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Learning Sample Relationship for Exposure Correction

Learning Sample Relationship for Exposure Correction

引用

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

作者： Huang, Jie Zhao, Feng Zhou, Man Xiao, Jie Zheng, Naishan Zheng, Kaiwen Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency. In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a mini-batch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design.

关键词： Low-level vision

来源：评论

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DiCo-NeRF: Difference of Cosine Similarity for Neural Rendering of Fisheye Driving Scenes

DiCo-NeRF: Difference of Cosine Similarity for Neural Render...

引用

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

作者： Choi, Jiho Hwang, Gyutae Lee, Sang Jun Jeonbuk Natl Univ Jeonju South Korea

ISBN: (纸本)9798350365474

Neural radiance fields have emerged in the field of autonomous driving, which contributes to improve perception of the complex 3D environment through the reconstruction of geometry and appearance. Moving objects and sky for outdoor environment is challenging to optimize the NeRF model. Previous work addresses these challenges through preprocessing such as masking;however, the masking process requires additional ground-truth data and a segmentation network. We propose DiCo-NeRF, an approach for driving scenes by leveraging cosine similarity map differences of vision-language aligned model. DiCo-NeRF investigates the correlation between rendered patches and pre-defined text and adjusts the loss of challenging patches, such as moving objects and the sky. Our neural radiance field utilizes embedding vectors from a pre-trained CLIP to obtain the cosine similarity maps. We introduce SimLoss, a loss function aimed at regulating the color field of NeRF based on the quantified distribution differences between ground-truth and rendered similarity maps. Unlike previous NeRF models that used driving datasets, our approach does not require additional input, such as sensor data, to the model. Experimental results demonstrate that the incorporation of language semantic cues improves the performance of the novel view synthesis task, particularly in complex driving environments. We conducted experiments that included fisheye driving scenes from the KITTI360 and real-world datasets. Our code is available at https://***/ziiho08/DiCoNeRF.

关键词： Autonomous driving Fisheye camera Neural Radiance Fields vision-Language models

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Divide and Conquer Boosting for Enhanced Traffic Safety Description and Analysis with Large vision Language Model

Divide and Conquer Boosting for Enhanced Traffic Safety Desc...

引用

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

作者： Khai Trinh Xuan Khoi Nguyen Nguyen Bach Hoang Ngo Vu Dinh Xuan Minh-Hung An Quang-Vinh Dinh Ho Chi Minh City Univ Technol VNU HCM Ho Chi Minh City Vietnam Univ Sci VNU HCM Ho Chi Minh City Vietnam Univ Informat Technol VNU HCM Ho Chi Minh City Vietnam FPT Telecom Hanoi Vietnam AI Lab AI VIETNAM Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam

ISBN: (纸本)9798350365474

The increasing complexity of traffic dynamics has underscored the necessity for advanced traffic safety description and analysis, challenging the efficacy of current methodologies in comprehensively understanding and predicting safety conditions from transportation videos. This paper addresses these challenges by analyzing specific segments crucial for precise traffic safety descriptions. Through this examination, we introduce an innovative preprocessing method named "segment extraction", facilitating the creation of a novel segment-based training dataset. Additionally, we present a practical two-stage training framework specifically tailored for this dataset. This framework is designed to produce accurate descriptions of traffic safety by incorporating the unique attributes of our segment-based training datasets. Leveraging these contributions, our method achieved a notable 2nd rank with a score of 32.8877 in the AI City Challenge Track2 test set: Traffic Safety Description and Analysis 2024. The source code for the proposed approaches is openly accessible at https://***/AIVIETNAMResearch/AI-CIty2024-Track2

关键词： large language model large vision language model video captioning video instance captioning video-text model

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Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-Resolution

Equivalent Transformation and Dual Stream Network Constructi...

引用

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

作者： Chao, Jiahao Zhou, Zhou Gao, Hongfan Gong, Jiali Yang, Zhengfeng Zeng, Zhenbing Dehbi, Lydia East China Normal Univ Shanghai Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Beijing Peoples R China

ISBN: (纸本)9798350301298

In recent years, there has been an increasing demand for real-time super-resolution networks on mobile devices. To address this issue, many lightweight super-resolution models have been proposed. However, these models still contain time-consuming components that increase inference latency, limiting their real-world applications on mobile devices. In this paper, we propose a novel model for single-image super-resolution based on Equivalent Transformation and Dual Stream network construction (ETDS). ET method is proposed to transform time-consuming operators into time-friendly operations, such as convolution and ReLU, on mobile devices. Then, a dual stream network is designed to alleviate redundant parameters resulting from the use of ET and enhance the feature extraction ability. Taking full advantage of the advance of ET and the dual stream network structure, we develop the efficient SR model ETDS for mobile devices. The experimental results demonstrate that our ETDS achieves superior inference speed and reconstruction quality compared to previous lightweight SR methods on mobile devices. The code is available at https://***/ECNUSR/ETDS.

