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Visual recognition-Driven Image Restoration for Multiple Degradation with Intrinsic Semantics Recovery

Visual Recognition-Driven Image Restoration for Multiple Deg...

引用

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

作者： Yang, Zizheng Huang, Jie Chang, Jiahao Zhou, Man Yu, Hu Zhang, Jinghao Zhao, Feng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images. Although many studies have investigated to solve the issue through image restoration or domain adaptation, the former focuses on visual quality rather than recognition quality, while the latter requires semantic annotations for task-specific training. In this paper, to address more practical scenarios, we propose a Visual recognition-Driven Image Restoration network for multiple degradation, dubbed VRD-IR, to recover high-quality images from various unknown corruption types from the perspective of visual recognition within one model. Concretely, we harmonize the semantic representations of diverse degraded images into a unified space in a dynamic manner, and then optimize them towards intrinsic semantics recovery. Moreover, a prior-ascribing optimization strategy is introduced to encourage VRD-IR to couple with various downstream recognition tasks better. Our VRD-IR is corruption- and recognition-agnostic, and can be inserted into various recognition tasks directly as an image enhancement module. Extensive experiments on multiple image distortions demonstrate that our VRD-IR surpasses existing image restoration methods and show superior performance on diverse high-level tasks, including classification, detection, and person re-identification.

关键词： Low-level vision

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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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ViPLO: vision Transformer based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection

ViPLO: Vision Transformer based Pose-Conditioned Self-Loop G...

引用

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

作者： Park, Jeeseung Park, Jin-Woo Lee, Jong-Seok MAy I Inc Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350301298

Human-Object Interaction (HOI) detection, which localizes and infers relationships between human and objects, plays an important role in scene understanding. Although two-stage HOI detectors have advantages of high efficiency in training and inference, they suffer from lower performance than one-stage methods due to the old backbone networks and the lack of considerations for the HOI perception process of humans in the interaction classifiers. In this paper, we propose vision Transformer based Pose-Conditioned Self-Loop Graph (ViPLO) to resolve these problems. First, we propose a novel feature extraction method suitable for the vision Transformer backbone, called masking with overlapped area (MOA) module. The MOA module utilizes the overlapped area between each patch and the given region in the attention function, which addresses the quantization problem when using the vision Transformer backbone. In addition, we design a graph with a pose-conditioned self-loop structure, which updates the human node encoding with local features of human joints. This allows the classifier to focus on specific human joints to effectively identify the type of interaction, which is motivated by the human perception process for HOI. As a result, ViPLO achieves the state-of-the-art results on two public benchmarks, especially obtaining a +2.07 mAP performance gain on the HICO-DET dataset.

关键词： detection recognition: Categorization retrieval

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Sparse Multimodal vision Transformer for Weakly Supervised Semantic Segmentation

Sparse Multimodal Vision Transformer for Weakly Supervised S...

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

作者： Hanna, Joëlle Mommert, Michael Borth, Damian University of St.Gallen AIML Lab School of Computer Science Switzerland

ISBN: (纸本)9798350302493

vision Transformers have proven their versatility and utility for complex computer vision tasks, such as land cover segmentation in remote sensing applications. While performing on par or even outperforming other methods like Convolutional Neural Networks (CNNs), Transformers tend to require even larger datasets with fine-grained annotations (e.g., pixel-level labels for land cover segmentation). To overcome this limitation, we propose a weakly-supervised vision Transformer that leverages image-level labels to learn a semantic segmentation task to reduce the human annotation load. We achieve this by slightly modifying the architecture of the vision Transformer through the use of gating units in each attention head to enforce sparsity during training and thereby retaining only the most meaningful heads. This allows us to directly infer pixel-level labels from image-level labels by post-processing the un-pruned attention heads of the model and refining our predictions by iteratively training a segmentation model with high fidelity. Training and evaluation on the DFC2020 dataset show that our method1 not only generates high-quality segmentation masks using image-level labels, but also performs on par with fully-supervised training relying on pixel-level labels. Finally, our results show that our method is able to perform weakly-supervised semantic segmentation even on small-scale datasets. © 2023 ieee.

