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Towards Efficient Audio-Visual Learners via Empowering Pre-trained vision Transformers with Cross-Modal Adaptation

Towards Efficient Audio-Visual Learners via Empowering Pre-t...

引用

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

作者： Kai Wang Yapeng Tian Dimitrios Hatzinakos University of Toronto University of Texas Dallas

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we explore the cross-modal adaptation of pre-trained vision Transformers (ViTs) for the audio-visual domain by incorporating a limited set of trainable parameters. To this end, we propose a Spatial-Temporal-Global Cross-Modal Adaptation (STG-CMA) to gradually equip the frozen ViTs with the capability for learning audio-visual representation, consisting of the modality-specific temporal adaptation for temporal reasoning of each modality, the cross-modal spatial adaptation for refining the spatial information with the cue from counterpart modality, and the cross-modal global adaptation for global interaction between audio and visual modalities. Our STG-CMA presents a meaningful finding that only leveraging the shared pre-trained image model with inserted lightweight adapters is enough for spatial-temporal modeling and feature interaction of audio-visual modality. Extensive experiments indicate that our STG-CMA achieves state-of-the-art performance on various audio-visual understanding tasks including AVE, AVS, and AVQA while containing significantly reduced tunable parameters. The code is available at https://***/kaiw7/STG-CMA.

关键词： computer vision Adaptation models Visualization Codes conferences Computational modeling Refining

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Super-Resolution Appearance Transfer for 4D Human Performances

Super-Resolution Appearance Transfer for 4D Human Performanc...

引用

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

作者： Pesavento, Marco Volino, Marco Hilton, Adrian Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781665448994

A common problem in the 4D reconstruction of people from multi-view video is the quality of the captured dynamic texture appearance which depends on both the camera resolution and capture volume. Typically the requirement to frame cameras to capture the volume of a dynamic performance (> 50m(3)) results in the person occupying only a small proportion < 10% of the field of view. Even with ultra high-definition 4k video acquisition this results in sampling the person at less-than standard definition 0.5k video resolution resulting in low-quality rendering. In this paper we propose a solution to this problem through super-resolution appearance transfer from a static high-resolution appearance capture rig using digital stills cameras (> 8k) to capture the person in a small volume (< 8m(3)). A pipeline is proposed for super-resolution appearance transfer from high-resolution static capture to dynamic video performance capture to produce super-resolution dynamic textures. This addresses two key problems: colour mapping between different camera systems;and dynamic texture map super-resolution using a learnt model. Comparative evaluation demonstrates a significant qualitative and quantitative improvement in rendering the 4D performance capture with super-resolution dynamic texture appearance. The proposed approach reproduces the high-resolution detail of the static capture whilst maintaining the appearance dynamics of the captured video.

关键词： Surface reconstruction Image color analysis Superresolution Pipelines Cameras Rendering (computer graphics) Surface texture

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Adversarial Identity Injection for Semantic Face Image Synthesis

Adversarial Identity Injection for Semantic Face Image Synth...

引用

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

作者： Giuseppe Tarollo Tomaso Fontanini Claudio Ferrari Guido Borghi Andrea Prati Department of Engineering and Architecture University of Parma Parma Italy Department of Computer Science and Engineering University of Bologna Cesena Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Nowadays, deep learning models have reached incredible performance in the task of image generation. Plenty of literature works address the task of face generation and editing, with human and automatic systems that struggle to distinguish what’s real from generated. Whereas most systems reached excellent visual generation quality, they still face difficulties in preserving the identity of the starting input subject. Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern. Therefore, in this paper, we investigate the problem of identity preservation in face image generation and present an SIS architecture that exploits a cross-attention mechanism to merge identity, style, and semantic features to generate faces whose identities are as similar as possible to the input ones. Experimental results reveal that the proposed method is not only suitable for preserving the identity but is also effective in the face recognition adversarial attack, i.e. hiding a second identity in the generated faces.

关键词： Deep learning Visualization computer vision Image synthesis Face recognition Semantic segmentation conferences

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LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network

LCCNet: LiDAR and Camera Self-Calibration using Cost Volume ...

