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Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior

Nighttime Smartphone Reflective Flare Removal Using Optical ...

引用

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

作者： Dai, Yuekun Luo, Yihang Zhou, Shangchen Li, Chongyi Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos, which can impact their quality. Eliminating reflective flare is highly desirable but challenging. Many existing methods rely on manually designed features to detect these bright spots, but they often fail to identify reflective flares created by various types of light and may even mistakenly remove the light sources in scenarios with multiple light sources. To address these challenges, we propose an optical center symmetry prior, which suggests that the reflective flare and light source are always symmetrical around the lens's optical center. This prior helps to locate the reflective flare's proposal region more accurately and can be applied to most smartphone cameras. Building on this prior, we create the first reflective flare removal dataset called BracketFlare, which contains diverse and realistic reflective flare patterns. We use continuous bracketing to capture the reflective flare pattern in the underexposed image and combine it with a normally exposed image to synthesize a pair of flare-corrupted and flare-free images. With the dataset, neural networks can be trained to remove the reflective flares effectively. Extensive experiments demonstrate the effectiveness of our method on both synthetic and real-world datasets.

关键词： Low-level vision

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You Are Catching My Attention: Are vision Transformers Bad Learners under Backdoor Attacks?

You Are Catching My Attention: Are Vision Transformers Bad L...

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

作者： Yuan, Zenghui Zhou, Pan Zou, Kai Cheng, Yu Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China Protagolabs Inc Vienna Austria Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the process of industrializing ViTs, backdoor attacks have brought severe challenges to security. The success of ViTs benefits from the self-attention mechanism. However, compared with CNNs, we find that this mechanism of capturing global information within patches makes ViTs more sensitive to patch-wise triggers. Under such observations, we delicately design a novel backdoor attack framework for ViTs, dubbed BadViT, which utilizes a universal patch-wise trigger to catch the model's attention from patches beneficial for classification to those with triggers, thereby manipulating the mechanism on which ViTs survive to confuse itself. Furthermore, we propose invisible variants of BadViT to increase the stealth of the attack by limiting the strength of the trigger perturbation. Through a large number of experiments, it is proved that BadViT is an efficient backdoor attack method against ViTs, which is less dependent on the number of poisons, with satisfactory convergence, and is transferable for downstream tasks. Furthermore, the risks inside of ViTs to backdoor attacks are also explored from the perspective of existing advanced defense schemes.

关键词： Machine learning (other than deep learning)

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Rapid 3D Model Generation with Intuitive 3D Input

Rapid 3D Model Generation with Intuitive 3D Input

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

作者： Chen, Tianrun Ding, Chaotao Zhang, Shangzhan Yu, Chunan Zang, Ying Li, Zejian Peng, Sida Sun, Lingyun Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Huzhou Univ Sch Informat Engn Huzhou Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Moxin Huzhou Technol Co Ltd KOKONI3D Huzhou Peoples R China

ISBN: (纸本)9798350353006

With the emergence of AR/VR, 3D models are in tremendous demand. However, conventional 3D modeling with computer-Aided Design software requires much expertise and is difficult for novice users. We find that AR/VR devices, in addition to serving as effective display mediums, can offer a promising potential as an intuitive 3D model creation tool, especially with the assistance of AI generative models. Here, we propose Deep3DVRSketch, the first 3D model generation network that inputs 3D VR sketches from novice users and generates highly consistent 3D models in multiple categories within seconds, irrespective of the users' drawing abilities. We also contribute KO3D+, the largest 3D sketch-shape dataset. Our method pre-trains a conditional diffusion model on quality 3D data, then fine-tunes an encoder to map 3D sketches onto the generator's manifold using an adaptive curriculum strategy for limited ground truths. In our experiment, our approach achieves state-of- the-art performance in both model quality and fidelity with real-world input from novice users, and users can even draw and obtain very detailed geometric structures. In our user study, users were able to complete the 3D modeling tasks over 10 times faster using our approach compared to conventional CAD software tools. We believe that our Deep3DVRSketch and KO3D+ dataset can offer a promising solution for future 3D modeling in metaverse era. Check the project page at http://***/Deep3DVRSketch.

关键词： 3D models 3D vision generative models

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Exploring Joint Embedding Architectures and Data Augmentations for Self-Supervised Representation Learning in Event-Based vision

Exploring Joint Embedding Architectures and Data Augmentatio...