关键词： Low-level vision

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Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank

Contrastive Semi-supervised Learning for Underwater Image Re...

引用

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

作者： Huang, Shirui Wang, Keyan Liu, Huan Chen, Jun Li, Yunsong Xidian Univ Xian Peoples R China McMaster Univ Hamilton ON Canada

ISBN: (纸本)9798350301298

Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress. In this work, we propose a mean-teacher based Semi-supervised Underwater Image Restoration (Semi-UIR) framework to incorporate the unlabeled data into network training. However, the naive mean-teacher method suffers from two main problems: (1) The consistency loss used in training might become ineffective when the teacher's prediction is wrong. (2) Using L1 distance may cause the network to overfit wrong labels, resulting in confirmation bias. To address the above problems, we first introduce a reliable bank to store the "best-ever" outputs as pseudo ground truth. To assess the quality of outputs, we conduct an empirical analysis based on the monotonicity property to select the most trustworthy NR-IQA method. Besides, in view of the confirmation bias problem, we incorporate contrastive regularization to prevent the overfitting on wrong labels. Experimental results on both full-reference and non-reference underwater benchmarks demonstrate that our algorithm has obvious improvement over SOTA methods quantitatively and qualitatively. Code has been released at https://***/Huang-ShiRui/Semi-UIR.

关键词： Low-level vision

来源：评论

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MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection

MixTeacher: Mining Promising Labels with Mixed Scale Teacher...

引用

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

作者： Liu, Liang Zhang, Boshen Zhang, Jiangning Zhang, Wuhao Gan, Zhenye Tian, Guanzhong Zhu, Wenbing Wang, Yabiao Wang, Chengjie Tencent Youtu Lab Shenzhen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Zhejiang Univ Ningbo Res Inst Hangzhou Zhejiang Peoples R China Rongcheer Co Ltd Suzhou Peoples R China

ISBN: (纸本)9798350301298

Scale variation across object instances remains a key challenge in object detection task. Despite the remarkable progress made by modern detection models, this challenge is particularly evident in the semi-supervised case. While existing semi-supervised object detection methods rely on strict conditions to filter high-quality pseudo labels from network predictions, we observe that objects with extreme scale tend to have low confidence, resulting in a lack of positive supervision for these objects. In this paper, we propose a novel framework that addresses the scale variation problem by introducing a mixed scale teacher to improve pseudo label generation and scale-invariant learning. Additionally, we propose mining pseudo labels using score promotion of predictions across scales, which benefits from better predictions from mixed scale features. Our extensive experiments on MS COCO and PASCAL VOC benchmarks under various semi-supervised settings demonstrate that our method achieves new state-of-the-art performance. The code and models are available at https: //***/lliuz/MixTeacher.

关键词： detection recognition: Categorization retrieval

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R²Former: Unified Retrieval and Reranking Transformer for Place recognition

R<SUP>2</SUP>Former: Unified Retrieval and Reranking Transfo...

引用

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

作者： Zhu, Sijie Yang, Linjie Chen, Chen Shah, Mubarak Shen, Xiaohui Wang, Heng ByteDance Beijing Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9798350301298

Visual Place recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC-based geometric verification for reranking. However, RANSAC only employs geometric information but ignores other possible information that could be useful for reranking, e.g. local feature correlations, and attention values. In this paper, we propose a unified place recognition framework that handles both retrieval and reranking with a novel transformer model, named R(2)Former. The proposed reranking module takes feature correlation, attention value, and xy coordinates into account, and learns to determine whether the image pair is from the same location. The whole pipeline is end-to-end trainable and the reranking module alone can also be adopted on other CNN or transformer backbones as a generic component. Remarkably, R(2)Former significantly outperforms state-of-the-art methods on major VPR datasets with much less inference time and memory consumption. It also achieves the state-of-the-art on the hold-out MSLS challenge set and could serve as a simple yet strong solution for real-world large-scale applications. Experiments also show vision transformer tokens are comparable and sometimes better than CNN local features on local matching. The code is released at https://***/Jeff-Zilence/R2Former.