关键词： Semantic Segmentation

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Fast building segmentation from satellite imagery and few local labels

Fast building segmentation from satellite imagery and few lo...

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

作者： Robinson, Caleb Ortiz, Anthony Park, Hogeun Gracia, Nancy Lozano Kaw, Jon Kher Sederholm, Tina Dodhia, Rahul Ferres, Juan M. Lavista Microsoft AI Good Res Lab Redmond WA 98052 USA World Bank 1818 H St NW Washington DC 20433 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level. However, domain shift problems are a common occurrence when trying to replicate models that drive these analyses to new areas, particularly in the developing world. If a model is trained with imagery and labels from one location, then it usually will not generalize well to new locations where the content of the imagery and data distributions are different. In this work, we consider the setting in which we have a single large satellite imagery scene over which we want to solve an applied problem - building footprint segmentation. Here, we do not necessarily need to worry about creating a model that generalizes past the borders of our scene but can instead train a local model. We show that surprisingly few labels are needed to solve the building segmentation problem with very high-resolution (0.5m/px) satellite imagery with this setting in mind. Our best model trained with just 527 sparse polygon annotations (an equivalent of 1500x1500 densely labeled pixels) has a recall of 0.87 over held out footprints and a R2 of 0.93 on the task of counting the number of buildings in 200x200 meter windows. We apply our models over high-resolution imagery in Amman, Jordan in a case study on urban change detection. [GRAPHICS] .

关键词： Training Image segmentation computer vision Technological innovation Satellites Computational modeling Buildings

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Blueprint Separable Residual Network for Efficient Image Super-Resolution

Blueprint Separable Residual Network for Efficient Image Sup...

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

作者： Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Gu, Jinjin Qiao, Yu Dong, Chao Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Beijing Peoples R China Univ Macau Taipa Macao Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent advances in single image super-resolution (SISR) have achieved extraordinary performance, but the computational cost is too heavy to apply in edge devices. To alleviate this problem, many novel and effective solutions have been proposed. Convolutional neural network (CNN) with the attention mechanism has attracted increasing attention due to its efficiency and effectiveness. However, there is still redundancy in the convolution operation. In this paper, we propose Blueprint Separable Residual Network (BSRN) containing two efficient designs. One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation. The other is to enhance the model ability by introducing more effective attention modules. The experimental results show that BSRN achieves state-of-the-art performance among existing efficient SR methods. Moreover, a smaller variant of our model BSRN-S won the first place in model complexity track of NTIRE 2022 Efficient SR Challenge. The code is available at https://***/xiaom233/BSRN.

关键词： Performance evaluation Convolution Computational modeling Superresolution Redundancy Complexity theory pattern recognition

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IMDeception: Grouped Information Distilling Super-Resolution Network

IMDeception: Grouped Information Distilling Super-Resolution...

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

作者： Ayazoglu, Mustafa Aselsan Res Ankara Turkey

ISBN: (纸本)9781665487399

Single-Image-Super-Resolution (SISR) is a classical computer vision problem that has benefited from the recent advancements in deep learning methods, especially the advancements of convolutional neural networks (CNN). Although state-of-the-art methods improve the performance of SISR on several datasets, direct application of these networks for practical use is still an issue due to heavy computational load. For this purpose, recently, researchers have focused on more efficient and high-performing network structures. Information multi-distilling network (IMDN) is one of the highly efficient SISR networks with high performance and low computational load. IMDN achieves this efficiency with various mechanisms such as Intermediate Information Collection (IIC), working in a global setting, Progressive Refinement Module (PRM), and Contrast Aware Channel Attention (CCA), employed in a local setting. These mechanisms, however, do not equally contribute to the efficiency and performance of IMDN. In this work, we propose the Global Progressive Refinement Module (GPRM) as a less parameter-demanding alternative to the IIC module for feature aggregation. To further decrease the number of parameters and floating point operations per second (FLOPS), we also propose Grouped Information Distilling Blocks (GIDB). Using the proposed structures, we design an efficient SISR network called IMDeception. Experiments reveal that the proposed network performs on par with state-of-the-art models despite having a limited number of parameters and FLOPS. Furthermore, using grouped convolutions as a building block of GIDB increases room for further optimization during deployment. To show its potential, the proposed model was deployed on NVIDIA Jetson Xavier AGX and it has been shown that it can run in real-time on this edge device.

