo-GT saliency maps to train our 3SD network. Specifically, we propose a contrastive learning-based training on multiple image patches for the classification task. We show the multi-patch classification with contrastive loss improves the quality of the CAM maps compared to naive classification on the entire image. Experiments on six bench-mark datasets demonstrate that without any labels, our 3SD method outperforms all existing weakly supervised and unsupervised methods, and its performance is on par with the fully-supervised methods.

关键词： Algorithms Image recognition and understanding

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Adaptive manifold for imbalanced transductive few-shot learning

Adaptive manifold for imbalanced transductive few-shot learn...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Lazarou, Michalis Avrithis, Yannis Stathaki, Tania Imperial Coll London London England Inst Adv Res Artificial Intelligence IARAI Vienna Austria

ISBN: (纸本)9798350318920;9798350318937

Transductive few-shot learning algorithms have showed substantially superior performance over their inductive counterparts by leveraging the unlabeled queries at inference. However, the vast majority of transductive methods are evaluated on perfectly class-balanced benchmarks. It has been shown that they undergo remarkable drop in performance under a more realistic, imbalanced setting. To this end, we propose a novel algorithm to address imbalanced transductive few-shot learning, named Adaptive Manifold. Our algorithm exploits the underlying manifold of the labeled examples and unlabeled queries by using manifold similarity to predict the class probability distribution of every query. It is parameterized by one centroid per class and a set of manifold parameters that determine the manifold. All parameters are optimized by minimizing a loss function that can be tuned towards class-balanced or imbalanced distributions. The manifold similarity shows substantial improvement over Euclidean distance, especially in the 1-shot setting. Our algorithm outperforms all other state of the art methods in three benchmark datasets, namely miniImageNet, tieredImageNet and CUB, and two different backbones, namely ResNet-18 and WideResNet-28-10. In certain cases, our algorithm outperforms the previous state of the art by as much as 4.2%. The publicly available source code can be found in https://***/MichalisLazarou/AM

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Evidential Uncertainty Quantification: A Variance-Based Perspective

Evidential Uncertainty Quantification: A Variance-Based Pers...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Duan, Ruxiao Caffo, Brian Bai, Harrison X. Sair, Haris I. Jones, Craig Johns Hopkins Univ Baltimore MD 21218 USA Johns Hopkins Univ Sch Med Baltimore MD USA

ISBN: (纸本)9798350318920;9798350318937

Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advances in evidential deep learning shed light on the direct quantification of aleatoric and epistemic uncertainties with a single forward pass of the model. Most traditional approaches adopt an entropy-based method to derive evidential uncertainty in classification, quantifying uncertainty at the sample level. However, the variance-based method that has been widely applied in regression problems is seldom used in the classification setting. In this work, we adapt the variance-based approach from regression to classification, quantifying classification uncertainty at the class level. The variance decomposition technique in regression is extended to class covariance decomposition in classification based on the law of total covariance, and the class correlation is also derived from the covariance. Experiments on cross-domain datasets are conducted to illustrate that the variance-based approach not only results in similar accuracy as the entropy-based one in active domain adaptation but also brings information about class-wise uncertainties as well as between-class correlations. The code is available at https://***/KerryDRX/EvidentialADA. This alternative means of evidential uncertainty quantification will give researchers more options when class uncertainties and correlations are important in their applications.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Tunable Hybrid Proposal Networks for the Open World

Tunable Hybrid Proposal Networks for the Open World

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Inkawhich, Matthew Inkawhich, Nathan Li, Hai Chen, Yiran Duke Univ Durham NC 27706 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9798350318920;9798350318937

Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes. These models fail to provide high recall in open-world environments where important novel objects may be encountered. While a handful of recent works attempt to tackle this problem, they fail to consider that the optimal behavior of a proposal network can vary significantly depending on the data and application. Our goal is to provide a flexible proposal solution that can be easily tuned to suit a variety of open-world settings. To this end, we design a Tunable Hybrid Proposal Network (THPN) that leverages an adjustable hybrid architecture, a novel self-training procedure, and dynamic loss components to optimize the tradeoff between known and unknown object detection performance. To thoroughly evaluate our method, we devise several new challenges which invoke varying degrees of label bias by altering known class diversity and label count. We find that in every task, THPN easily outperforms existing baselines (e.g., RPN, OLN). Our method is also highly data efficient, surpassing baseline recall with a fraction of the labeled data.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Semantic Generative Augmentations for Few-Shot Counting

Semantic Generative Augmentations for Few-Shot Counting

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Doubinsky, Perla Audebert, Nicolas Crucianu, Michel Le Borgne, Herve Cnam Paris CEDRIC EA4329 Paris France Univ Paris Saclay CEA List Palaiseau France

ISBN: (纸本)9798350318920;9798350318937

With the availability of powerful text-to-image diffusion models, recent works have explored the use of synthetic data to improve image classification performances. These works show that it can effectively augment or even replace real data. In this work, we investigate how synthetic data can benefit few-shot class-agnostic counting. This requires to generate images that correspond to a given input number of objects. However, text-to-image models struggle to grasp the notion of count. We propose to rely on a double conditioning of Stable Diffusion with both a prompt and a density map in order to augment a training dataset for few-shot counting. Due to the small dataset size, the fine-tuned model tends to generate images close to the training images. We propose to enhance the diversity of synthesized images by exchanging captions between images thus creating unseen configurations of object types and spatial layout. Our experiments show that our diversified generation strategy significantly improves the counting accuracy of two recent and performing few-shot counting models on FSC147 and CARPK.

