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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yuekun Dai Dafeng Zhang Xiaoming Li Zongsheng Yue Chongyi Li Shangchen Zhou Ruicheng Feng Peiqing Yang Zhezhu Jin Guanqun Liu Chen Change Loy S-Lab Nanyang Technological University Samsung Research China Nankai University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024.

关键词： Photography Image sensors Industries computer vision Reviews conferences Focusing

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Lifting Monocular Events to 3D Human Poses

Lifting Monocular Events to 3D Human Poses

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

作者： Scarpellini, Gianluca Morerio, Pietro Del Bue, Alessio Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Univ Genoa Genoa Italy Ist Italiano Tecnol Visual Geometry & Modelling Genoa Italy

ISBN: (纸本)9781665448994

This paper presents a novel 3D human pose estimation approach using a single stream of asynchronous events as input. Most of the state-of-the-art approaches solve this task with RGB cameras, however struggling when subjects are moving fast. On the other hand, event-based 3D pose estimation benefits from the advantages of event-cameras, especially their efficiency and robustness to appearance changes. Yet, finding human poses in asynchronous events is in general more challenging than standard RGB pose estimation, since little or no events are triggered in static scenes. Here we propose the first learning-based method for 3D human pose from a single stream of events. Our method consists of two steps. First, we process the event-camera stream to predict three orthogonal heatmaps per joint;each heatmap is the projection of of the joint onto one orthogonal plane. Next, we fuse the sets of heatmaps to estimate 3D localisation of the body joints. As a further contribution, we make available a new, challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. Experiments demonstrate that our method achieves solid accuracy, narrowing the performance gap between standard RGB and event-based vision. The code is freely available at https://***/lifting_events_to_3d_hpe.

关键词： Heating systems Learning systems Three-dimensional displays Fuses Pose estimation Solids Robustness

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GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes

GraFIQs: Face Image Quality Assessment Using Gradient Magnit...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jan Niklas Kolf Naser Damer Fadi Boutros Fraunhofer Institute for Computer Graphics Research IGD Germany Technical University of Darmstadt Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset. To achieve that, we propose quantifying the discrepancy in Batch Normalization statistics (BNS), including mean and variance, between those recorded during FR training and those obtained by processing testing samples through the pretrained FR model. We then generate gradient magnitudes of pretrained FR weights by backpropagating the BNS through the pretrained model. The cumulative absolute sum of these gradient magnitudes serves as the FIQ for our approach. Through comprehensive experimentation, we demonstrate the effectiveness of our training-free and quality labeling-free approach, achieving competitive performance to recent state-of-the-art FIQA approaches without relying on quality labeling, the need to train regression networks, specialized architectures, or designing and optimizing specific loss functions. 1

关键词： Training Image quality Weight measurement computer vision Face recognition conferences computer architecture

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Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection

Pseudo-IoU: Improving Label Assignment in Anchor-Free Object...

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

作者： Li, Jiachen Cheng, Bowen Feris, Rogerio Xiong, Jinjun Huang, Thomas S. Hwu, Wen-Mei Shi, Humphrey UIUC Champaign IL 61820 USA MIT IBM Watson AI Lab Cambridge MA USA IBM TJ Watson Res Ctr Ossining NY USA NVIDIA Santa Clara CA USA Univ Oregon Eugene OR 97403 USA Picsart AI Res PAIR Champaign IL USA

ISBN: (纸本)9781665448994

Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union (IoU) metric. In this paper, we present Pseudo-Intersection-over-Union (Pseudo-IoU): a simple metric that brings more standardized and accurate assignment rule into anchor-free object detection frameworks without any additional computational cost or extra parameters for training and testing, making it possible to further improve anchor-free object detection by utilizing training samples of good quality under effective assignment rules that have been previously applied in anchor-based methods. By incorporating Pseudo-IoU metric into an end-to-end single-stage anchor-free object detection framework, we observe consistent improvements in their performance on general object detection benchmarks such as PASCAL VOC and MSCOCO. Our method (single-model and single-scale) also achieves comparable performance to other recent state-of-the-art anchor-free methods without bells and whistles. Our code is based on mmdetection toolbox and will be made publicly available at https://***/SHILabs/Pseudo-IoU-for-Anchor-Free-Object-Detection.

