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SEM-POS: Grammatically and Semantically Correct Video Captioning

SEM-POS: Grammatically and Semantically Correct Video Captio...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Nadeem, Asmar Hilton, Adrian Dawes, Robert Thomas, Graham Mustafa, Annin United Kingdom Bbc Research and Development United Kingdom

ISBN: (纸本)9798350302493

Generating grammatically and semantically correct captions in video captioning is a challenging task.. The captions generated from the existing methods are either word-by-word that do not align with grammatical structure or miss key information from the input videos. To address these issues, we introduce a novel global-local fusion network, with a Global-Local Fusion Block (GLFB) that encodes and fuses features from different parts of speech (POS) components with visual-spatial features. We use novel combinations of different POS components - 'determinant + subject', 'auxiliary verb', 'verb', and 'determinant + object' for supervision of the POS blocks - Det + Subject, Aux Verb, Verb, and Det + Object respectively. The novel global-local fusion network together with POS blocks helps align the visual features with language description to generate grammatically and semantically correct captions. Extensive qualitative and quantitative experiments on benchmark MSVD and MSRVTT datasets demonstrate that the proposed approach generates more grammatically and semantically correct captions compared to the existing methods, achieving the new state-of-the-art. Ablations on the POS blocks and the GLFB demonstrate the impact of the contributions on the proposed method. © 2023 ieee.

关键词： computer vision

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Just a Glimpse: Rethinking Temporal Information for Video Continual Learning

Just a Glimpse: Rethinking Temporal Information for Video Co...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Alssum, Lama León Alcázar, Juan Ramazanova, Merey Zhao, Chen Ghanem, Bernard Saudi Arabia

ISBN: (纸本)9798350302493

Class-incremental learning is one of the most important settings for the study of Continual Learning, as it closely resembles real-world application scenarios. With constrained memory sizes, catastrophic forgetting arises as the number of classes/tasks increases. Studying continual learning in the video domain poses even more challenges, as video data contains a large number of frames, which places a higher burden on the replay memory. The current common practice is to sub-sample frames from the video stream and store them in the replay memory. In this paper, we propose SMILE a novel replay mechanism for effective video continual learning based on individual/single frames. Through extensive experimentation, we show that under extreme memory constraints, video diversity plays a more significant role than temporal information. Therefore, our method focuses on learning from a small number of frames that represent a large number of unique videos. On three representative video datasets, Kinetics, UCF101, and ActivityNet, the proposed method achieves state-of-the-art performance, outperforming the previous state-of-the-art by up to 21.49%o. © 2023 ieee.

关键词： computer vision

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Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion

Deep Convolutional Sparse Coding Networks for Interpretable ...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhao, Zixiang Zhang, Jiangshe Bai, Haowen Wang, Yicheng Cui, Yukun Deng, Lilun Sun, Kai Zhang, Chunxia Liu, Junmin Xu, Shuang Xi'an Jiaotong University China Eth Zurich Computer Vision Lab Switzerland Research and Development Institute Northwestern Polytechnical University in Shenzhen China Northwestern Polytechnical University China The University of Melbourne Australia

ISBN: (纸本)9798350302493

Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents CSCFuse, which contains three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion, and multi-spectral image fusion). The CSC model and the iterative shrinkage and thresholding algorithm are generalized into dictionary convolution units. As a result, all hyper-parameters are learned from data. Our extensive experiments and comprehensive comparisons reveal the superiority of CSCF use with regard to quantitative evaluation and visual inspection. © 2023 ieee.

关键词： Image fusion

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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...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

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

ISBN: (纸本)9798350302493

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/. © 2023 ieee.

关键词： Forecasting

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RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

RecycleNet: Latent Feature Recycling Leads to Iterative Deci...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Koehler, Gregor Wald, Tassilo Ulrich, Constantin Zimmerer, David Jaeger, Paul F. Franke, Joerg K. H. Kohl, Simon Isensee, Fabian Maier-Hein, Klaus H. German Canc Res Ctr DKFZ Heidelberg Div Med Image Comp Heidelberg Germany Helmholtz Informat & Data Sci Sch Hlth Karlsruhe Germany Helmholtz Informat & Data Sci Sch Hlth Heidelberg Germany DKFZ Helmholtz Imaging Heidelberg Germany DKFZ Natl Ctr Tumor Dis NCT NCT Heidelberg Heidelberg Germany DKFZ Interact Machine Learning Grp Heidelberg Germany DKFZ Appl Comp Vision Lab Heidelberg Germany Univ Freiburg Machine Learning Lab Freiburg Germany Latent Labs latentlabs com London England Univ Heidelberg Hosp Pattern Anal & Learning Grp Heidelberg Germany

ISBN: (纸本)9798350318920;9798350318937

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.