引用

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

作者： Lv, Xudong Wang, Boya Dou, Ziwen Ye, Dong Wang, Shuo Harbin Inst Technol Sch Instrumentat Sci & Engn Harbin Peoples R China

ISBN: (纸本)9781665448994

Multi-sensor fusion is for enhancing environment perception and 3D reconstruction in self-driving and robot navigation. Calibration between sensors is the precondition of effective multi-sensor fusion. Laborious manual works and complex environment settings exist in old-fashioned calibration techniques for Light Detection and Ranging (LiDAR) and camera. We propose an online LiDAR-Camera Self-calibration Network (LCCNet), different from the previous CNN-based methods. LCCNet can be trained end-to-end and predict the extrinsic parameters in real-time. In the LCCNet, we exploit the cost volume layer to express the correlation between the RGB image features and the depth image projected from point clouds. Besides using the smooth L1-Loss of the predicted extrinsic calibration parameters as a supervised signal, an additional self-supervised signal, point cloud distance loss, is applied during training. Instead of directly regressing the extrinsic parameters, we predict the decalibrated deviation from initial calibration to the ground truth. The calibration error decreases further with iterative refinement and the temporal filtering approach in the inference stage. The execution time of the calibration process is 24ms for each iteration on a single GPU. LCCNet achieves a mean absolute calibration error of 0.297cm in translation and 0.017. in rotation with miscalibration magnitudes of up to +/- 1.5m and +/- 20. on the KITTI-odometry dataset, which is better than the state-of-the-art CNN-based calibration methods. The code will be publicly available at https://***/LvXudong-HIT/LCCNet

关键词： Training Three-dimensional displays Laser radar Robot vision systems Sensor fusion Cameras Feature extraction

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Multi-modal Aerial View Image Challenge: SAR Classification

Multi-modal Aerial View Image Challenge: SAR Classification

引用

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

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH Ecuador Computer Vision Center ESPOL Polytechnic University Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This manuscript delineates the outcomes of the fourth Multi-modal Aerial View Image Challenge - Classification (MAVIC-C). The challenge is aimed at advancing the development of recognition models that leverage Synthetic Aperture Radar (SAR) and Electro-Optical (EO) imagery. Encouraging the integration of data from these two distinct modalities, the challenge seeks to foster the creation of multi-modal approaches that complement characteristics of SAR and EO information. Building upon the precedents set in previous years, the 2021 MAVOC challenge validated the potential of integrating SAR and EO modalities. The subsequent 2022 and 2023 challenges further explored the capabilities of multi-modal frameworks. In its latest iteration, the 2024 challenge presents an enhanced UNIfied COincident Optical and Radar for recognition (UNICORN) dataset alongside a revised competition format, focused on the task of SAR classification. The 2024 challenge evaluates model robustness through out-of-distribution measures, alongside traditional accuracy metrics. The core of this paper is devoted to analyzing the methodologies of the top-performing entries and their performance metrics on a blind test set.

关键词： Measurement Laser radar Image recognition Pipelines Optical imaging Robustness pattern recognition

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IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models

IDAdapter: Learning Mixed Features for Tuning-Free Personali...

引用

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

作者： Siying Cui Jia Guo Xiang An Jiankang Deng Yongle Zhao Xinyu Wei Ziyong Feng Peking University DeepGlint InsightFace

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character avatars based on their specific prompts. However, existing personalization methods face challenges, including test-time fine-tuning, the requirement of multiple input images, low preservation of identity, and limited diversity in generated outcomes. To overcome these challenges, we introduce IDAdapter, a tuning-free approach that enhances the diversity and identity preservation in personalized image generation from a single face image. IDAdapter integrates a personalized concept into the generation process through a combination of textual and visual injections and a face identity loss. During the training phase, we incorporate mixed features from multiple reference images of a specific identity to enrich identity-related content details, guiding the model to generate images with more diverse styles, expressions, and angles. Extensive evaluations demonstrate the effectiveness of our method, achieving both diversity and identity fidelity.

