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Blind Image Inpainting via Omni-dimensional Gated Attention and Wavelet Queries

Blind Image Inpainting via Omni-dimensional Gated Attention ...

引用

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

作者： Phutke, Shruti S. Kulkarni, Ashutosh Vipparthi, Santosh Kumar Murala, Subrahmanyam Indian Institute of Technology Ropar Computer Vision and Pattern Recognition Lab Punjab Rupnagar India

ISBN: (纸本)9798350302493

Blind image inpainting is a crucial restoration task that does not demand additional mask information to restore the corrupted regions. Yet, it is a very less explored research area due to the difficulty in discriminating between corrupted and valid regions. There exist very few approaches for blind image inpainting which sometimes fail at producing plausible inpainted images. Since they follow a common practice of predicting the corrupted regions and then inpaint them. To skip the corrupted region prediction step and obtain better results, in this work, we propose a novel end-to-end architecture for blind image inpainting consisting of wavelet query multi-head attention transformer block and the omni-dimensional gated attention. The proposed wavelet query multi-head attention in the transformer block provides encoder features via processed wavelet coefficients as query to the multi-head attention. Further, the proposed omni-dimensional gated attention effectively provides all dimensional attentive features from the encoder to the respective decoder. Our proposed approach is compared numerically and visually with existing state-of-the-art methods for blind image inpainting on different standard datasets. The comparative and ablation studies prove the effectiveness of the proposed approach for blind image inpainting. The testing code is available at : https://***/shrutiphutke/Blind-Omni-Wav-Net © 2023 ieee.

关键词： Restoration

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Event Transformer. A sparse-aware solution for efficient event data processing

Event Transformer. A sparse-aware solution for efficient eve...

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

作者： Sabater, Alberto Montesano, Luis Murillo, Ana C. Univ Zaragoza DIIS I3A Zaragoza Spain Bitbrain Technol Zaragoza Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Event cameras are sensors of great interest for many applications that run in low-resource and challenging environments. They log sparse illumination changes with high temporal resolution and high dynamic range, while they present minimal power consumption. However, top-performing methods often ignore specific event-data properties, leading to the development of generic but computationally expensive algorithms. Efforts toward efficient solutions usually do not achieve top-accuracy results for complex tasks. This work proposes a novel framework, Event Transformer (EvT)(1), that effectively takes advantage of event-data properties to be highly efficient and accurate. We introduce a new patch-based event representation and a compact transformer-like architecture to process it. EvT is evaluated on different event-based benchmarks for action and gesture recognition. Evaluation results show better or comparable accuracy to the state-of-the-art while requiring significantly less computation resources, which makes EvT able to work with minimal latency both on GPU and CPU.

关键词： Power demand Graphics processing units Gesture recognition computer architecture Benchmark testing Transformers Data processing

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Towards Exemplar-Free Continual Learning in vision Transformers: an Account of Attention, Functional and Weight Regularization

Towards Exemplar-Free Continual Learning in Vision Transform...

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

作者： Pelosin, Francesco Jha, Saurav Torsello, Andrea Raducanu, Bogdan van de Weijer, Joost Ca Foscari Univ Venice Italy Univ New South Wales Sydney NSW Australia Comp Vis Ctr Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we investigate the continual learning of vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) - while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners.(1)

关键词： Learning systems Weight measurement Image recognition Surgery Benchmark testing Transformers Stability analysis

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MARRS: Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation

MARRS: Modern Backbones Assisted Co-training for Rapid and R...

