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VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution

VFHQ: A High-Quality Dataset and Benchmark for Video Face Su...

引用

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

作者： Xie, Liangbin Wang, Xintao Zhang, Honglun Dong, Chao Shan, Ying Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tencent PCG ARC Lab Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Most of the existing video face super-resolution (VFSR) methods are trained and evaluated on VoxCeleb1, which is designed specifically for speaker identification and the frames in this dataset are of low quality. As a consequence, the VFSR models trained on this dataset can not output visual-pleasing results. In this paper, we develop an automatic and scalable pipeline to collect a high-quality video face dataset (VFHQ), which contains over 16, 000 high-fidelity clips of diverse interview scenarios. To verify the necessity of VFHQ, we further conduct experiments and demonstrate that VFSR models trained on our VFHQ dataset can generate results with sharper edges and finer textures than those trained on VoxCeleb1. In addition, we show that the temporal information plays a pivotal role in eliminating video consistency issues as well as further improving visual performance. Based on VFHQ, by analyzing the benchmarking study of several state-of-the-art algorithms under bicubic and blind settings.

关键词： Training Visualization Privacy Face recognition Superresolution Pipelines Benchmark testing

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Pseudo-label Generation for Agricultural Robotics Applications

Pseudo-label Generation for Agricultural Robotics Applicatio...

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

作者： Ciarfuglia, Thomas A. Motoi, Ionut Marian Saraceni, Leonardo Nardi, Daniele Sapienza Univ Rome Dept Comp Sci Management & Automat Engn DIAG Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In the context of table grape cultivation there is rising interest in robotic solutions for harvesting, pruning, precision spraying and other agronomic tasks. Perception algorithms at the core of these systems require large amounts of labelled data, which in this context is often not available. In this work, we propose a semi-supervised solution to reduce the data needed to get state-of-the-art detection and segmentation of fruits in orchards. We present the case of table grape vineyards in southern Lazio (Italy) since grapes are a difficult fruit to segment due to occlusion, color and general illumination conditions. We consider the concrete scenario where the source labelled data is wine grape, while the target data is table grape, with considerable covariate shift. Starting from a simple video input, our method generates first bounding box labels, leveraging the structure from motion information, then segmentation masks, using the same weakly generated bounding box labels and a refining step based on Grabcut. This system is able to produce labels that considerably reduce the covariate shift from source to target data and that requires very limited data acquisition effort. Comparisons with State-of-the-art supervised solutions show how our methods are able to train new models that achieve high performances with few labelled images, with very simple labelling.

关键词： Structure from motion Motion segmentation Pipelines Refining Spraying Lighting pattern recognition

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Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos

Tragedy Plus Time: Capturing Unintended Human Activities fro...

引用

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

作者： Chakravarthy, Arnav Fang, Zhiyuan Yang, Yezhou Arizona State Univ Tempe AZ 85287 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In videos that contain actions performed unintentionally, agents do not achieve their desired goals. In such videos, it is challenging for computer vision systems to understand high-level concepts such as goal-directed behavior, an ability present in humans from a very early age. Inculcating this ability in artificially intelligent agents would make them better social learners by allowing them to evaluate human action under a teleological lens. To validate this ability of deep learning models to perform this task, we curate the W-Oops dataset, built upon the Oops dataset [11]. W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 30 unintentional video-level activity labels collected through human annotations. Due to the expensive segment annotation procedure, we propose a weakly supervised algorithm for localizing the goal-directed as well as unintentional temporal regions in the video leveraging solely video-level labels. In particular, we employ an attention mechanism based strategy that predicts the temporal regions which contributes the most to a classification task. Meanwhile, our designed overlap regularization allows the model to focus on distinct portions of the video for inferring the goal-directed and unintentional activity, while guaranteeing their temporal ordering. Extensive quantitative experiments verify the validity of our localization method. We further conduct a video captioning experiment which demonstrates that the proposed localization module does indeed assist teleological action understanding.

关键词： Location awareness computer vision Annotations Neural networks Prediction algorithms pattern recognition Intelligent agents

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deepPIC: Deep Perceptual Image Clustering For Identifying Bias In vision Datasets

deepPIC: Deep Perceptual Image Clustering For Identifying Bi...

