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32st Color and Imaging Conference - Color Science and Engineering Systems, Technologies, and Applications, CIC 2024

作者： Giroux, Justine Dastjerdi, Mohammad Reza Karimi Hold-Geoffroy, Yannick Vazquez-Corral, Javier Lalonde, Jean-Francois Universite Laval Canada Adobe Research United States Computer Vision Center Spain Universitat Autonoma de Barcelona Spain

ISBN: (纸本)9780892083688

Lighting is a crucial component of an image, especially during the task of virtual object insertion in a photograph, e.g. for AR/VR/MR applications. The human brain is quite attuned to changes in lighting for a given object. Thus, it is imperative to estimate the scene's lighting in order to produce a realistic image. However, this task remains complex, as disentangling lighting from the 3D geometry and the material properties of the objects in the image is an ill-posed problem. For this reason, the community has been developing lighting estimation methods for over the past decades, by using handcrafted priors and more recently leveraging the power of deep learning. The great variety of lighting estimation methods currently available must be compared to each order in order to quantify their progress with regard to their accuracy and realism of their estimations. For this task, it is standard to use popular image quality assessment (IQA) metrics to compare a rendered virtual object, lit using the predicted lighting from different methods, compared to the ground truth render. However, standard IQA metrics are not designed to quantify differences in lighting, since they are usually developed for other specific tasks (such as noise perception for compression). Thus, it is unclear if standard IQA metrics are appropriate to use when judging the perceptual quality of renders generated with estimated lighting. In this work, we evaluate whether IQA metrics and human perception align. To do so, we perform a calibrated user study, which allows us to compare the preferences of humans with standard IQA metrics. We demonstrate that they are not in agreement;hence we propose our new IQA metric for lighting estimation, which is in agreement with the perceptual data. Our new perceptual IQA metric shows great generalisation to other lighting estimation methods not included in our dataset, meaning that it will be helpful for the development of new lighting estimation methods. To encou

关键词： Virtual reality

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AIM: ADAPTING IMAGE MODELS FOR EFFICIENT VIDEO ACTION RECOGNITION 11

AIM: ADAPTING IMAGE MODELS FOR EFFICIENT VIDEO ACTION RECOGN...

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11th International Conference on Learning Representations, ICLR 2023

作者： Yang, Taojiannan Zhu, Yi Xie, Yusheng Zhang, Aston Chen, Chen Li, Mu Center for Research in Computer Vision University of Central Florida United States Amazon Web Services United States

Recent vision transformer based video models mostly follow the "image pretraining then finetuning" paradigm and have achieved great success on multiple video benchmarks. However, fully finetuning such a video model could be computationally expensive and unnecessary, given the pre-trained image transformer models have demonstrated exceptional transferability. In this work, we propose a novel method to Adapt pre-trained Image Models (AIM) for efficient video understanding. By freezing the pre-trained image model and adding a few lightweight Adapters, we introduce spatial adaptation, temporal adaptation and joint adaptation to gradually equip an image model with spatiotemporal reasoning capability. We show that our proposed AIM can achieve competitive or even better performance than prior arts with substantially fewer tunable parameters on four video action recognition benchmarks. Thanks to its simplicity, our method is also generally applicable to different image pre-trained models, which has the potential to leverage more powerful image foundation models in the future. The project webpage is https://***/. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Benchmarking

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Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning

Exploring Parameter-Efficient Fine-Tuning to Enable Foundati...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Sun, Guangyu Khalid, Umar Mendieta, Matias Wang, Pu Chen, Chen University of Central Florida Center for Research in Computer Vision OrlandoFL United States University of North Carolina at Charlotte Department of Computer Science CharlotteNC United States