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

作者： Jain, Saurabh Kumar Das, Sukhendu Indian Institute of Technology Visualization and Perception Lab Department of Computer Science Engineering Madras India

ISBN: (纸本)9798350302493

Semi-Supervised Domain Adaptation (SSDA) aims to develop domain invariant models from scarcely labeled target domain in addition to the fully labeled source domain. Current SSDA works are often applied in conjunction with ResNet34 backbone, which makes them overlook the advantages of utilizing other backbones. Hence, in this paper, we investigate the impact of employing different modern backbones in SSDA and propose a novel solution named Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation (MARRS), that uses discriminative features of two modern backbones for training linear classifiers using the well established co-training framework. To induce diversity among classifiers for effective co-training, we propose a novel module that produces diversity at three levels, namely image, backbone, and feature distribution levels. Experiments reveal that MARRS not only achieves state-of-the-art performance across all popular SSDA datasets, but also drastically cuts the computation time compared to other SSDA approaches, making MARRS a rapid and robust solution for SSDA. We also provide extensive ablation experiments to verify our framework's vitality and primary design choices. © 2023 ieee.

关键词： computer vision

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HSI-Guided Intrinsic Image Decomposition for Outdoor Scenes

HSI-Guided Intrinsic Image Decomposition for Outdoor Scenes

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

作者： Zhang, Fan You, Shaodi Li, Yu Fu, Ying Beijing Inst Technol Beijing Peoples R China Univ Amsterdam Amsterdam Netherlands Int Digital Econ Acad Shenzhen Peoples R China Jiangsu Univ Sci & Technol Zhenjiang Jiangsu Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Intrinisic image decomposition (IID) aims to recover the reflectance and shading components from images and is the prerequisite to many downstream computer vision applications, such as image editing and image relighting. Due to the inherent difficulty in acquiring ground truth reflectance and shading, existing datasets are either synthetic indoor scenes or objects using graphics rendering (e.g., CGIntrinsics and ShapeNet etc.) or real photos with very sparse manual annotation (e.g., IIW and SAW etc.). Accompanied with the complex nature of outdoor scenes, most IID methods focus on the decomposition of indoor environment. There is still a long way to go before we can handle IID of outdoor scenes. In this paper, we take the attempt to perform intrinsic image decomposition for outdoor scenes when RGB image is not the only thing we can get from the enviroment. With the observation of prior work where nir-infrared (NIR) images are transparent to a range of colourants/dyes, we propose to extend it to more spectra by collecting hyperspectral imaging (HSI) data which are well aligned with RGB images and to perform IID with both of them. We also apply existing mainstream IID methods for comparison to examine current progress and challenges at the road towards IID outdoors. We still make some improvements and find problems when performing IID for outdoor scenes, even though we do not handle it perfectly. The data we collect will be made publicly available for further potential investigation.

关键词： Reflectivity computer vision Roads Surface acoustic waves Manuals Rendering (computer graphics) Image decomposition

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Transformer for Single Image Super-Resolution

Transformer for Single Image Super-Resolution

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

作者： Lu, Zhisheng Li, Juncheng Liu, Hong Huang, Chaoyan Zhang, Linlin Zeng, Tieyong Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Single image super-resolution (SISR) has witnessed great strides with the development of deep learning. However, most existing studies focus on building more complex networks with a massive number of layers. Recently, more and more researchers start to explore the application of Transformer in computer vision tasks. However, the heavy computational cost and high GPU memory occupation of the vision Transformer cannot be ignored. In this paper, we propose a novel Efficient Super-Resolution Transformer (ESRT) for SISR. ESRT is a hybrid model, which consists of a Lightweight CNN Backbone (LCB) and a Lightweight Transformer Backbone (LTB). Among them, LCB can dynamically adjust the size of the feature map to extract deep features with a low computational costs. LTB is composed of a series of Efficient Transformers (ET), which occupies a small GPU memory occupation, thanks to the specially designed Efficient Multi-Head Attention (EMHA). Extensive experiments show that ESRT achieves competitive results with low computational cost. Compared with the original Transformer which occupies 16,057M GPU memory, ESRT only occupies 4,191M GPU memory. All codes are available at https://***/luissen/ESRT.

关键词： computer vision Adaptation models Computational modeling Superresolution Memory management Graphics processing units Transformers

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TikTok for good: Creating a diverse emotion expression database

TikTok for good: Creating a diverse emotion expression datab...

