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Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image recognition

Incremental Meta-Learning via Episodic Replay Distillation f...

引用

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

作者： Wang, Kai Liu, Xialei Bagdanov, Andy Herranz, Luis Jui, Shangling van de Weijer, Joost Comp Vis Ctr Barcelona Spain Nankai Univ Tianjin Peoples R China Univ Florence MICC Florence Italy Huawei Kirin Solut Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more generalizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively.

关键词： Training computer vision Image recognition Upper bound conferences pattern recognition Task analysis

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Learning Generalized Feature for Temporal Action Detection: Application for Natural Driving Action recognition Challenge

Learning Generalized Feature for Temporal Action Detection: ...

引用

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

作者： Chuong Nguyen Ngoc Nguyen Su Huynh Vinh Nguyen Son Nguyen CyberCore AI Morioka Iwate Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reports our approach for the 2022 AI City Challenge - Naturalistic Driving Action recognition (Track 3), where the objective is to detect when and what kinds of actions that a driver performs in a long, untrimmed video. Our solution is built upon the single stage ActionFormer detector, in which temporal location and classification are predicted simultaneously for efficiency. The input feature for the detector is extracted offline using our proposed backbone, which we named "ConvNext-Video". However, due to the small size of the dataset, training the model to avoid over-fitting becomes challenging. To address this problem, we focus on training techniques that can improve the generalization of underlying features. Specifically, we utilize two methods: "learning without forgetting" and semi-weak supervised learning on the unlabeled data A2. Finally, we also add a second-stage classifier (SSC) using our ConvNeXt-Video backbone. The SSC Classifer is designed to combine information from multi-clips and multi-view cameras to improve the prediction precision. Our best result achieves 29.1 F1 score on the public test set. Our source code is released at link.

关键词： Training conferences Urban areas Supervised learning Detectors Feature extraction pattern recognition

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vision Transformers with Mixed-Resolution Tokenization

Vision Transformers with Mixed-Resolution Tokenization

引用

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

作者： Ronen, Tomer Levy, Omer Golbert, Avram Tel Aviv University Israel Google Research

ISBN: (纸本)9798350302493

vision Transformer models process input images by dividing them into a spatially regular grid of equal-size patches. Conversely, Transformers were originally introduced over natural language sequences, where each token represents a subword - a chunk of raw data of arbitrary size. In this work, we apply this approach to vision Transformers by introducing a novel image tokenization scheme, replacing the standard uniform grid with a mixed-resolution sequence of tokens, where each token represents a patch of arbitrary size. Using the Quadtree algorithm and a novel saliency scorer, we construct a patch mosaic where low-saliency areas of the image are processed in low resolution, routing more of the model's capacity to important image regions. Using the same architecture as vanilla ViTs, our Quadformer models achieve substantial accuracy gains on image classification when controlling for the computational budget. Code and models are publicly available at https://***/TomerRonen34/mixed-resolution-vit. © 2023 ieee.

关键词： Budget control

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Visual Domain Bridge: A source-free domain adaptation for cross-domain few-shot learning

Visual Domain Bridge: A source-free domain adaptation for cr...

引用

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

作者： Yazdanpanah, Moslem Moradi, Parham Univ Kurdistan Erbil Iraq

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the covariate shift, deep neural networks performance always degrades when applied to novel domains. In order to mitigate this problem, domain adaptation techniques require samples from target data during the feature extraction training, which is not always applicable in real-world scenarios. Batch Normalization is a known component of computer vision models, aiming at reducing the training-time covariate shift. However, facing distribution shift results in an internal state mismatch inside the Batch-Norm layers during the inference time. In favor of alleviating the induced mismatch, this paper proposes a source-free, lightweight and straightforward approach by introducing the "Visual Domain Bridge" concept reducing the BatchNorm's internal mismatch in the cross-domain settings. Compared to the other BatchNorm-based source-free domain adaptation techniques such as AdaBN and Prediction-BN, our method formed a new state-of-the-art cross-domain few-shot fine-tuning method neglecting extra augmentations;while improving the performance in near-domain settings too. The proposed method can integrate with other domain adaptation methods and enhance their performance requiring just a few lines of modification in the BatchNorm's implementation. Implementations are available in https://***/MosyMosy/VDB

关键词： Bridges Training Deep learning Visualization computer vision conferences Neural networks

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SphereGlue: Learning Keypoint Matching on High Resolution Spherical Images

SphereGlue: Learning Keypoint Matching on High Resolution Sp...

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

作者： Gava, Christiano Mukunda, Vishal Habtegebrial, Tewodros Raue, Federico Palacio, Sebastian Dengel, Andreas Dfki Germany University of Kaiserslautern-Landau Germany

ISBN: (纸本)9798350302493

Traditionally, spherical keypoint matching has been performed using greedy algorithms, such as Nearest Neighbors (NN) search. NN based algorithms often lead to erroneous or insufficient matches as they fail to leverage global keypoint neighborhood information. Inspired by a recent learned perspective matching approach [53] we introduce SphereGlue: a Graph Neural Network based feature matching for high-resolution spherical images. The proposed model naturally handles the severe distortions resulting from geometric transformations. Rigorous evaluations demonstrate the efficacy of SphereGlue in matching both learned and handcrafted keypoints, on synthetic and real high-resolution spherical images. Moreover, SphereGlue generalizes well to previously unseen real-world and synthetic scenes. Results on camera pose estimation show that SphereGlue can directly replace state-of-the-art matching algorithms, in downstream tasks. © 2023 ieee.

