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Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting

Towards High-Quality and Efficient Video Super-Resolution vi...

引用

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

作者： Li, Gen Ji, Jie Qin, Minghai Niu, Wei Ren, Bin Afghah, Fatemeh Guo, Linke Ma, Xiaolong Clemson Univ Clemson SC 29631 USA William & Mary Williamsburg VA USA

ISBN: (纸本)9798350301298

As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has become a new trend in the modern video delivery system. By dividing videos into chunks and overfitting each chunk with a super-resolution model, the server encodes videos before transmitting them to the clients, thus achieving better video quality and transmission efficiency. However, a large number of chunks are expected to ensure good overfitting quality, which substantially increases the storage and consumes more bandwidth resources for data transmission. On the other hand, decreasing the number of chunks through training optimization techniques usually requires high model capacity, which significantly slows down execution speed. To reconcile such, we propose a novel method for high-quality and efficient video resolution upscaling tasks, which leverages the spatial-temporal information to accurately divide video into chunks, thus keeping the number of chunks as well as the model size to minimum. Additionally, we advance our method into a single overfitting model by a data-aware joint training technique, which further reduces the storage requirement with negligible quality drop. We deploy our models on an off-the-shelf mobile phone, and experimental results show that our method achieves real-time video super-resolution with high video quality. Compared with the state-of-the-art, our method achieves 28 fps streaming speed with 41.6 PSNR, which is 14x faster and 2.29 dB better in the live video resolution upscaling tasks. Code available in https://***/coulsonlee/***.

关键词： Efficient and scalable vision

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PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery

PromptCAL: Contrastive Affinity Learning via Auxiliary Promp...

引用

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

作者： Zhang, Sheng Khan, Salman Shen, Zhiqiang Naseer, Muzammal Chen, Guangyi Khan, Fahad Shahbaz Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Hong Kong Univ Sci & Technol Hong Kong Peoples R China Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)9798350301298

Although existing semi-supervised learning models achieve remarkable success in learning with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled from novel semantic classes due to their closed-set assumption. In this work, we target a pragmatic but under-explored Generalized Novel Category Discovery (GNCD) setting. The GNCD setting aims to categorize unlabeled training data coming from known and novel classes by leveraging the information of partially labeled known classes. We propose a two-stage Contrastive Affinity Learning method with auxiliary visual Prompts, dubbed PromptCAL, to address this challenging problem. Our approach discovers reliable pairwise sample affinities to learn better semantic clustering of both known and novel classes for the class token and visual prompts. First, we propose a discriminative prompt regularization loss to reinforce semantic discriminativeness of prompt-adapted pre-trained vision transformer for refined affinity relationships. Besides, we propose contrastive affinity learning to calibrate semantic representations based on our iterative semi-supervised affinity graph generation method for semantically-enhanced supervision. Extensive experimental evaluation demonstrates that our PromptCAL method is more effective in discovering novel classes even with limited annotations and surpasses the current state-of-the-art on generic and fine-grained benchmarks (e.g., with nearly 11% gain on CUB-200, and 9% on ImageNet-100) on overall accuracy. Our code is available at https: // ***/ sheng- eatamath / PromptCAL.

关键词： detection recognition: Categorization retrieval

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Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection

Ambiguity-Resistant Semi-Supervised Learning for Dense Objec...

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

作者： Liu, Chang Zhang, Weiming Lin, Xiangru Zhang, Wei Tan, Xiao Han, Junyu Li, Xiaomao Ding, Errui Wang, Jingdong Shanghai Univ Shanghai Peoples R China Baidu Inc Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scores cannot properly represent the localization quality. (2) Assignment ambiguity that samples are matched with improper labels in pseudo-label assignment, as the strategy is misguided by missed objects and inaccurate pseudo boxes. To tackle these problems, we propose a Ambiguity-Resistant Semi-supervised Learning (ARSL) for one-stage detectors. Specifically, to alleviate the selection ambiguity, Joint-Confidence Estimation (JCE) is proposed to jointly quantifies the classification and localization quality of pseudo labels. As for the assignment ambiguity, Task-Separation Assignment (TSA) is introduced to assign labels based on pixel-level predictions rather than unreliable pseudo boxes. It employs a 'divide-and-conquer' strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity. Comprehensive experiments demonstrate that ARSL effectively mitigates the ambiguities and achieves state-of-the-art SSOD performance on MS COCO and PASCAL VOC. Codes can be found at https://***/PaddlePaddle/PaddleDetection.

关键词： detection recognition: Categorization retrieval

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Rethinking Optical Flow from Geometric Matching Consistent Perspective

Rethinking Optical Flow from Geometric Matching Consistent P...