关键词： Measurement Training Location awareness computer vision Computational modeling conferences Object detection

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A Closer Look at Self-training for Zero-Label Semantic Segmentation

A Closer Look at Self-training for Zero-Label Semantic Segme...

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

作者： Pastore, Giuseppe Cermelli, Fabio Xian, Yongqin Mancini, Massimiliano Akata, Zeynep Caputo, Barbara Politecn Torino Turin Italy Italian Inst Technol Genoa Italy MPI Informat Saarbrucken Germany Univ Tubingen Tubingen Germany MPI Intelligent Syst Saarbrucken Germany

ISBN: (纸本)9781665448994

Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. However, they are prone to overfitting on the seen classes because there is no training signal for them. In this paper, we study the challenging generalized zero-label semantic segmentation task where the model has to segment both seen and unseen classes at test time. We assume that pixels of unseen classes could be present in the training images but without being annotated. Our idea is to capture the latent information on unseen classes by supervising the model with self-produced pseudo-labels for unlabeled pixels. We propose a consistency regularizer to filter out noisy pseudolabels by taking the intersections of the pseudo-labels generated from different augmentations of the same image. Our framework generates pseudo-labels and then retrain the model with human-annotated and pseudo-labelled data. This procedure is repeated for several iterations. As a result, our approach achieves the new state-of-the-art on PascalVOC12 and COCO-stuff datasets in the challenging generalized zero-label semantic segmentation setting, surpassing other existing methods addressing this task with more complex strategies. Code can be found at https: //***/giuseppepastore10/STRICT.

关键词： Training Image segmentation Semantics Pipelines Predictive models Information filters pattern recognition

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Phenology Alignment Network: A Novel Framework for Cross-Regional Time Series Crop Classification

Phenology Alignment Network: A Novel Framework for Cross-Reg...

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

作者： Wang, Ziqiao Zhang, Hongyan He, Wei Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China RIKEN Ctr Adv Intelligence Project AIP Tokyo 1030027 Japan

ISBN: (纸本)9781665448994

Timely and accurate crop type classification plays an essential role in the study of agricultural application. However, large area or cross-regional crop classification confronts huge challenges owing to dramatic phenology discrepancy among training and test regions. In this work, we propose a novel framework to address these challenges based on deep recurrent network and unsupervised domain adaptation (DA). Specifically, we firstly propose a Temporal Spatial Network (TSNet) for pixelwise crop classification, which contains stacked RNN and self-attention module to adaptively extract multi-level features from crop samples under various planting conditions. To deal with the cross-regional challenge, an unsupervised DA-based framework named Phenology Alignment Network (PAN) is proposed. PAN consists of two branches of two identical TSNet pre-trained on source domain;one branch takes source samples while the other takes target samples as input. Through aligning the hierarchical deep features extracted from two branches, the discrepancy between two regions is decreased and the pre-trained model is adapted to the target domain without using target label information. As another contribution, a time series dataset based on Sentinel-2 was annotated containing winter crop samples collected on three study sites of China. Cross-regional experiments demonstrate that TSNet shows comparable accuracy to state-of-the-art methods, and PAN further improves the overall accuracy by 5.62%, and macro average F1 score by 0.094 unsupervisedly.

关键词： Training computer vision Adaptation models conferences Time series analysis Feature extraction Agriculture

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Mitigating synchronization bottlenecks in high-performance actor-model-based software

Mitigating synchronization bottlenecks in high-performance a...

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2024 workshops of the International conference for High Performance Computing, Networking, Storage and Analysis, SC workshops 2024

作者： Klenk, Kyle Moayeri, Mohammad Mahdi Guo, Junwei Clark, Martyn P. Spiteri, Raymond J. University of Saskatchewan Department of Computer Science Saskatoon Canada University of Calgary Department of Civil Engineering Calgary Canada

ISBN: (纸本)9798350355543

Bulk synchronous programming (in distributed-memory systems) and the fork-join pattern (in shared-memory systems) are often used for problems where independent processes must periodically synchronize. Frequent synchronization can greatly undermine the performance of software designed to solve such problems. We use the actor model of concurrent computing to balance the load of hundreds of thousands of short-lived tasks and mitigate synchronization bottlenecks by buffering communication via actor batching. The actor model is becoming increasingly popular in scientific and high-performance computing because it can handle heterogeneous tasks and computing environments with enhanced programming flexibility and ease relative to conventional paradigms like MPI. For a hydrologic simulation of continental North America with over 500,000 elements, the proposed buffering approach is approximately 4 times faster than no buffering, outperforms MPI on single and multiple nodes, and remains competitive with OpenMP on a single node and MPI+OpenMP on multiple nodes. © 2024 ieee.

