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EFI-Net: Video Frame Interpolation from Fusion of Events and Frames

EFI-Net: Video Frame Interpolation from Fusion of Events and...

引用

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

作者： Paikin, Genady Ater, Yotam Shaul, Roy Soloveichik, Evgeny Samsung Israel R&D Ctr Tel Aviv Israel

ISBN: (纸本)9781665448994

Event cameras are sensors with pixels that respond independently and asynchronously to changes in scene illumination. Event cameras have a number of advantages when compared to conventional cameras: low-latency, high temporal resolution, high dynamic range, low power and sparse data output. However, existing event cameras also suffer from comparatively low spatial resolution and are sensitive to noise. Recently, it has been shown that it is possible to reconstruct an intensity frame stream from an event stream. These reconstructions preserve the high temporal rate of the event stream, but tend to suffer from significant artifacts and low image quality due to the shortcomings of event cameras. In this work we demonstrate that it is possible to combine the best of both worlds, by fusing a color frame stream at low temporal resolution and high spatial resolution with an event stream at high temporal resolution and low spatial resolution to generate a video stream with both high temporal and spatial resolutions while preserving the original color information. We utilize a novel event frame interpolation network (EFI-Net), a multi-phase convolutional neural network which fuses the frame and event streams. EFI-Net is trained using only simulated data and generalizes exceptionally well to real-world experimental data. We show that our method is able to interpolate frames where traditional video interpolation approaches fail, while also outperforming event-only reconstructions. We further contribute a new dataset, containing event camera data synchronized with high speed video. This work opens the door to a new application for event cameras, enabling high fidelity fusion with frame based image streams for generation of high-quality high-speed video. The dataset is available at https://***/file/d/1UIGVBqNER_5KguYPAu5y7TVg-JlNhz3-/view?usp=sharing [GRAPHICS] .

关键词： Interpolation Image color analysis Lighting Streaming media Cameras Sensors pattern recognition

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Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey

Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Sur...

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

作者： Nicolas Chahine Marcos V. Conde Daniela Carfora Gabriel Pacianotto Benoit Pochon Sira Ferradans Radu Timofte Zhichao Duan Xinrui Xu Yipo Huang Quan Yuan Xiangfei Sheng Zhichao Yang Leida Li Haotian Fan Fangyuan Kong Yifang Xu Wei Sun Weixia Zhang Yanwei Jiang Haoning Wu Zicheng Zhang Jun Jia Yingjie Zhou Zhongpeng Ji Xiongkuo Min Weisi Lin Guangtao Zhai Xiaoqi Wang Junqi Liu Zixi Guo Yun Zhang Zewen Chen Wen Wang Juan Wang Bing Li DXOMARK CAIDAS & IFI Computer Vision Lab University of Würzburg Xidian University ByteDance Inc Shanghai Jiao Tong University Nanyang Technological University Huawei School of Electronics and Communication Engineering Sun Yat-sen University China State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Jiaotong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 Portrait Quality Assessment Challenge, highlighting the proposed solutions and results. This challenge aims to obtain an efficient deep neural network capable of estimating the perceptual quality of real portrait photos. The methods must generalize to diverse scenes and diverse lighting conditions (indoor, outdoor, low-light), movement, blur, and other challenging conditions. In the challenge, 140 participants registered, and 35 submitted results during the challenge period. The performance of the top 5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in Portrait Quality Assessment.

关键词： Surveys computer vision Reviews conferences Lighting Artificial neural networks Quality assessment pattern recognition

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Where are they looking in the 3D space?

Where are they looking in the 3D space?

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

作者： Nora Horanyi Linfang Zheng Eunji Chong Aleš Leonardis Hyung Jin Chang School of Computer Science University of Birmingham *** Inc.

We propose a novel depth-aware joint attention target estimation framework that estimates the attention target in 3D space. Our goal is to mimic human’s ability to understand where each person is looking in their proximity. In this work, we tackle the previously unexplored problem of utilising a depth prior along with a 3D joint FOV probability map to estimate the joint attention target of people in the scene. We leverage the insight that besides the 2D image content, strong gaze-related constraints exist in the depth order of the scene and different subject-specific attributes. Extensive experiments show that our method outperforms favourably against existing joint attention target estimation methods on the VideoCoAtt benchmark dataset. Despite the proposed framework being designed for joint attention target estimation, we show that it outperforms single attention target estimation methods on both the GazeFollow image and the VideoAttentionTarget video benchmark datasets.