引用

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

作者： Jaipuria, Nikita Stevo, Katherine Zhang, Xianling Gaopande, Meghana L. Garcia, Ian Calle Jain, Jinesh Murali, Vidya N. Ford Greenfield Labs Palo Alto CA 94304 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dataset bias in manually collected datasets is a known problem in computer vision. In safety-critical applications such as autonomous driving, these biases can lead to catastrophic errors from models trained on such datasets, jeopardizing the safety of users and their surroundings. Being able to unpuzzle the bias in a given dataset, and across datasets, is an essential tool for building safe and responsible AI. In this paper, we present deepPIC: deep Perceptual Image Clustering, a novel hierarchical clustering pipeline that leverages deep perceptual features to visualize and understand bias in unstructured and unlabeled datasets. It does so by effectively highlighting nuanced subcategories of information embedded within the data (such as multiple but repetitive shadow types) that typically are hard and/or expensive to annotate. Through experiments on a variety of image datasets, both open-source and internal, we demonstrate the effectiveness of deepPIC in (i) singling out errors in metadata from open-source datasets such as BDD100K;(ii) automatic nuanced metadata annotation;(iii) mining for edge cases;(iv) visualizing inherent bias both within and across multiple datasets;and (v) capturing synthetic data limitations;thus highlighting the wide variety of applications this pipeline can be applied to. All clustering results included here have been uploaded with image thumbnails on our project website - https://***/unpuzzle_dataset_bias/. We recommend zooming in for best impact.

关键词： Analytical models computer vision Visualization Annotations Pipelines Buildings Data visualization

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Few-Shot Image Classification Along Sparse Graphs

Few-Shot Image Classification Along Sparse Graphs

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

作者： Comer, Joseph F. Jacobson, Philip L. Hoffmann, Heiko HRL Labs LLC 3011 Malibu Canyon Rd Malibu CA 90265 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Few-shot learning remains a challenging problem, with unsatisfactory 1-shot accuracies for most real-world data. Here, we present a new perspective for data distributions in the feature space of a deep network and show how to exploit this perspective for few-shot learning. First, we observe that nearest neighbors in the feature space are with high probability members of the same class while generally two random points from one class are not much closer to each other than two points between classes. This observation suggests that classes in feature space form sparse, loosely connected graphs instead of dense clusters. To exploit this property, we propose using label propagation to the nearest unlabeled data and then using a kernel PCA reconstruction error as decision boundary in feature-space for the data distribution of each class. Using this method, which we call "K-Prop," we demonstrate largely improved few-shot learning performances (e.g., 83% accuracy for 1-shot 5-way classification on the RESISC45 satellite-images dataset) for datasets for which a backbone network can be trained to produce high within-class nearest-neighbor probabilities. We demonstrate this relationship using six different datasets.

关键词： Target tracking Limiting Shape Training data Streaming media pattern recognition Reliability

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Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency

Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyr...

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

作者： Huo, Dong Masoumzadeh, Abbas Yang, Yee-Hong Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Many deep learning based methods are designed to remove non-uniform (spatially variant) motion blur caused by object motion and camera shake without knowing the blur kernel. Some methods directly output the latent sharp image in one stage, while others utilize a multi-stage strategy (e.g. multi-scale, multi-patch, or multi-temporal) to gradually restore the sharp image. However, these methods have the following two main issues: 1) The computational cost of multi-stage is high;2) The same convolution kernel is applied in different regions, which is not an ideal choice for non-uniform blur. Hence, non-uniform motion deblurring is still a challenging and open problem. In this paper, we propose a new architecture which consists of multiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to deblur an image end-to-end with more flexibility. Multiple ASPDC modules implicitly learn the pixel-specific motion with different dilation rates in the same layer to handle movements of different magnitude. To improve the training, we also propose a reblurring network to map the deblurred output back to the blurred input, which constrains the solution space. Our experimental results show that the proposed method outperforms state-of-the-art methods on the benchmark datasets. The code is available at https://***/Dong-Huo/ASPDC.

关键词： Training Degradation Deep learning computer vision Convolution Image color analysis conferences

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What Should Be Equivariant In Self-Supervised Learning