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

作者： Dong, Qiaole Cao, Chenjie Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Zhejiang Normal Univ Fudan ISTBI ZJNU Algorithm Ctr Brain Inspired Int Jinhua Peoples R China

ISBN: (纸本)9798350301298

Optical flow estimation is a challenging problem remaining unsolved. Recent deep learning based optical flow models have achieved considerable success. However, these models often train networks from the scratch on standard optical flow data, which restricts their ability to robustly and geometrically match image features. In this paper, we propose a rethinking to previous optical flow estimation. We particularly leverage Geometric Image Matching (GIM) as a pre-training task for the optical flow estimation (MatchFlow) with better feature representations, as GIM shares some common challenges as optical flow estimation, and with massive labeled real-world data. Thus, matching static scenes helps to learn more fundamental feature correlations of objects and scenes with consistent displacements. Specifically, the proposed MatchFlow model employs a QuadTree attention-based network pre-trained on MegaDepth to extract coarse features for further flow regression. Extensive experiments show that our model has great cross-dataset generalization. Our method achieves 11.5% and 10.1% error reduction from GMA on Sintel clean pass and KITTI test set. At the time of anonymous submission, our MatchFlow(G) enjoys state-of-the-art performance on Sintel clean and final pass compared to published approaches with comparable computation and memory footprint. Codes and models will be released in https://***/DQiaole/MatchFlow.

关键词： Low-level vision

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PD-Quant: Post-Training Quantization Based on Prediction Difference Metric

PD-Quant: Post-Training Quantization Based on Prediction Dif...

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

作者： Liu, Jiawei Niu, Lin Yuan, Zhihang Yang, Dawei Wang, Xinggang Liu, Wenyu Huazhong Univ Sci & Technol Sch EIC Wuhan Peoples R China Houmo AI Nanjing Peoples R China

ISBN: (纸本)9798350301298

Post-training quantization (PTQ) is a neural network compression technique that converts a full-precision model into a quantized model using lower-precision data types. Although it can help reduce the size and computational cost of deep neural networks, it can also introduce quantization noise and reduce prediction accuracy, especially in extremely low-bit settings. How to determine the appropriate quantization parameters (e.g., scaling factors and rounding of weights) is the main problem facing now. Existing methods attempt to determine these parameters by minimize the distance between features before and after quantization, but such an approach only considers local information and may not result in the most optimal quantization parameters. We analyze this issue and propose PD-Quant, a method that addresses this limitation by considering global information. It determines the quantization parameters by using the information of differences between network prediction before and after quantization. In addition, PD-Quant can alleviate the overfitting problem in PTQ caused by the small number of calibration sets by adjusting the distribution of activations. Experiments show that PD-Quant leads to better quantization parameters and improves the prediction accuracy of quantized models, especially in low-bit settings. For example, PD-Quant pushes the accuracy of ResNet-18 up to 53.14% and RegNetX-600MF up to 40.67% in weight 2-bit activation 2-bit. The code is released at https://***/hustvl/PD-Quant.

关键词： Efficient and scalable vision

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CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution

CiaoSR: Continuous Implicit Attention-in-Attention Network f...

引用

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

作者： Cao, Jiezhang Wang, Qin Xian, Yongqin Li, Yawei Ni, Bingbing Pi, Zhiming Zhang, Kai Zhang, Yulun Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Huawei Inc Shenzhen Peoples R China Univ Wurzburg Wurzburg Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

Learning continuous image representations is recently gaining popularity for image super-resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary scales from low-resolution inputs. Existing methods mostly ensemble nearby features to predict the new pixel at any queried coordinate in the SR image. Such a local ensemble suffers from some limitations: i) it has no learnable parameters and it neglects the similarity of the visual features;ii) it has a limited receptive field and cannot ensemble relevant features in a large field which are important in an image. To address these issues, this paper proposes a continuous implicit attention-in-attention network, called CiaoSR. We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features. Furthermore, we embed a scale-aware attention in this implicit attention network to exploit additional non-local information. Extensive experiments on benchmark datasets demonstrate CiaoSR significantly outperforms the existing single image SR methods with the same backbone. In addition, CiaoSR also achieves the state-of-the-art performance on the arbitrary-scale SR task. The effectiveness of the method is also demonstrated on the real-world SR setting. More importantly, CiaoSR can be flexibly integrated into any backbone to improve the SR performance.

关键词： Low-level vision

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Equiangular Basis Vectors

Equiangular Basis Vectors

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

作者： Shen, Yang Sun, Xuhao Wei, Xiu-Shen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798350301298

We propose Equiangular Basis Vectors (EBVs) for classification tasks. In deep neural networks, models usually end with a k-way fully connected layer with softmax to handle different classification tasks. The learning objective of these methods can be summarized as mapping the learned feature representations to the samples' label space. While in metric learning approaches, the main objective is to learn a transformation function that maps training data points from the original space to a new space where similar points are closer while dissimilar points become farther apart. Different from previous methods, our EBVs generate normalized vector embeddings as "predefined classifiers" which are required to not only be with the equal status between each other, but also be as orthogonal as possible. By minimizing the spherical distance of the embedding of an input between its categorical EBV in training, the predictions can be obtained by identifying the categorical EBV with the smallest distance during inference. Various experiments on the ImageNet-1K dataset and other downstream tasks demonstrate that our method outperforms the general fully connected classifier while it does not introduce huge additional computation compared with classical metric learning methods. Our EBVs won the first place in the 2022 DIGIX Global AI Challenge, and our code is open-source and available at https://***/NJUST-VIPGroup/Equiangular-Basis-Vectors.