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

作者： Barchid, Sami Mennesson, José Djéraba, Chaabane Univ. Lille Cnrs Centrale Lille Umr 9189 CRIStAL France Univ. Lille Centre for Digital Systems Imt Nord Europe Institut Mines-Télécom LilleF-59000 France

ISBN: (纸本)9798350302493

This paper proposes a self-supervised representation learning (SSRL) framework for event-based vision, which leverages various lightweight convolutional neural networks (CNNs) including 2D-, 3D-, and Spiking CNNs. The method uses a joint embedding architecture to maximize the agreement between features extracted from different views of the same event sequence. Popular event data augmentation techniques are employed to design an efficient augmentation policy for event-based SSRL, and we provide novel data augmentation methods to enhance the pretraining pipeline. Given the novelty of SSRL for event-based vision, we elaborate standard evaluation protocols and use them to evaluate our approach. Our study demonstrates that pretrained CNNs acquire effective and transferable features, enabling them to achieve competitive performance in object or action recognition across various commonly used event-based datasets, even in a low-data regime. This paper also conducts an experimental analysis of the extracted features regarding the Uniformity-Tolerance tradeoff to assess their quality, and measure the similarity of representations using linear Center Kernel Alignement. These quantitative measurements reinforce our observations from the performance benchmarks and show substantial differences between the learned representations of all types of CNNs despite being optimized with the same approach. © 2023 ieee.

关键词： Embeddings

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Is Meta-Learning Always Necessary?: A Practical ML Framework Solving Novel Tasks at Large-scale Car Sharing Platform

Is Meta-Learning Always Necessary?: A Practical ML Framework...

引用

23rd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Chung, Hyunhee Park, Kyung Ho SOCAR AI Res Seoul South Korea

ISBN: (纸本)9798350320565

While the deep neural networks achieved superior performance in various tasks under the supervised regime, the ML practitioners in the real world frequently encounter a novel task that cannot acquire the labeled dataset shortly. Even if they have become available in acquiring the target samples from the unlabeled dataset, conventional labeling procedures require the practitioners to invest in resource consumption. Pursuing an effective solution to these problems, our study proposes a practical ML framework that efficiently enables practitioners to solve novel tasks. Our ML framework consists of two solutions consisting of early and mature stages. First, the early stage solution lets the practitioners solve the novel task under the few-shot classification setting. Second, the mature stage solution enhances the labeling efficiency by retrieving samples that seem relevant to the target. Upon these solutions, acquiring a qualified representation power is the most important job. Under the public benchmark datasets and image recognition tasks in a large-scale car-sharing platform, we examined that the paradigm of supervised learning, surprisingly not meta-learning, produces the most beneficial representation power to solve novel tasks. We further scrutinized the supremacy of supervised representation derives from broader, nourished high-level representations in the neural networks. We highly expect our analyses can be a concrete benchmark to the ML practitioners who solve novel tasks in their domain.

关键词： Deep learning computer vision Image recognition conferences Neural networks Supervised learning Benchmark testing

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OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval

OMG: Observe Multiple Granularities for Natural Language-Bas...

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

作者： Du, Yunhao Zhang, Binyu Ruan, Xiangning Su, Fei Zhao, Zhicheng Chen, Hong Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Retrieving tracked-vehicles by natural language descriptions plays a critical role in smart city construction. It aims to find the best match for the given texts from a set of tracked vehicles in surveillance videos. Existing works generally solve it by a dual-stream framework, which consists of a text encoder, a visual encoder and a cross-modal loss function. Although some progress has been made, they failed to fully exploit the information at various levels of granularity. To tackle this issue, we propose a novel framework for the natural language-based vehicle retrieval task, OMG, which Observes Multiple Granularities with respect to visual representation, textual representation and objective functions. For the visual representation, target features, context features and motion features are encoded separately. For the textual representation, one global embedding, three local embeddings and a color-type prompt embedding are extracted to represent various granularities of semantic features. Finally, the overall framework is optimized by a cross-modal multi-granularity contrastive loss function. Experiments demonstrate the effectiveness of our method. Our OMG significantly outperforms all previous methods and ranks the 9th on the 6th AI City Challenge Track2.

关键词： Representation learning Visualization Smart cities Surveillance Semantics Feature extraction pattern recognition

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Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack

Breaching FedMD: Image Recovery via Paired-Logits Inversion ...

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

作者： Takahashi, Hideaki Liu, Jingjing Liu, Yang Univ Tokyo Tokyo Japan Tsinghua Univ Inst AI Ind Res Beijing Peoples R China Tsinghua Univ Inst AI Ind Res Shanghai Artificial Intelligence Lab Beijing Peoples R China

ISBN: (纸本)9798350301298

Federated Learning with Model Distillation (FedMD) is a nascent collaborative learning paradigm, where only output logits of public datasets are transmitted as distilled knowledge, instead of passing on private model parameters that are susceptible to gradient inversion attacks, a known privacy risk in federated learning. In this paper, we found that even though sharing output logits of public datasets is safer than directly sharing gradients, there still exists a substantial risk of data exposure caused by carefully designed malicious attacks. Our study shows that a malicious server can inject a PLI (Paired-Logits Inversion) attack against FedMD and its variants by training an inversion neural network that exploits the confidence gap between the server and client models. Experiments on multiple facial recognition datasets validate that under FedMD-like schemes, by using paired server-client logits of public datasets only, the malicious server is able to reconstruct private images on all tested benchmarks with a high success rate.

