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CASTing Your Model: Learning to Localize Improves Self-Supervised Representations

CASTing Your Model: Learning to Localize Improves Self-Super...

引用

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

作者： Selvaraju, Ramprasaath R. Desai, Karan Johnson, Justin Naik, Nikhil Salesforce Res Palo Alto CA 94301 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665445092

Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. Despite their success these methods have been primarily applied to unlabeled ImageNet images, and show marginal gains when trained on larger sets of uncurated images. We hypothesize that current SSL methods perform best on iconic images, and struggle on complex scene images with many objects. Analyzing contrastive SSL methods shows that they have poor visual grounding and receive poor supervisory signal when trained on scene images. We propose Contrastive Attention-Supervised Tuning (CAST) to overcome these limitations. CAST uses unsupervised saliency maps to intelligently sample crops, and to provide grounding supervision via a Grad-CAM attention loss. Experiments on COCO show that CAST significantly improves the features learned by SSL methods on scene images, and further experiments show that CAST-trained models are more robust to changes in backgrounds. Our code is available at https://***/salesforce/CAST/.

关键词： Visualization computer vision Correlation Codes Grounding Crops Robustness

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Convolutional Hough Matching Networks

Convolutional Hough Matching Networks

引用

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

作者： Min, Juhong Cho, Minsu POSTECH CSE Pohang South Korea POSTECH GSAI Pohang South Korea

ISBN: (纸本)9781665445092

Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM). The method distributes similarities of candidate matches over a geometric transformation space and evaluate them in a convolutional manner. We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. To validate the effect, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.

关键词： Visualization computer vision Semantics Neural networks Transforms Benchmark testing Robustness

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LQF: Linear Quadratic Fine-Tuning

LQF: Linear Quadratic Fine-Tuning

引用

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

作者： Achille, Alessandro Golatkar, Aditya Ravichandran, Avinash Polito, Marzia Soatto, Stefano Amazon Web Serv Seattle WA 98109 USA Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781665445092

Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial conditions, and optimization. Such desirable properties are absent in deep neural networks (DNNs), typically trained by non-linear fine-tuning of a pre-trained model. Previous attempts to linearize DNNs have led to interesting theoretical insights, but have not impacted the practice due to the substantial performance gap compared to standard non-linear optimization. We present the first method for linearizing a pre-trained model that achieves comparable performance to non-linear fine-tuning on most of real-world image classification tasks tested, thus enjoying the interpretability of linear models without incurring punishing losses in performance. LQF consists of simple modifications to the architecture, loss function and optimization typically used for classification: Leaky-ReLU instead of ReLU, mean squared loss instead of cross-entropy, and pre-conditioning using Kronecker factorization. None of these changes in isolation is sufficient to approach the performance of non-linear fine-tuning. When used in combination, they allow us to reach comparable performance, and even superior in the low-data regime, while enjoying the simplicity, robustness and interpretability of linear-quadratic optimization.

关键词： Deep learning computer vision Training data computer architecture Robustness pattern recognition Task analysis

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PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training

PixMatch: Unsupervised Domain Adaptation via Pixelwise Consi...

引用

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

作者： Melas-Kyriazi, Luke Manrai, Arjun K. Harvard Univ Cambridge MA 02138 USA Harvard Univ Boston Childrens Hosp Boston MA 02115 USA

ISBN: (纸本)9781665445092

Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming. In semantic segmentation, it is attractive to train models on annotated images from a simulated (source) domain and deploy them on real (target) domains. In this work, we present a novel framework for unsupervised domain adaptation based on the notion of target-domain consistency training. Intuitively, our work is based on the idea that in order to perform well on the target domain, a model's output should be consistent with respect to small perturbations of inputs in the target domain. Specifically, we introduce a new loss term to enforce pixelwise consistency between the model's predictions on a target image and a perturbed version of the same image. In comparison to popular adversarial adaptation methods, our approach is simpler, easier to implement, and more memory-efficient during training. Experiments and extensive ablation studies demonstrate that our simple approach achieves remarkably strong results on two challenging synthetic-to-real benchmarks, GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes.

关键词： Training Adaptation models Image segmentation computer vision Perturbation methods Computational modeling Semantics

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Efficient Object Embedding for Spliced Image Retrieval

Efficient Object Embedding for Spliced Image Retrieval

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

作者： Chen, Bor-Chun Wu, Zuxuan Davis, Larry S. Lim, Ser-Nam Univ Maryland College Pk MD 20742 USA Facebook AI Menlo Pk CA 94025 USA Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

Detecting spliced images is one of the emerging challenges in computer vision. Unlike prior methods that focus on detecting low-level artifacts generated during the manipulation process, we use an image retrieval approach to tackle this problem. When given a spliced query image, our goal is to retrieve the original image from a database of authentic images. To achieve this goal, we propose representing an image by its constituent objects based on the intuition that the finest granularity of manipulations is often-times at the object-level. We introduce a framework, object embeddings for spliced image retrieval (OE-SIR), that utilizes modern object detectors to localize object regions. Each region is then embedded and collectively used to represent the image. Further, we propose a student-teacher training paradigm for learning discriminative embeddings within object regions to avoid expensive multiple forward passes. Detailed analysis of the efficacy of different feature embedding models is also provided in this study. Extensive experimental results show that the OE-SIR achieves state-of-the-art performance in spliced image retrieval.

关键词： Training Analytical models computer vision Databases Computational modeling Image retrieval Detectors

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Single-Stage Instance Shadow Detection with Bidirectional Relation Learning

Single-Stage Instance Shadow Detection with Bidirectional Re...