关键词： computer vision

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GP22: A Car Styling Dataset for Automotive Designers

GP22: A Car Styling Dataset for Automotive Designers

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

作者： Lee, Gyunpyo Kim, Taesu Suk, Hyeon-Jeong Korea Adv Inst Sci & Technol Dept Ind Design Color Lab Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

An automated design data archiving could reduce the time wasted by designers from working creatively and effectively. Though many datasets on classifying, detecting, and instance segmenting on car exterior exist, these large datasets are not relevant for design practices as the primary purpose lies in autonomous driving or vehicle verification. Therefore, we release GP22, composed of car styling features defined by automotive designers. The dataset contains 1480 car side profile images from 37 brands and ten car segments. It also contains annotations of design features that follow the taxonomy of the car exterior design features defined in the eye of the automotive designer. We trained the baseline model using YOLO v5 as the design feature detection model with the dataset. The presented model resulted in an mAP score of 0.995 and a recall of 0.984. Furthermore, exploration of the model performance on sketches and rendering images of the car side profile implies the scalability of the dataset for design purposes.

关键词： Image segmentation Annotations Feature detection Scalability Taxonomy Rendering (computer graphics) pattern recognition

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Concept Activation Vectors for Generating User-Defined 3D Shapes

Concept Activation Vectors for Generating User-Defined 3D Sh...

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

作者： Druc, Stefan Balu, Aditya Wooldridge, Peter Krishnamurthy, Adarsh Sarkar, Soumik MonolithAI 12-18 Hoxton St London N1 6NG England Iowa State Univ Ames IA 50011 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We explore the interpretability of 3D geometric deep learning models in the context of computer-Aided Design (CAD). The field of parametric CAD can be limited by the difficulty of expressing high-level design concepts in terms of a few numeric parameters. In this paper, we use a deep learning architectures to encode high dimensional 3D shapes into a vectorized latent representation that can be used to describe arbitrary concepts. Specifically, we train a simple auto-encoder to parameterize a dataset of complex shapes. To understand the latent encoded space, we use the idea of Concept Activation Vectors (CAV) to reinterpret the latent space in terms of user-defined concepts. This allows modification of a reference design to exhibit more or fewer characteristics of a chosen concept or group of concepts. We also test the statistical significance of the identified concepts and determine the sensitivity of a physical quantity of interest across the dataset.

关键词： Deep learning Solid modeling Three-dimensional displays Design automation Sensitivity Shape pattern recognition

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An Effective Framework of Multi-Class Product Counting and recognition for Automated Retail Checkout

An Effective Framework of Multi-Class Product Counting and R...

引用

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

作者： Wan, Junfeng Qian, Shuhao Tian, Zihan Zhao, Yanyun Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

As the field of computer vision grows, Automated Retail Checkout has become a highly anticipated development goal. The key of this task is to improve the accuracy rate. If there is an error, it will bring serious losses to the business and awful experience for customers which is not our expected. This competition gives us an opportunity to simulate check-out in a real world scenario, so that we can identify problems and solve them, not only for the competition, but also for the practical application. As one of the participating teams in this task, we pursue the goal of avoiding misdetection and misclassification, and build a complete set of framework to achieve high-precise, high-recall performance. In addition, there is an excessive difference between the training data and test data. How to use limited data to make up for the differences in this part is also one of the highlights of our framework. In general, our framework consists of three main parts. Firstly, the Pre-Processing module to make up for the differences between training and test data. The DTC module completes the overall process of automatic recognition. Finally the MTCR module is proposed to post-process the output of the DTC module. On the TestA data of AICITY2022 Task 4, we have achieved significant result compared to the other teams. Finally, our model is ranked 1st in AICITY2022 Task 4 [17].

关键词： Training computer vision Codes conferences Training data Trajectory pattern recognition

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Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets

Delving into High-Quality Synthetic Face Occlusion Segmentat...

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

作者： Voo, Kenny T. R. Jiang, Liming Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper performs comprehensive analysis on datasets for occlusion-aware face segmentation, a task that is crucial for many downstream applications. The collection and annotation of such datasets are time-consuming and labor-intensive. Although some efforts have been made in synthetic data generation, the naturalistic aspect of data remains less explored. In our study, we propose two occlusion generation techniques, Naturalistic Occlusion Generation (NatOcc), for producing high-quality naturalistic synthetic occluded faces;and Random Occlusion Generation (RandOcc), a more general synthetic occluded data generation method (Figure 1). We empirically show the effectiveness and robustness of both methods, even for unseen occlusions. To facilitate model evaluation, we present two high-resolution real-world occluded face datasets with fine-grained annotations, RealOcc and RealOcc-Wild, featuring both careful alignment preprocessing and an in-the-wild setting for robustness test. We further conduct a comprehensive analysis on a newly introduced segmentation benchmark, offering insights for future exploration. Our code and dataset are available at https://***/kennyvoo/faceocclusion-generation.

关键词： computer vision Codes Annotations Face recognition conferences Benchmark testing Robustness

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Diffusart: Enhancing Line Art Colorization with Conditional Diffusion Models

Diffusart: Enhancing Line Art Colorization with Conditional ...

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

作者： Carrillo, Hernan Clément, Michaël Bugeau, Aurélie Simo-Serra, Edgar Univ. Bordeaux CNRS Bordeaux INP LaBRI UMR 5800 France France Waseda University Japan

ISBN: (纸本)9798350302493

Colorization of line art drawings is an important task in illustration and animation workflows. However, this highly laborious process is mainly done manually, limiting the creative productivity. This paper presents a novel interactive approach for line art colorization using conditional Diffusion Probabilistic Models (DPMs). In our proposed approach, the user provides initial color strokes for colorizing the line art. The strokes are then integrated into the conditional DPM-based colorization process by means of a coupled implicit and explicit conditioning strategy to generates diverse and high-quality colorized images. We evaluate our proposal and show it outperforms existing state-of-the-art approaches using the FID, LPIPS and SSIM metrics. © 2023 ieee.

关键词： computer vision

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