关键词： accountability ethics in vision fairness privacy Transparency

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Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications

Regression or Classification? Reflection on BP prediction fr...

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

作者： Schrumpf, Fabian Serdack, Paul Rudi Fuchs, Mirco Leipzig Univ Appl Sci Leipzig Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart rate analysis or blood oxygen level monitoring. In the recent past, research focused extensively on non-invasive PPG-based approaches to blood pressure (BP) estimation. These approaches can be subdivided into regression and classification methods. The latter assign PPG signals to predefined BP intervals that represent clinically relevant ranges. The former predict systolic (SBP) and diastolic (DBP) BP as continuous variables and are of particular interest to the research community. However, the reported accuracies of BP regression methods vary widely among publications with some authors even questioning the feasibility of PPG-based BP regression altogether. In our work, we compare BP regression and classification approaches. We argue that BP classification might provide diagnostic value that is equivalent to regression in many clinically relevant scenarios while being similar or even superior in terms of performance. We compare several established neural architectures using publicly available PPG data for SBP regression and classification with and without personalization using subject-specific data. We found that classification and regression models perform similar before personalization. However, after personalization, the accuracy of classification based methods outperformed regression approaches. We conclude that BP classification might be preferable over BP regression in certain scenarios where a coarser segmentation of the BP range is sufficient.

关键词： Heart rate Deep learning computer vision conferences Neural networks Estimation computer architecture

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Deep Scale-space Mining Network for Single Image Deraining

Deep Scale-space Mining Network for Single Image Deraining

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

作者： Li, Pengpeng Jin, Jiyu Jin, Guiyue Fan, Lei Gao, Xiao Song, Tianyu Chen, Xiang Dalian Polytech Univ Sch Informat Sci & Engn Dalian Peoples R China Shenyang Aerosp Univ Coll Elect & Informat Engn Shenyang Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Images captured by outdoor vision systems can often be affected by rain weather, resulting in severe degradation of the visual quality of the captured images. Therefore, image deraining has attracted attention as urgent and challenging research. Many current data-driven approaches achieve better performance but are limited in recovering image details. This is because these methods do not fully mine the correlation of scale-space, which are beneficial for rain removal. In this paper, we design an end-to-end Deep Scalespace Mining Network (DSM-Net) for single image deraining to solve these problems. The proposed network with multi-scale extraction, concurrent attention distillation, and hierarchical information fusion accurately captures scale-space features and learns richer information for better deraining. For better feature extraction, a Multi-scale Attention Block (MAB) is introduced to obtain multi-scale rain streak features by different dilated convolutions. Besides,a Concurrent Attention Distillation Block (CADB) is developed which combined channel attention and subspace attention to calibrate the image features obtained from multiscale acquisition and hierarchical learning, then eliminate redundant features. Importantly, the overall architecture of DSM-Net is inspired by the HourglassNet and DenseNet, which progressively explores and fuses local and global features at different scales in a hierarchical manner instead of direct concatenation. Extensive experiments on synthetic and real datasets show that the proposed DSM-Net outperforms recent state-of-the-art deraining algorithms in terms of both performance and preservation of image details.

关键词： Degradation Deep learning Visualization Rain Correlation Fuses Machine vision

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RV-GAN: Recurrent GAN for Unconditional Video Generation

RV-GAN: Recurrent GAN for Unconditional Video Generation

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

作者： Gupta, Sonam Keshari, Arti Das, Sukhendu Indian Inst Technol Dept Comp Sci Engn Visualizat & Percept Lab Madras Tamil Nadu India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generative models aiming to generate content from noise have achieved high-fidelity synthesis for image data. However, obtaining comparable performance in the field of unconditional video generation still remains challenging. In this work, we propose a recurrent GAN architecture to model the high-dimensional video data distribution. Recurrent networks by design are able to generate complex, long sequences in an autoregressive fashion. However, the standard LSTM unit for videos (ConvLSTM) is not ideally suited for the task of unconditional video generation. Therefore, we propose a simple yet effective LSTM variant called as TransConv LSTM (TC-LSTM) by modulating the conventional ConvLSTM to have a transpose convolutional structure in input-to-state transitions. This enables the network to model both spatial and temporal relationships across layers simultaneously inside the TC-LSTM unit. TC-LSTM unit acts as a building block of our generator. Extensive quantitative and qualitative analysis shows that RV-GAN outperforms state-of-the-art methods by a significant margin on Moving MNIST, MUG, Weizmann and UCF101 datasets. Additionally, owing to the recurrent structure, our method is able to generate high-quality videos, up to 2 times longer (32 frames) than training videos at inference time. Further analysis confirms that the proposed architecture is generic and can be easily adapted to other tasks like class-conditional video synthesis and text-to-video synthesis.

关键词： Training Architecture computer architecture Generative adversarial networks Data models Hybrid power systems Generators

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