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Federated Hyperparameter Optimization through Reward-Based Strategies: Challenges and Insights

Federated Hyperparameter Optimization through Reward-Based S...

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

作者： Krishna Kanth Nakka Ahmed Frikha Ricardo Mendis Xue Jiang Xuebing Zhou Huawei Munich Research Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Performing hyperparameter tuning in federated learning is often prohibitively expensive due to the substantial communication overhead associated with training a single configuration, especially with a large hyperparameter search space. To overcome this challenge, recent works explored reward-based approaches to learn a policy distribution over a set of hyperparameter configurations. These approaches enable the concurrent exploration of multiple hyperparameter configurations within a single communication round, thereby accelerating the search *** this paper, we take a deeper look at the reward-based strategies and systematically analyze them, uncovering several issues and challenges associated with their adoption in practice. Furthermore, motivated by the insights from our analysis, we propose an in-depth evaluation of policy distribution with metrics that capture rankings of standalone configurations. We contribute this critical examination and proposed evaluation metrics in order to raise awareness about the challenges and hidden issues that reward-based federated hyperparameter optimization might face and to enable a more rigorous evaluation and therefore a faster progress in this research area. We expect that the identified challenges will serve as inspiration for the development of more robust and hyperparameter-free federated hyperparameter tuning approaches.

关键词： Training computer vision Systematics Sensitivity Federated learning Face recognition conferences

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StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation

StillFast: An End-to-End Approach for Short-Term Object Inte...

引用

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

作者： Francesco Ragusa Giovanni Maria Farinella Antonino Furnari FPV@IPLab University of Catania Next Vision s.r.l. - Spin-off of the University of Catania

Anticipation problem has been studied considering different aspects such as predicting humans’ locations, predicting hands and objects trajectories, and forecasting actions and human-object interactions. In this paper, we studied the short-term object interaction anticipation problem from the egocentric point of view, proposing a new end-to-end architecture named StillFast. Our approach simultaneously processes a still image and a video detecting and localizing next-active objects, predicting the verb which describes the future interaction and determining when the interaction will start. Experiments on the large-scale egocentric dataset EGO4D [17] show that our method outperformed state-of-the-art approaches on the considered task. Our method is ranked first in the public leaderboard of the EGO4D short term object interaction anticipation challenge 2022 and it is the official baseline for the 2023 one. Please see the project web page for code and additional details: https://***/stillfast/.

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Language Guided Local Infiltration for Interactive Image Retrieval

Language Guided Local Infiltration for Interactive Image Ret...

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

作者： Fuxiang Huang Lei Zhang Learning Intelligence & Vision Essential (LiVE) Group School of Microelectronics and Communication Engineering Chongqing University China

Interactive Image Retrieval (IIR) aims to retrieve images that are generally similar to the reference image but under the requested text modification. The existing methods usually concatenate or sum the features of image and text simply and roughly, which, however, is difficult to precisely change the local semantics of the image that the text intends to modify. To solve this problem, we propose a Language Guided Local Infiltration (LGLI) system, which fully utilizes the text information and penetrates text features into image features as much as possible. Specifically, we first propose a Language Prompt Visual Localization (LPVL) module to generate a localization mask which explicitly locates the region (semantics) intended to be modified. Then we introduce a Text Infiltration with Local Awareness (TILA) module, which is deployed in the network to precisely modify the reference image and generate image-text infiltrated representation. Extensive experiments on various benchmark databases validate that our method outperforms most state-of-the-art IIR approaches.

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Robustness of Visual Explanations to Common Data Augmentation Methods

Robustness of Visual Explanations to Common Data Augmentatio...

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

作者： Lenka Tĕtková Lars Kai Hansen Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but their reliability is being called into question. Our research investigates the response of post-hoc visual explanations to naturally occurring transformations, often referred to as augmentations. We anticipate explanations to be invariant under certain transformations, such as changes to the colour map while responding in an equivariant manner to transformations like translation, object scaling, and rotation. We have found remarkable differences in robustness depending on the type of transformation, with some explainability methods (such as LRP composites and Guided Backprop) being more stable than others. We also explore the role of training with data augmentation. We provide evidence that explanations are typically less robust to augmentation than classification performance, regardless of whether data augmentation is used in training or not.