关键词： actor model of concurrent computing bulk synchronous programming fork-join pattern high-throughput computing scientific and high-performance computing

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Exploring the Zero-Shot Capabilities of vision-Language Models for Improving Gaze Following

Exploring the Zero-Shot Capabilities of Vision-Language Mode...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Anshul Gupta Pierre Vuillecard Arya Farkhondeh Jean-Marc Odobez Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Contextual cues related to a person’s pose and interactions with objects and other people in the scene can provide valuable information for gaze following. While existing methods have focused on dedicated cue extraction methods, in this work we investigate the zero-shot capabilities of vision-Language Models (VLMs) for extracting a wide array of contextual cues to improve gaze following performance. We first evaluate various VLMs, prompting strategies, and in-context learning (ICL) techniques for zero-shot cue recognition performance. We then use these insights to extract contextual cues for gaze following, and investigate their impact when incorporated into a state of the art model for the task. Our analysis indicates that BLIP-2 is the overall top performing VLM and that ICL can improve performance. We also observe that VLMs are sensitive to the choice of the text prompt although ensembling over multiple text prompts can provide more robust performance. Additionally, we discover that using the entire image along with an ellipse drawn around the target person is the most effective strategy for visual prompting. For gaze following, incorporating the extracted cues results in better generalization performance, especially when considering a larger set of cues, highlighting the potential of this approach.

关键词： Visualization computer vision conferences Computational modeling Predictive models pattern recognition Context modeling

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Boosting Co-teaching with Compression Regularization for Label Noise

Boosting Co-teaching with Compression Regularization for Lab...

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

作者： Chen, Yingyi Shen, Xi Hu, Shell Xu Suykens, Johan A. K. Katholieke Univ Leuven ESAT STADIUS Leuven Belgium UPE Ecole Ponts LIGM UMR 8049 Champs Sur Marne France Upload AI LLC Houston TX USA

ISBN: (纸本)9781665448994

In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout [22]. We find that Nested Dropout [22], though originally proposed to perform fast information retrieval and adaptive data compression, can properly regularize a neural network to combat label noise. Moreover, owing to its simplicity, it can be easily combined with Co-teaching [5] to further boost the performance. Our final model remains simple yet effective: it achieves comparable or even better performance than the state-of-the-art approaches on two real-world datasets with label noise which are Clothing1M [28] and ANIMAL-10N [24]. On Clothing1M [28], our approach obtains 74.9% accuracy which is slightly better than that of DivideMix [12]. On ANIMAL-10N [24], we achieve 84.1% accuracy while the best public result by PLC [30] is 83.4%. We hope that our simple approach can be served as a strong baseline for learning with label noise. Our implementation is available at https://***/yingyichen-cyy/Nested-Co-teaching.

关键词： Adaptation models computer vision Image coding conferences Neural networks Data compression Information retrieval

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DeCAtt: Efficient vision Transformers with Decorrelated Attention Heads

DeCAtt: Efficient Vision Transformers with Decorrelated Atte...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Mayukh Bhattacharyya Soumitri Chattopadhyay Sayan Nag Stony Brook University Jadavpur University University of Toronto

The advent of vision Transformers (ViT) has led to significant performance gains across various computer vision tasks over the last few years, surpassing the de facto standard CNN architectures. However, most of the prominent variations of vision Transformers are resource-intensive architectures with huge parameter sizes. They are known to be data-hungry and overfit quickly on comparatively smaller datasets. Consequently, this holds back their widespread usage across low-resource settings, which brings forth the need to develop resource-efficient vision transformers. To this end, we introduce a regularization loss that prioritizes efficient utilization of model parameters by decorrelating the heads of a multi-headed attention block in a vision transformer. This forces the heads to learn distinct features rather than focus on the same ones. Using this loss provides a consistent performance improvement over a wide range of varying scenarios of models and datasets as we show in our experiments, which proves its superior effectiveness.

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