关键词： Algorithms Algorithms and algorithms Applications Biomedical / healthcare / medicine formulations Image recognition and understanding Machine learning architectures

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MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results

MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhu, Qingpeng Sun, Wenxiu Dai, Yuekun Li, Chongyi Zhou, Shangchen Feng, Ruicheng Sun, Qianhui Loy, Chen Change Gu, Jinwei Yu, Yi Huang, Yangke Zhang, Kang Chen, Meiya Wang, Yu Li, Yongchao Jiang, Hao Muduli, Amrit Kumar Kumar, Vikash Swami, Kunal Bajpai, Pankaj Kumar Ma, Yunchao Xiao, Jiaojun Ling, Zhi SenseTime Research and Tetras.AI Nanyang Technological University Singapore SenseBrain Mgtv China Xiaomi Inc. China Samsung R & D Institute India - Bangalore India

ISBN: (纸本)9798350302493

Depth completion from RGB images and sparse Time-of-Flight (ToF) measurements is an important problem in computer vision and robotics. While traditional methods for depth completion have relied on stereo vision or structured light techniques, recent advances in deep learning have enabled more accurate and efficient completion of depth maps from RGB images and sparse ToF measurements. To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition. The competition aimed to encourage research in this area by providing a standardized dataset and evaluation metrics to compare the accuracy of different approaches. In this report, we present the results of the competition and analyze the strengths and weaknesses of the top-performing methods. We also discuss the implications of our findings for future research in RGB+sparse ToF depth completion. We hope that this competition and report will help to advance the state-of-the-art in this important area of research. More details of this challenge and the link to the dataset can be found at https://***/MIPI2023/. © 2023 ieee.

关键词： Stereo vision

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Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results

Rip Current Segmentation: A Novel Benchmark and YOLOv8 Basel...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Dumitriu, Andrei Tatui, Florin Miron, Florin Ionescu, Radu Tudor Timofte, Radu CAIDAS IFI University of Würzburg Computer Vision Lab Germany University of Bucharest Romania

ISBN: (纸本)9798350302493

Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water currents. In this paper, we address a novel task: rip current instance segmentation. We introduce a comprehensive dataset containing 2,466 images with newly created polygonal annotations for instance segmentation, used for training and validation. Additionally, we present a novel dataset comprising 17 drone videos (comprising about 24K frames) captured at 30 FPS, annotated with both polygons for instance segmentation and bounding boxes for object detection, employed for testing purposes. We train various versions of YOLOv8 for instance segmentation on static images and assess their performance on the test dataset (videos). The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of 88.94% on the validation dataset and 81.21% macro average on the test dataset. The results provide a baseline for future research in rip current segmentation. Our work contributes to the existing literature by introducing a detailed, annotated dataset, and training a deep learning model for instance segmentation of rip currents. The code, training details and the annotated dataset are made publicly available at https://***/Irikos/rip-currents. © 2023 ieee.

关键词： Surface waters

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Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective

Investigating Catastrophic Overfitting in Fast Adversarial T...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： He, Zhengbao Li, Tao Chen, Sizhe Huang, Xiaolin Shanghai Jiao Tong University Department of Automation China

ISBN: (纸本)9798350302493

Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where multi-step robust accuracy suddenly collapses to zero. In this paper, we for the first time decouple single-step adversarial examples into data-information and self-information, which reveals an interesting phenomenon called "self-fitting". Self-fitting, i.e., the network learns the self-information embedded in single-step perturbations, naturally leads to the occurrence of CO. When self-fitting occurs, the network experiences an obvious "channel differentiation"phenomenon that some convolution channels accounting for recognizing self-information become dominant, while others for data-information are suppressed. In this way, the network can only recognize images with sufficient self-information and loses generalization ability to other types of data. Based on self-fitting, we provide new insights into the existing methods to mitigate CO and extend CO to multi-step adversarial training. Our findings reveal a self-learning mechanism in adversarial training and open up new perspectives for suppressing different kinds of information to mitigate CO. © 2023 ieee.

关键词： computer vision

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Stereo Cross Global Learnable Attention Module for Stereo Image Super-Resolution

Stereo Cross Global Learnable Attention Module for Stereo Im...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhou, Yuanbo Xue, Yuyang Deng, Wei Nie, Ruofeng Zhang, Jiajun Pu, Jiaqi Gao, Qinquan Lan, Junlin Tong, Tong Fuzhou University China University of Edinburgh United Kingdom Imperial Vision Technology

ISBN: (纸本)9798350302493

Stereo super-resolution is a technique that utilizes corresponding information from multiple viewpoints to enhance the texture of low-resolution images. In recent years, numerous impressive works have advocated attention mechanisms based on epipolar constraints to boost the performance of stereo super-resolution. However, techniques that exclusively depend on epipolar constraint attention are insufficient to recover realistic and natural textures for heavily corrupted low-resolution images. We noticed that global self-similarity features within the image and across the views can proficiently fix the texture details of low-resolution images that are severely damaged. Therefore, in the current paper, we propose a stereo cross global learnable attention module (SCGLAM), aiming to improve the performance of stereo super-resolution. The experimental outcomes show that our approach outperforms others when dealing with heavily damaged low-resolution images. The relevant code is made available on this link as open source. © 2023 ieee.

关键词： Textures

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An Empty Room is All We Want: Automatic Defurnishing of Indoor Panoramas

An Empty Room is All We Want: Automatic Defurnishing of Indo...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mira Slavcheva Dave Gausebeck Kevin Chen David Buchhofer Azwad Sabik Chen Ma Sachal Dhillon Olaf Brandt Alan Dolhasz Matterport

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a pipeline that leverages Stable Diffusion to improve inpainting results in the context of defurnishing—the removal of furniture items from indoor panorama images. Specifically, we illustrate how increased context, domain-specific model fine-tuning, and improved image blending can produce high-fidelity inpaints that are geometrically plausible without needing to rely on room layout estimation. We demonstrate qualitative and quantitative improvements over other furniture removal techniques.

关键词： computer vision conferences Layout Pipelines Estimation Robustness Reflection

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