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PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt Condition

PCQA: A Strong Baseline for AIGC Quality Assessment Based on...

引用

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

作者： Xi Fang Weigang Wang Xiaoxin Lv Jun Yan DP Technology Cisco Shopee TongJi University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The development of Large Language Models (LLM) and Diffusion Models brings the boom of Artificial Intelligence Generated Content (AIGC). It is essential to build an effective quality assessment framework to provide a quantifiable evaluation of different images or videos based on the AIGC technologies. The content generated by AIGC methods is driven by the crafted prompts. Therefore, it is intuitive that the prompts can also serve as the foundation of the AIGC quality assessment. This study proposes an effective AIGC quality assessment (QA) framework. First, we propose a hybrid prompt encoding method based on a dual-source CLIP (Contrastive Language-Image Pre-Training) text encoder to understand and respond to the prompt conditions. Second, we propose an ensemble-based feature mixer module to effectively blend the adapted prompt and vision features. The empirical study practices in two datasets: AIGIQA-20K (AI-Generated Image Quality Assessment database) and T2VQA-DB (Text-to-Video Quality Assessment DataBase), which validates the effectiveness of our proposed method: Prompt Condition Quality Assessment (PCQA). Our proposed simple and feasible framework may promote research development in the multimodal generation field.

关键词： Image quality Databases Large language models Semantics Quality assessment pattern recognition Ensemble learning

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Video-based Person Re-identification without Bells and Whistles

Video-based Person Re-identification without Bells and Whist...

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

作者： Liu, Chih-Ting Chen, Jun-Cheng Chen, Chu-Song Chien, Shao-Yi Natl Taiwan Univ Grad Inst Elect & Engn Taipei Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781665448994

Video-based person re-identification (Re-ID) aims at matching the video tracklets with cropped video frames for identifying the pedestrians under different cameras. However, there exists severe spatial and temporal misalignment for those cropped tracklets due to the imperfect detection and tracking results generated with obsolete methods. To address this issue, we present a simple re-Detect and Link (DL) module which can effectively reduce those unexpected noise through applying the deep learning-based detection and tracking on the cropped tracklets. Furthermore, we introduce an improved model called Coarse-to-Fine Axial-Attention Network (CF-AAN). Based on the typical Nonlocal Network, we replace the non-local module with three 1-D position-sensitive axial attentions, in addition to our proposed coarse-to-fine structure. With the developed CF-AAN, compared to the original non-local operation, we can not only significantly reduce the computation cost but also obtain the state-of-the-art performance (91:3% in rank-1 and 86:5% in mAP) on the large-scale MARS dataset. Meanwhile, by simply adopting our DL module for data alignment, to our surprise, several baseline models can achieve better or comparable results with the current state-of-the-arts. Besides, we discover the errors not only for the identity labels of tracklets but also for the evaluation protocol for the test data of MARS. We hope that our work can help the community for the further development of invariant representation without the hassle of the spatial and temporal alignment and dataset noise. The code, corrected labels, evaluation protocol, and the aligned data will be available at https://***/jackie840129/CF-AAN.

关键词： computer vision Protocols Computational modeling conferences Lighting Cameras Data models

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Generating Diverse Agricultural Data for vision-Based Farming Applications

Generating Diverse Agricultural Data for Vision-Based Farmin...

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

作者： Mikolaj Cieslak Umabharathi Govindarajan Alejandro Garcia Anuradha Chandrashekar Torsten Hädrich Aleksander Mendoza-Drosik Dominik L. Michels Sören Pirk Chia-Chun Fu Wojciech Pałubicki GreenMatterAI Blue River Technology King Abdullah University of Science and Technology Saudi Arabia and Technical University of Darmstadt Germany Christian-Albrecht University of Kiel Germany Adam Mickiewicz University in Poznań Poland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model’s effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

关键词： Training Adaptation models Semantic segmentation Crops Training data Soil Data augmentation

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