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3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks

3DRRDB: Super Resolution of Multiple Remote Sensing Images u...

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

作者： Ramzy Ibrahim, Mohamed Benavente, Robert Lumbreras, Felipe Ponsa, Daniel Arab Acad Sci & Technol Comp Engn Dept Alexandria Egypt Univ Autonoma Barcelona Dept Comp Sci Barcelona Spain Comp Vis Ctr Campus UAB Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively.

关键词： Training Solid modeling Three-dimensional displays PSNR Convolution Superresolution pattern recognition

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Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning

Hybrid Consistency Training with Prototype Adaptation for Fe...

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

作者： Ye, Meng Lin, Xiao Burachas, Giedrius Divakaran, Ajay Yao, Yi SRI Int 333 Ravenswood Ave Menlo Pk CA 94025 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes. Recent FSL works have made steady progress via metric learning, meta learning, representation learning, etc. However, FSL remains challenging due to the following longstanding difficulties. 1) The seen and unseen classes are disjoint, resulting in a distribution shift between training and testing. 2) During testing, labeled data of previously unseen classes is sparse, making it difficult to reliably extrapolate from labeled support examples to unlabeled query examples. To tackle the first challenge, we introduce Hybrid Consistency Training to jointly leverage two types of consistency: 1) interpolation consistency, which interpolates hidden features to imposes linear behavior locally, and 2) data augmentation consistency, which learns robust embeddings against sample variations. As for the second challenge, we use unlabeled examples to iteratively normalize features and adapt prototypes, as opposed to commonly used one-time update, for more reliable prototype-based transductive inference. We show that our method generates a 2% to 5% improvement over the state-of-the-art methods with similar backbones on five FSL datasets and, more notably, a 7% to 8% improvement for more challenging cross-domain FSL.

关键词： Training Representation learning Measurement Interpolation Prototypes Inference algorithms pattern recognition

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A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds

A Closer Look at Blind Super-Resolution: Degradation Models,...

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

作者： Zhang, Wenlong Shi, Guangyuan Liu, Yihao Dong, Chao Wu, Xiao-Ming HongKong Polytech Univ Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Degradation models play an important role in Blind super-resolution (SR). The classical degradation model, which mainly involves blur degradation, is too simple to simulate real-world scenarios. The recently proposed practical degradation model includes a full spectrum of degradation types, but only considers complex cases that use all degradation types in the degradation process, while ignoring many important corner cases that are common in the real world. To address this problem, we propose a unified gated degradation model to generate a broad set of degradation cases using a random gate controller. Based on the gated degradation model, we propose simple baseline networks that can effectively handle non-blind, classical, practical degradation cases as well as many other corner cases. To fairly evaluate the performance of our baseline networks against state-of-the-art methods and understand their limits, we introduce the performance upper bound of an SR network for every degradation type. Our empirical analysis shows that with the unified gated degradation model, the proposed baselines can achieve much better performance than existing methods in quantitative and qualitative results, which are close to the performance upper bounds.

关键词： Degradation Analytical models computer vision Upper bound conferences Superresolution Logic gates

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A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution

A Hybrid Network of CNN and Transformer for Lightweight Imag...

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

作者： Fang, Jinsheng Lin, Hanjiang Chen, Xinyu Zeng, Kun Minnan Normal Univ Zhangzhou Peoples R China Minjiang Univ Fuzhou Fujian Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recently, a number of CNN based methods have made great progress in single image super-resolution. However, these existing architectures commonly build massive number of network layers, bringing high computational complexity and heavy memory consumption, which is inappropriate to be applied on embedded terminals such as mobile platforms. In order to solve this problem, we propose a hybrid network of CNN and Transformer (HNCT) for lightweight image super-resolution. In general, HNCT consists of four parts, which are shallow feature extraction module, Hybrid Blocks of CNN and Transformer (HBCTs), dense feature fusion module and up-sampling module, respectively. By combining CNN and Transformer, HBCT extracts deep features beneficial for super-resolution reconstruction in consideration of both local and non-local priors, while being lightweight and flexible enough. Enhanced spatial attention is introduced in HBCT to further improve performance. Extensive experimental results show our HNCT is superior to the state-of-the-art methods in terms of super-resolution performance and model complexity. Moreover, we won the second best PSNR and the least activation operations in NTIRE 2022 Efficient SR Challenge. Code is available at https://***/lhjthp/HNCT.