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

作者： Surabhi, Saimourya Shah, Bhavik Washington, Peter Mutlu, Onur Cezmi Leblanc, Emilie Mohite, Prathamesh Husic, Arman Kline, Aaron Dunlap, Kaitlyn McNealis, Maya Liu, Bennett Deveaux, Nick Sleiman, Essam Wall, Dennis P. Stanford Univ Dept Biomed Data Sci Dept Pediat Syst Med Stanford CA 94305 USA Stanford Univ Dept Psychiat & Behav Sci Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition (FER) is a critical computer vision task for a variety of applications. Despite the widespread use of FER, there is a dearth of racially diverse facial emotion datasets which are enriched for children, teens, and adults. To bridge this gap, we have built a diverse expression recognition database using publicly available videos from TikTok, a video-focused social networking service. We describe the construction of the TikTok Facial expression recognition (FER) database. The dataset is extracted from 6428 videos scraped from TikTok. The videos consist of 9392 distinct individuals and labels for 15 emotion-related prompts. We were able to achieve a F1 score 0.78 for Ekman emotions on expression classification using transfer learning. We hope that the scale and diversity of the TikTokFER dataset will be of use to affective computing practitioners.

关键词： Bridges computer vision Emotion recognition Databases Social networking (online) Face recognition conferences

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EFE: End-to-end Frame-to-Gaze Estimation

EFE: End-to-end Frame-to-Gaze Estimation

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

作者： Balim, Haldun Park, Seonwook Wang, Xi Zhang, Xucong Hilliges, Otmar ETH Zürich Department of Computer Science Switzerland Lunit Inc. Delft University of Technology Computer Vision Lab Netherlands

ISBN: (纸本)9798350302493

Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher resolution in the eye regions and having fewer confounding factors (such as clothing and hair) is believed to benefit the final model performance. However, this eye/face patch cropping process is expensive, erroneous, and implementation-specific for different methods. In this paper, we propose a frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze direction from the raw frame out of the camera without any face or eye cropping. Our method demonstrates that direct gaze regression from the raw downscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite the challenges of having very few pixels in the eye region. The proposed method achieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG) estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE, and generalizes well to extreme camera view changes. © 2023 ieee.

关键词： Cameras

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CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

CFA: Constraint-based Finetuning Approach for Generalized Fe...

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

作者： Guirguis, Karim Hendawy, Ahmed Eskandar, George Abdelsamad, Mohamed Kayser, Matthias Beyerer, Juergen Robert Bosch GmbH Stuttgart Germany Univ Stuttgart Stuttgart Germany Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany

ISBN: (纸本)9781665487399

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.

关键词： Learning systems Adaptation models computer vision conferences Object detection Search problems pattern recognition

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Contrastive Learning-based Robust Object Detection under Smoky Conditions

Contrastive Learning-based Robust Object Detection under Smo...

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

作者： Wu, Wei Chang, Hao Zheng, Yonghua Li, Zhu Chen, Zhiwen Zhang, Ziheng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Object detection is to effectively find out interested targets in images and then accurately determine their categories and positions. Recently many excellent methods have been developed to provide powerful detection capability. However, their performance may degrade significantly under severe weather such as smoky conditions. In this paper, we propose a contrastive learning-based robust object detection algorithm for smoke images. The proposed object detector consists of two modules: contrastive learning module and object bounding box prediction module. The first module learns representation vectors by maximizing agreement between different augmented views of the same smoke image. These representations are then sent to the second module to yield the bounding box for each object. In addition, we also propose a novel affine data augmentation method. Extensive experiments have been conducted on A2I2-Haze dataset which is the first real haze dataset with in-situ smoke measurement aligned to aerial and ground imagery. This dataset is also the only dataset used in the 5th UG(2)+ challenges of CVPR 2022 for both training and testing. Compared with state-of-the-art methods, evaluation results show the superiority of our proposed object detector.

关键词： Training computer vision conferences Object detection Detectors Transforms Prediction algorithms

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