关键词： 3D Algorithms Algorithms etc. Generative models for image Image recognition and understanding video

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Learning Class and Domain Augmentations for Single-Source Open-Domain Generalization

Learning Class and Domain Augmentations for Single-Source Op...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Bele, Prathmesh Bundele, Valay Bhattacharya, Avigyan Jha, Ankit Roig, Gemma Banerjee, Biplab Indian Inst Technol Bombay Maharashtra India Goethe Univ Frankfurt Frankfurt Germany

ISBN: (纸本)9798350318920;9798350318937

Single-source open-domain generalization (SS-ODG) addresses the challenge of labeled source domains with supervision during training and unlabeled novel target domains during testing. The target domain includes both known classes from the source domain and samples from previously unseen classes. Existing techniques for SS-ODG primarily focus on calibrating source-domain classifiers to identify open samples in the target domain. However, these methods struggle with visually fine-grained open-closed data, often misclassifying open samples as closed-set classes. Moreover, relying solely on a single source domain restricts the model's ability to generalize. To overcome these limitations, we propose a novel framework called SODG-NET that simultaneously synthesizes novel domains and generates pseudo-open samples using a learning-based objective, in contrast to the ad-hoc mixing strategies commonly found in the literature. Our approach enhances generalization by diversifying the styles of known class samples using a novel metric criterion and generates diverse pseudo-open samples to train a unified and confident multiclass classifier capable of handling both open and closed-set data. Extensive experimental evaluations conducted on multiple benchmarks consistently demonstrate the superior performance of SODG- NET compared to the literature.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection

ReConPatch : Contrastive Patch Representation Learning for I...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Hyun, Jeeho Kim, Sangyun Jeon, Giyoung Kim, Seung Hwan Bae, Kyunghoon Kang, Byung Jun LG AI Res Bundang Dong South Korea

ISBN: (纸本)9798350318920;9798350318937

Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the rare observations and unknown types of defects, anomaly detection is considered to be challenging in machine learning. To overcome this difficulty, recent approaches utilize the common visual representations pre-trained from natural image datasets and distill the relevant features. However, existing approaches still have the discrepancy between the pre-trained feature and the target data, or require the input augmentation which should be carefully designed, particularly for the industrial dataset. In this paper, we introduce ReConPatch, which constructs discriminative features for anomaly detection by training a linear modulation of patch features extracted from the pre-trained model. ReConPatch employs contrastive representation learning to collect and distribute features in a way that produces a target-oriented and easily separable representation. To address the absence of labeled pairs for the contrastive learning, we utilize two similarity measures between data representations, pairwise and contextual similarities, as pseudo-labels. Our method achieves the state-of-the-art anomaly detection performance (99.72%) for the widely used and challenging MVTec AD dataset. Additionally, we achieved a state-of-the-art anomaly detection performance (95.8%) for the BTAD dataset.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Object Re-Identification from Point Clouds

Object Re-Identification from Point Clouds

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Therien, Benjamin Huang, Chengjie Chow, Adrian Czarnecki, Krzysztof Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798350318920;9798350318937

Object re-identification (ReID) from images plays a critical role in application domains of image retrieval (surveillance, retail analytics, etc.) and multi-object tracking (autonomous driving, robotics, etc.). However, systems that additionally or exclusively perceive the world from depth sensors are becoming more commonplace without any corresponding methods for object ReID. In this work, we fill the gap by providing the first large-scale study of object ReID from point clouds and establishing its performance relative to image ReID. To enable such a study, we create two large-scale ReID datasets with paired image and LiDAR observations and propose a lightweight matching head that can be concatenated to any set or sequence processing backbone (e.g., PointNet or ViT), creating a family of comparable object ReID networks for both modalities. Run in Siamese style, our proposed point cloud ReID networks can make thousands of pairwise comparisons in real-time (10 Hz). Our findings demonstrate that their performance increases with higher sensor resolution and approaches that of image ReID when observations are sufficiently dense. Our strongest network trained at the largest scale achieves ReID accuracy exceeding 90% for rigid objects and 85% for deformable objects (without any explicit skeleton normalization). To our knowledge, we are the first to study object re-identification from real point cloud observations. Our code is available at https://***/bentherien/point-cloud-reid.

关键词： 3D computer vision Algorithms Algorithms Applications Robotics Video recognition and understanding

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EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection

EResFD: Rediscovery of the Effectiveness of Standard Convolu...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Jeong, Joonhyun Kim, Beomyoung Yu, Joonsang Yoo, YoungJoon NAVER Cloud ImageVis Seongnam South Korea Korea Adv Inst Sci & Technol Daejeon South Korea NAVER AI Lab Grenoble France

ISBN: (纸本)9798350318920;9798350318937

This paper analyzes the design choices of face detection architecture that improve efficiency of computation cost and accuracy. Specifically, we re-examine the effectiveness of the standard convolutional block as a lightweight backbone architecture for face detection. Unlike the current tendency of lightweight architecture design, which heavily utilizes depthwise separable convolution layers, we show that heavily channel-pruned standard convolution layers can achieve better accuracy and inference speed when using a similar parameter size. This observation is supported by the analyses concerning the characteristics of the target data domain, faces. Based on our observation, we propose to employ ResNet with a highly reduced channel, which surprisingly allows high efficiency compared to other mobilefriendly networks (e.g., MobileNetV1, V2, V3). From the extensive experiments, we show that the proposed backbone can replace that of the state-of-the-art face detector with a faster inference speed. Also, we further propose a new feature aggregation method to maximize the detection performance. Our proposed detector EResFD obtained 80.4% mAP on WIDER FACE Hard subset which only takes 37.7 ms for VGA image inference on CPU. Code is available at https://***/clovaai/EResFD.

关键词： Algorithms Image recognition and understanding

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