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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

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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A Temporal Encoder-Decoder Approach to Extracting Blood Volume Pulse Signal Morphology from Face Videos

A Temporal Encoder-Decoder Approach to Extracting Blood Volu...

引用

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

作者： Li, Fulan Thapa, Surendrabikram Bhat, Shreyas Sarkar, Abhijit Abbott, A. Lynn Virginia Tech Bradley Department of Electrical and Computer Engineering United States Virginia Tech Department of Computer Science United States Virginia Tech Transportation Institute United States

ISBN: (纸本)9798350302493

This paper considers methods for extracting blood volume pulse (BVP) representations from video of the human face. Whereas most previous systems have been concerned with estimating vital signs such as average heart rate, this paper addresses the more difficult problem of recovering BVP signal morphology. We present a new approach that is inspired by temporal encoder-decoder architectures that have been used for audio signal separation. As input, this system accepts a temporal sequence of RGB (red, green, blue) values that have been spatially averaged over a small portion of the face. The output of the system is a temporal sequence that approximates a BVP signal. In order to reduce noise in the recovered signal, a separate processing step extracts individual pulses and performs normalization and outlier removal. After these steps, individual pulse shapes have been extracted that are sufficiently distinct to support biometric authentication. Our findings demonstrate the effectiveness of our approach in extracting BVP signal morphology from facial videos, which presents exciting opportunities for further research in this area. The source code is available at https://***/Adleof/CVPM-2023-Temporal-Encoder-Decoder-iPPG. © 2023 IEEE.

关键词： Morphology

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Self-Supervised Video Similarity Learning

Self-Supervised Video Similarity Learning

引用

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

作者： Kordopatis-Zilos, Giorgos Tolias, Giorgos Tzelepis, Christos Kompatsiaris, Ioannis Patras, Ioannis Papadopoulos, Symeon Czech Technical University VRG FEE Prague Czech Republic Queen Mary University London United Kingdom Information Technologies Institute CERTH Greece

ISBN: (纸本)9798350302493

We introduce S2VS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to have strong transferability on target tasks after fine-tuning. Here, in contrast to prior work, SSL is used to perform video similarity learning and address multiple retrieval and detection tasks at once with no use of labeled data. This is achieved by learning via instance-discrimination with task-tailored augmentations and the widely used InfoNCE loss together with an additional loss operating jointly on self-similarity and hard-negative similarity. We benchmark our method on tasks where video relevance is defined with varying granularity, ranging from video copies to videos depicting the same incident or event. We learn a single universal model that achieves state-of-the-art performance on all tasks, surpassing previously proposed methods that use labeled data. The code and pretrained models are publicly available at: https://***/gkordo/s2vs © 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 ...

引用

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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Learning Multi-scale Representations with Single-stream Network for Video Retrieval

Learning Multi-scale Representations with Single-stream Netw...

引用

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

作者： Wang, Chia-Hui Tseng, Yu-Chee Chiang, Ting-Hui Chen, Yan-Ann National Yang Ming Chiao Tung University Department of Computer Science Taiwan National Yang Ming Chiao Tung University College of AI Taiwan Chunghwa Telecom Laboratories Advanced Technology Laboratory Taoyuan Taiwan Yuan Ze University Department of Computer Science and Engineering Taiwan

ISBN: (纸本)9798350302493

With the explosive growth of video contents in the Internet, video retrieval has become an important issue that can benefit video recommendation and copyright detection. Since the key features of a video may distribute in distant regions of a lengthy video, several works have made a success by exploiting multi-stream, multi-scale architectures to learn and merge distant features. However, a multi-stream network is costly in terms of memory and computing overhead. The number of scales and these scales are handcrafted and fixed once a model is finalized. Further, being more complicated, multi-stream networks are more prone to being overfitting and lead to poorer generalization. This paper proposes a single-stream network with built-in dilated spatial and temporal learning capability. By combining with modern techniques, including Denoising Autoencoder, Squeeze-and-Excitation Attention, and Triplet Comparative Mechanism, our model achieves state-of-the-art performance in several video retrieval tasks on the FIVR200K, CC WEB VIDEO, and EVVE datasets. © 2023 IEEE.

关键词： Excited states

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Towards Real-Time 4K Image Super-Resolution

Towards Real-Time 4K Image Super-Resolution

引用

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

作者： Zamfir, Eduard Conde, Marcos V. Timofte, Radu University of Würzburg Computer Vision Lab Caidas Ifi Germany

ISBN: (纸本)9798350302493

Over the past few years, high-definition videos and images in 720p (HD), 1080p (FHD), and 4K (UHD) resolution have become standard. While higher resolutions offer improved visual quality for users, they pose a significant challenge for super-resolution networks to achieve real-time performance on commercial GPUs. This paper presents a comprehensive analysis of super-resolution model designs and techniques aimed at efficiently upscaling images from 720p and 1080p resolutions to 4K. We begin with a simple, effective baseline architecture and gradually modify its design by focusing on extracting important high-frequency details efficiently. This allows us to subsequently downscale the resolution of deep feature maps, reducing the overall computational footprint, while maintaining high reconstruction fidelity. We enhance our method by incorporating pixel-unshuffling, a simplified and speed-up reinterpretation of the basic block proposed by NAFNet, along with structural re-parameterization. We assess the performance of the fastest version of our method in the new NTIRE 2023 Real-Time 4K Super-Resolution challenge and demonstrate its potential in comparison with state-of-the-art efficient super-resolution models when scaled up. Our method was tested successfully on high-quality content from photography, digital art, and gaming content. © 2023 IEEE.

关键词： Optical resolving power

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Just a Glimpse: Rethinking Temporal Information for Video Continual Learning

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

引用

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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Win-Fail Action recognition

Win-Fail Action Recognition

引用

22nd IEEE/CVF Winter conference on Applications of computer vision (WACV)

作者： Parmar, Paritosh Morris, Brendan Univ British Columbia Vancouver BC Canada Univ Nevada Las Vegas NV 89154 USA

ISBN: (纸本)9781665458245

Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understand the actions. To spur progress in the direction of a more comprehensive understanding of videos, we introduce the task of win-fail action recognition - differentiating between successful and failed attempts at various activities. We introduce a first of its kind paired win-fail action understanding dataset with samples from the following domains: "General Stunts," "Internet Wins-Fails," "Trick Shots," & "Party Games." Unlike existing action recognition datasets, intra-class variation is high making the task challenging, yet feasible. Using a battery of experiments, including a novel video retrieval test, we systematically analyze the characteristics of our win-fail task/dataset, and determine its suitability to serve as a video understanding problem benchmark. While current prototypical action recognition methods work well on our task/dataset, they still leave a large gap to achieve high performance. We hope to motivate more work towards the true understanding of actions/videos. Dataset will be available from: https://***/ParitoshParmar/Win-Fail-Action-recognition.

关键词： Analytical models Pediatrics Limiting Benchmark testing Spatial databases Spatiotemporal phenomena pattern recognition

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Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results

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

引用

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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Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

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

作者： Liu, Ajian Tan, Zichang Liang, Yanyan Wan, Jun Institute of Automation Chinese Academy of Sciences Mais Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350302493

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack pattern Generation (Adv-APG) module and a Supervised learning-based Attack pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets. © 2023 IEEE.

关键词： Face recognition

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