What Should Be Equivariant In Self-Supervised Learning

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

作者： Xie, Yuyang Wen, Jianhong Lau, Kin Wai Rehman, Yasar Abbas Ur Shen, Jiajun TCL AI Lab Hong Kong Peoples R China Fuzhou Univ Fuzhou Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Self-supervised learning (SSL) aims to learn feature representation without human-annotated data. Existing methods approach this goal by encouraging the feature representations to be invariant under a set of task-irrelevant transformations and distortions defined a priori. However, multiple studies have shown that such an assumption often limits the expressive power of the representations and model would perform poorly when downstream tasks violate this assumption. For example, being invariant to rotations would prevent features from retaining enough information to estimate object rotation angles. This suggests additional manual work and domain knowledge are required for selecting augmentation types during SSL. In this work, we relax the transformation-invariance assumption by introducing a SSL framework that encourages the feature representations to preserve the order of transformation scale in embedding space for some transformations while maintaining invariance to other transformations. This allows the learned feature representations to retain information about task-relevant transformations. In addition, this framework gives rise to a handy mechanism to determine the augmentation types to which the features representations should be invariant and equivariant during SSL. We demonstrate the effectiveness of our method on various datasets such as Fruits 360, Caltech-UCSD Birds 200, and Blood cells dataset.

关键词： computer vision conferences Self-supervised learning Cells (biology) Feature extraction Distortion Birds

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Interpolation-Based Event Visual Data Filtering Algorithms

Interpolation-Based Event Visual Data Filtering Algorithms

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

作者： Kowalczyk, Marcin Kryjak, Tomasz AGH University of Krakow Embedded Vision Systems Group Poland

ISBN: (纸本)9798350302493

The field of neuromorphic vision is developing rapidly, and event cameras are finding their way into more and more applications. However, the data stream from these sensors is characterised by significant noise. In this paper, we propose a method for event data that is capable of removing approximately 99% of noise while preserving the majority of the valid signal. We have proposed four algorithms based on the matrix of infinite impulse response (IIR) filters method. We compared them on several event datasets that were further modified by adding artificially generated noise and noise recorded with dynamic vision sensor. The proposed methods use about 30KB of memory for a sensor with a resolution of 1280 × 720 and is therefore well suited for implementation in embedded devices. © 2023 ieee.

关键词： IIR filters

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Learning to Ask Informative Sub-Questions for Visual Question Answering

Learning to Ask Informative Sub-Questions for Visual Questio...

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

作者： Uehara, Kohei Duan, Nan Harada, Tatsuya Univ Tokyo Tokyo Japan Microsoft Res Asia Beijing Peoples R China Univ Tokyo RIKEN Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

VQA (Visual Question Answering) model tends to make incorrect inferences for questions that require reasoning over world knowledge. Recent study has shown that training VQA models with questions that provide lower-level perceptual information along with reasoning questions improves performance. Inspired by this, we propose a novel VQA model that generates questions to actively obtain auxiliary perceptual information useful for correct reasoning. Our model consists of a VQA model for answering questions, a Visual Question Generation (VQG) model for generating questions, and an Info-score model for estimating the amount of information the generated questions contain, which is useful in answering the original question. We train the VQG model to maximize the "informativeness" provided by the Info-score model to generate questions that contain as much information as possible, about the answer to the original question. Our experiments show that by in-putting the generated questions and their answers as additional information to the VQA model, it can indeed predict the answer more correctly than the baseline model.

关键词： Training Visualization computer vision Computational modeling conferences Reinforcement learning Predictive models

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Does Federated Dropout actually work?

Does Federated Dropout actually work?

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

作者： Cheng, Gary Charles, Zachary Garrett, Zachary Rush, Keith Stanford Univ Stanford CA 94305 USA Google Res Mountain View CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Model sizes are limited in Federated Learning due to network bandwidth and on-device memory constraints. The success of increasing model sizes in other machine learning domains motivates the development of methods for training large-scale models in Federated Learning. To this end, [3] draws inspiration from dropout and proposes Federated Dropout: an algorithm where clients train randomly selected subsets of a larger server model. Despite the promising empirical results and the many other works that build on it [1, 8, 13], we argue in this paper that the metrics used to measure the performance of Federated Dropout and its variants are misleading. We propose and perform new experiments which suggest that Federated Dropout is actually detrimental to scaling efforts. We show how a simple ensembling technique outperforms Federated Dropout and other baselines. We perform ablations that suggest that the best performing variations of Federated Dropout approximate ensembling. The simplicity of ensembling allows for easy, practical implementations. Furthermore, ensembling naturally leverages the parallelizable nature of Federated Learning-recall that it is easy to train several models independently because there are a lot of clients and server-compute is not the bottleneck. Ensembling's strong performance against our baselines suggests that Federated Learning models may be more easily scaled than previously thought with more sophisticated ensembling strategies e.g., via boosting.

关键词： Training Measurement computer vision Machine learning algorithms conferences Computational modeling Memory management

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