引用

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

作者： Wang, Tianyu Hu, Xiaowei Fu, Chi-Wing Heng, Pheng-Ann Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Hong Kong Macao Joint Lab Human Machine Beijing Peoples R China

ISBN: (纸本)9781665445092

Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows. The previous work adopts a two-stage framework to first predict shadow instances, object instances, and shadow-object associations from the region proposals, then leverage a post-processing to match the predictions to form the final shadow-object pairs. In this paper, we present a new single-stage fully-convolutional network architecture with a bidirectional relation learning module to directly learn the relations of shadow and object instances in an end-to-end manner. Compared with the prior work, our method actively explores the internal relationship between shadows and objects to learn a better pairing between them, thus improving the overall performance for instance shadow detection. We evaluate our method on the benchmark dataset for instance shadow detection, both quantitatively and visually. The experimental results demonstrate that our method clearly outperforms the state-of-the-art method.

关键词： Knowledge engineering computer vision computer graphics Benchmark testing Network architecture pattern recognition Internet

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ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning

ReAgent: Point Cloud Registration using Imitation and Reinfo...

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

作者： Bauer, Dominik Patten, Timothy Vincze, Markus TU Wien Vienna Austria

ISBN: (纸本)9781665445092

Point cloud registration is a common step in many 3D computer vision tasks such as object pose estimation, where a 3D model is aligned to an observation. Classical registration methods generalize well to novel domains but fail when given a noisy observation or a bad initialization. Learning-based methods, in contrast, are more robust but lack in generalization capacity. We propose to consider iterative point cloud registration as a reinforcement learning task and, to this end, present a novel registration agent (ReAgent). We employ imitation learning to initialize its discrete registration policy based on a steady expert policy. Integration with policy optimization, based on our proposed alignment reward, further improves the agent's registration performance. We compare our approach to classical and learning-based registration methods on both ModelNet40 (synthetic) and ScanObjectNN (real data) and show that our ReAgent achieves state-of-the-art accuracy. The lightweight architecture of the agent, moreover, enables reduced inference time as compared to related approaches. Code is available at ***/dornik/reagent.

关键词： computer vision Solid modeling Three-dimensional displays Pose estimation Reinforcement learning Real-time systems Trajectory

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CondenseNet V2: Sparse Feature Reactivation for Deep Networks

CondenseNet V2: Sparse Feature Reactivation for Deep Network...

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

作者： Yang, Le Jiang, Haojun Cai, Ruojin Wang, Yulin Song, Shiji Huang, Gao Tian, Qi Tsinghua Univ Dept Automat Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Cornell Univ Ithaca NY 14853 USA Huawei Cloud & AI Ithaca NY USA

ISBN: (纸本)9781665445092

Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet [14] has shown that this mechanism can befiirther improved if redundant features are removed. In this paper, we propose an alternative approach named sparse feature reactivation (SFR), aiming at actively increasing the utility of features for reusing. In the proposed network, named CondenseNetV2, each layer can simultaneously learn to 1) selectively reuse a set of most important features from preceding layers;and 2) actively update a set of preceding features to increase their utility for later layers. Our experiments show that the proposed models achieve promising performance on image classification (ImageNet and CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency and practical speed.

关键词： Training computer vision Computational modeling Object detection computer architecture Search problems pattern recognition

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引用

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

作者： Faraki, Masoud Yu, Xiang Tsai, Yi-Hsuan Suh, Yumin Chandraker, Manmohan NEC Labs Amer Princeton NJ 08540 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781665445092

Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as a novel ethnicity or unpredictable individual make-ups during test time. In this paper, we introduce a novel cross-domain metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve face recognition in unseen domains. The CDT loss encourages learning semantically meaningful features by enforcing compact feature clusters of identities from one domain, where the compactness is measured by underlying similarity metrics that belong to another training domain with different statistics. Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains. The network parameters are further enforced to learn generalized features under domain shift, in a model-agnostic learning pipeline. Unlike the recent work of Meta Face recognition [18], our method does not require careful hard-pair sample mining and filtering strategy during training. Extensive experiments on various face recognition benchmarks show the superiority of our method in handling variations, compared to baseline and the state-of-the-art methods.

关键词： Training computer vision Filtering Face recognition Pipelines Benchmark testing Loss measurement

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EGF: An Improved Edge Detection Model for Low-Resolution Images 2

EGF: An Improved Edge Detection Model for Low-Resolution Ima...

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2nd ieee International conference on Futuristic Technologies, INCOFT 2023

作者： Deepak Raj, D.M. Shanmuganathan, Harinee Geetha, A. Keerthika, V. Alliance University Department of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350308846

Edge detection can benefit many different industries and domains, including computer vision, machine learning, image analysis, remote sensing, thermal imaging, pattern recognition, and medical imaging. The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in a picture serve as the object's limits and can reveal crucial details about the object's size, shape, and position. Since low-resolution images have low pixel densities or pixel values, which muddy the images, detecting edges in them is demanding work. This paper proposes a novel edge-detection approach called EGF (Extended Gaussian Filter) for low-resolution images. EGF utilizes the basic concept of Gaussian filter to find the edges of images. The objective function of EGF is developed to reduce the noise and pixel differentiation in images. The outcomes show that the suggested strategy outperforms the conventional edge detection technique. © 2023 ieee.

关键词： edge detection gaussian filter image processing machine learning pre-processing

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