关键词： Deep learning computer vision conferences Superresolution Real-time systems Hardware Timing

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Dynamic Inference with Grounding Based vision and Language Models

Dynamic Inference with Grounding Based Vision and Language M...

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

作者： Uzkent, Burak Garg, Amanmeet Zhu, Wentao Doshi, Keval Yi, Jingru Wang, Xiaolong Omar, Mohamed Amazon Prime Video Seattle WA 30439 USA

ISBN: (纸本)9798350301298

Transformers have been recently utilized for vision and language tasks successfully. For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks. On the other hand, there exists a large amount of computational redundancy in these large models which skips their run-time efficiency. To address this problem, we propose dynamic inference for grounding based vision and language models conditioned on the input imagetext pair. We first design an approach to dynamically skip multihead self-attention and feed forward network layers across two backbones and multimodal network. Additionally, we propose dynamic token pruning and fusion for two backbones. In particular, we remove redundant tokens at different levels of the backbones and fuse the image tokens with the language tokens in an adaptive manner. To learn policies for dynamic inference, we train agents using reinforcement learning. In this direction, we replace the CNN backbone in a recent grounding-based vision and language model, MDETR, with a vision transformer and call it ViTMDETR. Then, we apply our dynamic inference method to ViTMDETR, called D-ViTDMETR, and perform experiments on image-language tasks. Our results show that we can improve the run-time efficiency of the state-of-the-art models MDETR and GLIP by up to similar to 50% on Referring Expression Comprehension and Segmentation, and VQA with only maximum similar to 0.3% accuracy drop.

关键词： Efficient and scalable vision

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Joint Appearance and Motion Learning for Efficient Rolling Shutter Correction

Joint Appearance and Motion Learning for Efficient Rolling S...

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

作者： Fan, Bin Mao, Yuxin Dai, Yuchao Wan, Zhexiong Liu, Qi Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9798350301298

Rolling shutter correction (RSC) is becoming increasingly popular for RS cameras that are widely used in commercial and industrial applications. Despite the promising performance, existing RSC methods typically employ a two-stage network structure that ignores intrinsic information interactions and hinders fast inference. In this paper, we propose a single-stage encoder-decoder-based network, named JAMNet, for efficient RSC. It first extracts pyramid features from consecutive RS inputs, and then simultaneously refines the two complementary information (i.e., global shutter appearance and undistortion motion field) to achieve mutual promotion in a joint learning decoder. To inject sufficient motion cues for guiding joint learning, we introduce a transformer-based motion embedding module and propose to pass hidden states across pyramid levels. Moreover, we present a new data augmentation strategy "vertical flip + inverse order" to release the potential of the RSC datasets. Experiments on various benchmarks show that our approach surpasses the state-of-the-art methods by a large margin, especially with a 4.7 dB PSNR leap on real-world RSC. Code is available at https://***/GitCVfb/JAMNet.

关键词： Low-level vision

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Zero-shot Model Diagnosis

Zero-shot Model Diagnosis

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

作者： Luo, Jinqi Wang, Zhaoning Wu, Chen Henry Huang, Dong De la Torre, Fernando Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350301298

When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expensive, and prone to mistakes. The question we try to address is: can we evaluate the sensitivity of deep learning models to arbitrary visual attributes without an annotated test set? This paper argues the case that Zero-shot Model Diagnosis (ZOOM) is possible without the need for a test set nor labeling. To avoid the need for test sets, our system relies on a generative model and CLIP. The key idea is enabling the user to select a set of prompts (relevant to the problem) and our system will automatically search for semantic counterfactual images (i.e., synthesized images that flip the prediction in the case of a binary classifier) using the generative model. We evaluate several visual tasks (classification, key-point detection, and segmentation) in multiple visual domains to demonstrate the viability of our methodology. Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.

关键词： Explainable computer vision

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