关键词： detection recognition: Categorization retrieval

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STDLens: Model Hijacking-resilient Federated Learning for Object Detection

STDLens: Model Hijacking-resilient Federated Learning for Ob...

引用

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

作者： Chow, Ka-Ho Liu, Ling Wei, Wenqi Ilhan, Fatih Wu, Yanzhao Georgia Instutite Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350301298

Federated Learning (FL) has been gaining popularity as a collaborative learning framework to train deep learning-based object detection models over a distributed population of clients. Despite its advantages, FL is vulnerable to model hijacking. The attacker can control how the object detection system should misbehave by implanting Trojaned gradients using only a small number of compromised clients in the collaborative learning process. This paper introduces STDLens, a principled approach to safeguarding FL against such attacks. We first investigate existing mitigation mechanisms and analyze their failures caused by the inherent errors in spatial clustering analysis on gradients. Based on the insights, we introduce a three-tier forensic framework to identify and expel Trojaned gradients and reclaim the performance over the course of FL. We consider three types of adaptive attacks and demonstrate the robustness of STDLens against advanced adversaries. Extensive experiments show that STDLens can protect FL against different model hijacking attacks and outperform existing methods in identifying and removing Trojaned gradients with significantly higher precision and much lower false-positive rates. The source code is available at https: //***/git-disl/STDLens.

关键词： accountability ethics in vision fairness privacy Transparency

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An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling

An Empirical Study of End-to-End Video-Language Transformers...

引用

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

作者： Fu, Tsu-Jui Li, Linjie Gan, Zhe Lin, Kevin Wang, William Yang Wang, Lijuan Liu, Zicheng UC Santa Barbara Santa Barbara CA 93106 USA Microsoft Redmond WA USA

ISBN: (纸本)9798350301298

Masked visual modeling (MVM) has been recently proven effective for visual pre-training. While similar re-constructive objectives on video inputs (e.g., masked frame modeling) have been explored in video-language (VidL) pre-training, previous studies fail to find a truly effective MVM strategy that can largely benefit the downstream performance. In this work, we systematically examine the potential of MVM in the context of VidL learning. Specifically, we base our study on a fully end-to-end VIdeO-LanguagE Transformer (VIOLET) [15], where the supervision from MVM training can be backpropogated to the video pixel space. In total, eight different reconstructive targets of MVM are explored, from low-level pixel values and oriented gradients to high-level depth maps, optical flow, discrete visual tokens and latent visual features. We conduct comprehensive experiments and provide insights into the factors leading to effective MVM training, resulting in an enhanced model VIOLETv2. Empirically, we show VIOLETv2 pre-trained with MVM objective achieves notable improvements on 13 VidL benchmarks, ranging from video question answering, video captioning, to text-to-video retrieval.(1)

关键词： and reasoning language vision

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Unsupervised Intrinsic Image Decomposition with LiDAR Intensity

Unsupervised Intrinsic Image Decomposition with LiDAR Intens...

引用

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

作者： Sato, Shogo Yao, Yasuhiro Yoshida, Taiga Kaneko, Takuhiro Ando, Shingo Shimamura, Jun INTT Human Informat Labs Tokyo Japan NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9798350301298

Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learning methods since there are no criteria for solving the ill-posed problems. Recently, light detection and ranging (LiDAR) is widely used due to its ability to make highly precise distance measurements. Thus, we have focused on the utilization of LiDAR, especially LiDAR intensity, to address this issue. In this paper, we propose unsupervised intrinsic image decomposition with LiDAR intensity (IID-LI). Since the conventional unsupervised learning methods consist of image-to-image transformations, simply inputting LiDAR intensity is not an effective approach. Therefore, we design an intensity consistency loss that computes the error between LiDAR intensity and gray-scaled albedo to provide a criterion for the ill-posed problem. In addition, LiDAR intensity is difficult to handle due to its sparsity and occlusion, hence, a LiDAR intensity densification module is proposed. We verified the estimating quality using our own dataset, which include RGB images, LiDAR intensity and human judged annotations. As a result, we achieved an estimation accuracy that outperforms conventional unsupervised learning methods.

关键词： Physics-based vision and shape-from-X

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