关键词： computer vision Codes conferences Superresolution Memory management Transformers Feature extraction

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Trust Your IMU: Consequences of Ignoring the IMU Drift

Trust Your IMU: Consequences of Ignoring the IMU Drift

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

作者： Ornhag, Marcus Valtonen Persson, Patrik Wadenback, Marten Astrom, Kalle Heyden, Anders Lund Univ Ctr Math Sci Lund Sweden Linkoping Univ Dept Elect Engn Linkoping Sweden

ISBN: (纸本)9781665487399

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups. The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods.

关键词： computer vision Measurement units Codes conferences Inertial navigation Distortion Real-time systems

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Semi-Supervised Hyperspectral Object Detection Challenge Results - PBVS 2022

Semi-Supervised Hyperspectral Object Detection Challenge Res...

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

作者： Rangnekar, Aneesh Mulhollan, Zachary Vodacek, Anthony Hoffman, Matthew Sappa, Angel Blasch, Erik Yu, Jun Zhang, Liwen Du, Shenshen Chang, Hao Lu, Keda Zhang, Zhong Gao, Fang Yu, Ye Shuang, Feng Wang, Lei Ling, Qiang Shyam, Pranjay Yoon, Kuk-Jin Kim, Kyung-Soo Rochester Inst Technol Rochester NY 14623 USA ESPOL Polytech Univ Guayaquil Ecuador Comp Vision Ctr Campus UAB Barcelona Spain US Air Force Res Lab Rome NY USA

ISBN: (纸本)9781665487399

This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the computer vision and pattern recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 x 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. [GRAPHICS] .

关键词： Training computer vision conferences Training data Object detection Semisupervised learning Transformers

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Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution

Fast and Memory-Efficient Network Towards Efficient Image Su...

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

作者： Du, Zongcai Liu, Ding Liu, Jie Tang, Jie Wu, Gangshan Fu, Lean Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Runtime and memory consumption are two important aspects for efficient image super-resolution (EISR) models to be deployed on resource-constrained devices. Recent advances in EISR [16, 32] exploit distillation and aggregation strategies with plenty of channel split and concatenation operations to fully use limited hierarchical features. In contrast, sequential network operations avoid frequently accessing preceding states and extra nodes, and thus are beneficial to reducing the memory consumption and runtime overhead. Following this idea, we design our lightweight network backbone by mainly stacking multiple highly optimized convolution and activation layers and decreasing the usage of feature fusion. We propose a novel sequential attention branch, where every pixel is assigned an important factor according to local and global contexts, to enhance high-frequency details. In addition, we tailor the residual block for EISR and propose an enhanced residual block (ERB) to further accelerate the network inference. Finally, combining all the above techniques, we construct a fast and memory-efficient network (FMEN) and its small version FMEN-S, which runs 33% faster and reduces 74% memory consumption compared with the state-of-the-art EISR model: E-RFDN, the champion in [49]. Besides, FMEN-S achieves the lowest memory consumption and the second shortest runtime in NTIRE 2022 challenge on efficient super-resolution [28]. Code is available at https://***/NJU-Jet/FMEN.

关键词： computer vision Runtime Codes Convolution conferences Memory management Superresolution

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Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators

Real-time Hyper-Dimensional Reconfiguration at the Edge usin...