关键词： Training Visualization computer vision Image synthesis Face recognition Avatars conferences

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GESCAM : A Dataset and Method on Gaze Estimation for Classroom Attention Measurement

GESCAM : A Dataset and Method on Gaze Estimation for Classro...

引用

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

作者： Athul M. Mathew Arshad Ali Khan Thariq Khalid Riad Souissi Elm Company Saudi Arabia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Human gaze provides crucial insights into individual attention during social or educational interactions. Attention systems often rely on head and facial features to predict gaze direction, but reliable gaze target detection (GTD) requires rich contextual cues. These cues inform the system about an individual’s position within a scene and the surrounding objects they might be interacting with. Our paper proposes attention measurement using GTD in educational classrooms, leveraging a synthetic dataset called GESCAM (Gaze Estimation based Synthetic Classroom Attention Measurement). This dataset was meticulously generated using 3D modelling, animation, simulation, and rendering techniques comprising 60,000 images with 650,000 instances of individuals (students, teachers) engaged in various activities, including looking at blackboard, notebooks, mobile phones etc. Our novel network trained on GESCAM proficiently identifies gaze fixations within complex classroom scenes, offering insights into human attention in classrooms across diverse contexts.

关键词： Three-dimensional displays Head Estimation Object detection Rendering (computer graphics) Mobile handsets pattern recognition

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MaskSim: Detection of synthetic images by masked spectrum similarity analysis

MaskSim: Detection of synthetic images by masked spectrum si...

引用

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

作者： Yanhao Li Quentin Bammey Marina Gardella Tina Nikoukhah Jean-Michel Morel Miguel Colom Rafael Grompone Von Gioi Université Paris-Saclay ENS Paris-Saclay CNRS Centre Borelli France Instituto de Matemática Pura e Aplicada Rio de Janeiro Brazil Department of Mathematics City University of Hong Kong Kowloon Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic image generation methods have recently revolutionized the way in which visual content is created. This opens up creative opportunities but also presents challenges in preventing misinformation and crime. However, these methods leave traces in the Fourier spectrum that are invisible to humans, but can be detected by specialized tools. This paper describes a semi-white-box method for detecting synthetic images by revealing anomalous patterns in the spectral domain. Specifically, we train a mask to enhance the most discriminative frequencies and simultaneously train a reference pattern that resembles the patterns produced by a given generative method. The proposed method produces explainable results with state-of-the-art performances and highlights cues that can be used as forensic evidence. Code is available at https://***/li-yanhao/masksim.

关键词： Visualization computer vision Image coding Image synthesis Forensics conferences Transform coding

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Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration

Know Your Surroundings: Panoramic Multi-Object Tracking by M...

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

作者： He, Yuhang Yu, Wentao Han, Jie Wei, Xing Hong, Xiaopeng Gong, Yihong Xi An Jiao Tong Univ Coll Artificial Intelligence Xian Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian Peoples R China Peng Cheng Lab Res Ctr Artificial Intelligence Shenzhen Peoples R China

ISBN: (纸本)9781665448994

In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation. Most existing MOT methods track multiple objects using a singular RGB camera, which are prone to camera field-of-view and suffer tracking failures in complex scenarios due to background clutters and poor light conditions. To meet these challenges, we propose a MultiModality PAnoramic multi-object Tracking framework (MMPAT), which takes both 2D panorama images and 3D point clouds as input and then infers target trajectories using the multimodality data. The proposed method contains four major modules, a panorama image detection module, a multimodality data fusion module, a data association module and a trajectory inference model. We evaluate the proposed method on the JRDB dataset, where the MMPAT achieves the top performance in both the detection and tracking tasks and significantly outperforms state-of-the-art methods by a large margin (15.7 and 8.5 improvement in terms of AP and MOTA, respectively).

关键词： Three-dimensional displays Target tracking Navigation Robot vision systems Object detection Cameras Data models

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ieee computer society

IEEE Computer Society

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ieee Winter Applications and computer vision workshops (WACVW)

ieee computer society

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