关键词： detection recognition: Categorization retrieval

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Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision

Mapping Degeneration Meets Label Evolution: Learning Infrare...

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

作者： Ying, Xinyi Liu, Li Wang, Yingqian Li, Ruojing Chen, Nuo Lin, Zaiping Sheng, Weidong Zhou, Shilin Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9798350301298

Training a convolutional neural network (CNN) to detect infrared small targets in a fully supervised manner has gained remarkable research interests in recent years, but is highly labor expensive since a large number of per-pixel annotations are required. To handle this problem, in this paper, we make the first attempt to achieve infrared small target detection with point-level supervision. Interestingly, during the training phase supervised by point labels, we discover that CNNs first learn to segment a cluster of pixels near the targets, and then gradually converge to predict groundtruth point labels. Motivated by this "mapping degeneration" phenomenon, we propose a label evolution framework named label evolution with single point supervision (LESPS) to progressively expand the point label by leveraging the intermediate predictions of CNNs. In this way, the network predictions can finally approximate the updated pseudo labels, and a pixel-level target mask can be obtained to train CNNs in an end-to-end manner. We conduct extensive experiments with insightful visualizations to validate the effectiveness of our method. Experimental results show that CNNs equipped with LESPS can well recover the target masks from corresponding point labels, and can achieve over 70% and 95% of their fully supervised performance in terms of pixel-level intersection over union (IoU) and object-level probability of detection (Pd), respectively. Code is available at https://***/XinyiYing/LESPS.

关键词： detection recognition: Categorization retrieval

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Learning Partial Correlation based Deep Visual Representation for Image Classification

Learning Partial Correlation based Deep Visual Representatio...

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

作者： Rahmanl, Saimunur Koniuszt, Piotr Wang, Lei Zhou, Luping Moghadam, Peyman Sun, Changming CSIRO Data61 Clayton Vic Australia Univ Wollongong Wollongong NSW Australia Australian Natl Univ Canberra ACT Australia Univ Sydney Sydney NSW Australia Queensland Univ Technol Kelvin Grove Qld Australia

ISBN: (纸本)9798350301298

Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the "confounding" effect. For this case, "partial correlation" which removes the confounding effect shall be estimated instead. Nevertheless, reliably estimating partial correlation requires to solve a symmetric positive definite matrix optimisation, known as sparse inverse covariance estimation (SICE). How to incorporate this process into CNN remains an open issue. In this work, we formulate SICE as a novel structured layer of CNN. To ensure end-to-end trainability, we develop an iterative method to solve the above matrix optimisation during forward and backward propagation steps. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN. Computationally, our model can be effectively trained with GPU and works well with a large number of channels of advanced CNNs. Experiments show the efficacy and superior classification performance of our deep visual representation compared to covariance matrix based counterparts.

关键词： detection recognition: Categorization retrieval

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PHA: Patch-wise High-frequency Augmentation for Transformer-based Person Re-identification

PHA: Patch-wise High-frequency Augmentation for Transformer-...

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

作者： Zhang, Guiwei Zhang, Yongfei Zhang, Tianyu Li, Bo Pu, Shiliang Beihang Univ Beijing Key Lab Digital Media Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Pengcheng Lab Shenzhen Peoples R China Hikvis Res Inst Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Although recent studies empirically show that injecting Convolutional Neural Networks (CNNs) into vision Transformers (ViTs) can improve the performance of person re-identification, the rationale behind it remains elusive. From a frequency perspective, we reveal that ViTs perform worse than CNNs in preserving key high-frequency components (e.g, clothes texture details) since high-frequency components are inevitably diluted by low-frequency ones due to the intrinsic Self-Attention within ViTs. To remedy such inadequacy of the ViT, we propose a Patch-wise High-frequency Augmentation (PHA) method with two core designs. First, to enhance the feature representation ability of high-frequency components, we split patches with high-frequency components by the Discrete Haar Wavelet Transform, then empower the ViT to take the split patches as auxiliary input. Second, to prevent high-frequency components from being diluted by low-frequency ones when taking the entire sequence as input during network optimization, we propose a novel patch-wise contrastive loss. From the view of gradient optimization, it acts as an implicit augmentation to improve the representation ability of key high-frequency components. This benefits the ViT to capture key high-frequency components to extract discriminative person representations. PHA is necessary during training and can be removed during inference, without bringing extra complexity. Extensive experiments on widely-used ReID datasets validate the effectiveness of our method.

关键词： detection recognition: Categorization retrieval

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