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

作者： Kandaswamy, Indhumathi Farkya, Saurabh Daniels, Zachary van der Wal, Gooitzen Raghavan, Aswin Zhang, Yuzheng Hu, Jun Lomnitz, Michael Isnardi, Michael Zhang, David Piacentino, Michael SRI Int Ctr Vis Technol Princeton NJ 08540 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point Multiply-ACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators. We describe the algorithm, trained quantized model generation, and simulated performance of a feature extractor free of multiply-accumulates feeding a hyperdimensional logic-based classifier. Then we show how performance increases with the number of hyperdimensions. We describe the realized low-SWaP FPGA hardware and embedded software system compared to traditional DNNs and detail the implemented hardware accelerators. We discuss the measured system latency and power, noise robustness due to use of learnable quantization and HD computing, actual versus simulated system performance for a video activity classification task and demonstration of reconfiguration on this same dataset. We show that reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without gradient descent backpropagation (gradient-free), using few-shot learning of new classes at the edge. Initial work performed used LRCN DNN and is currently extended to use Two-stream DNN with improved performance.

关键词： Quantization (signal) Power measurement System performance Software algorithms Feature extraction Real-time systems pattern recognition

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Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge Distillation

Auxiliary Learning for Self-Supervised Video Representation ...

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

作者： Dadashzadeh, Amirhossein Whone, Alan Mirmehdi, Majid Univ Bristol Bristol Avon England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Despite the outstanding success of self-supervised pretraining methods for video representation learning, they generalise poorly when the unlabeled dataset for pretraining is small or the domain difference between unlabelled data in source task (pretraining) and labeled data in target task (finetuning) is significant. To mitigate these issues, we propose a novel approach to complement self-supervised pretraining via an auxiliary pretraining phase, based on knowledge similarity distillation, auxSKD, for better generalisation with a significantly smaller amount of video data, e.g. Kinetics-100 rather than Kinetics-400. Our method deploys a teacher network that iteratively distils its knowledge to the student model by capturing the similarity information between segments of unlabelled video data. The student model meanwhile solves a pretext task by exploiting this prior knowledge. We also introduce a novel pretext task, Video Segment Pace Prediction or VSPP, which requires our model to predict the playback speed of a randomly selected segment of the input video to provide more reliable self-supervised representations. Our experimental results show superior results to the state of the art on both UCF101 and HMDB51 datasets when pretraining on K100 in apple-to-apple comparisons. Additionally, we show that our auxiliary pretraining, auxSKD, when added as an extra pretraining phase to recent state of the art self-supervised methods (i.e. VCOP, VideoPace, and RSPNet), improves their results on UCF101 and HMDB51.

关键词： Representation learning Knowledge engineering Training Predictive models Data models pattern recognition Reliability

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Strengthening the Transferability of Adversarial Examples Using Advanced Looking Ahead and Self-CutMix

Strengthening the Transferability of Adversarial Examples Us...

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

作者： Jang, Donggon Son, Sanghyeok Kim, Dae-Shik Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep neural networks (DNNs) are vulnerable to adversarial examples generated by adding malicious noise imperceptible to a human. The adversarial examples successfully fool the models under the white-box setting, but the performance of attacks under the black-box setting degrades significantly, which is known as the low transferability problem. Various methods have been proposed to improve transferability, yet they are not effective against adversarial training and defense models. In this paper, we introduce two new methods termed Lookahead Iterative Fast Gradient Sign Method (LI-FGSM) and Self-CutMix (SCM) to address the above issues. LI-FGSM updates adversarial perturbations with the accumulated gradient obtained by looking ahead. A previous gradient-based attack is used for looking ahead during N steps to explore the optimal direction at each iteration. It allows the optimization process to escape the sub-optimal region and stabilize the update directions. SCM leverages the modified CutMix, which copies a patch from the original image and pastes it back at random positions of the same image, to preserve the internal information. SCM makes it possible to generate more transferable adversarial examples while alleviating the overfitting to the surrogate model employed. Our two methods are easily incorporated with the previous iterative gradient-based attacks. Extensive experiments on ImageNet show that our approach acquires state-of-the-art attack success rates not only against normally trained models but also against adversarial training and defense models.

关键词： Training Deep learning computer vision Computational modeling Perturbation methods conferences Neural networks

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