ISBN: (纸本)9798350362480

Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the typical FL paradigm (e.g., FedAvg), model weights are sent to and from the server each round to participating clients. Recently, the use of small pre-trained models has been shown to be effective in federated learning optimization and improving convergence. However, recent state-of-the-art pre-trained models are getting more capable but also have more parameters, known as the "Foundation Models."In conventional FL, sharing the enormous model weights can quickly put a massive communication burden on the system, especially if more capable models are employed. Can we find a solution to enable those strong and readily available pre-trained models in FL to achieve excellent performance while simultaneously reducing the communication burden? To this end, we investigate the use of parameter-efficient fine-tuning in federated learning and thus introduce a new framework: FedPEFT. Specifically, we systemically evaluate the performance of FedPEFT across a variety of client stability, data distribution, and differential privacy settings. By only locally tuning and globally sharing a small portion of the model weights, significant reductions in the total communication overhead can be achieved while maintaining competitive or even better performance in a wide range of federated learning scenarios, providing insight into a new paradigm for practical and effective federated systems. © 2024 IEEE.

关键词： Federated learning

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Scientific Documents Recommendation Based on Graph Convolutional Network

Scientific Documents Recommendation Based on Graph Convolut...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st International Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Li, Jianmin School of Cyber Security and Computer Hebei University Baoding071002 China Hebei Machine Vision Engineering Research Center Hebei University Baoding071002 China

ISBN: (纸本)9789819614820

The rapid growth in the number of scientific documents makes it increasingly necessary to provide researchers with efficient paper recommendation services. Since scientific documents contain many mathematical expressions and rich text information, effectively mining this information to improve recommendation quality is a challenging and necessary task. Therefore, this paper proposes a method that fuses content-based and Graph Convolutional Network (GCN) introducing the multi-head attention mechanism for scientific document recommendation. The method employs Hesitant Fuzzy Set (HFS) and Bidirectional Encoder Representations from Transformers (BERT) to calculate the similarity between mathematical expressions and their context, respectively, and utilizes GCN to learn the complex relationships between nodes and mine the potential similarity between nodes. Then, the multi-head attention mechanism is introduced to dynamically assign importance weights to neighbor nodes to enhance the expression of embedding vectors, thereby generating more accurate and personalized recommendation lists. Experimental results show that the method performs well on both Ntcir and extended Chinese datasets, achieving maximum F1_Score vales of 0.817 and 0.837 at Top-50 and Top-30, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-Id

DLCR: A Generative Data Expansion Framework via Diffusion fo...

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2025 IEEE/CVF Winter Conference on Applications of computer vision, WACV 2025

作者： Siddiqui, Nyle Croitoru, Florinel Alin Nayak, Gaurav Kumar Ionescu, Radu Tudor Shah, Mubarak University of Central Florida Center for Research in Computer Vision United States University of Bucharest Romania Indian Institute of Technology Roorkee India

ISBN: (纸本)9798331510831

With the recent exhibited strength of generative diffusion models, an open research question is if images generated by these models can be used to learn better visual representations. While this generative data expansion may suffice for easier visual tasks, we explore its efficacy on a more difficult discriminative task: clothes-changing person re-identification (CC-ReID). CC-ReID aims to match people appearing in non-overlapping cameras, even when they change their clothes across cameras. Not only that current CC-ReID models are constrained by the limited diversity of clothing in current CC-ReID datasets, but generating additional data that retains important personal features for accurate identification is a current challenge. To address this issue we propose DLCR, a novel data expansion frame-work that leverages pretrained diffusion and large language models (LLMs) to accurately generate diverse images of in-dividuals in varied attire. We generate additional data for five benchmark CC-ReID datasets (PRCC, CCVID, LaST, VC-Clothes, and LTCC) and increase their clothing diversity by 10 x, totaling over 2.1M generated images. DLCR employs diffusion-based text-guided inpainting, conditioned on clothing prompts constructed using LLMs, to generate synthetic data that only modifies a subject's clothes, while preserving their personally identifiable features. With this massive increase in data, we introduce two novel strategies - progressive learning and test-time prediction refinement - that reduce training time and boost CC-ReID performance. We validate our method through extensive ablations and experiments, showing massive improvements when training previous CC-ReID methods on our generated data. On the PRCC dataset, we obtain a large top-1 accuracy improvement of 11.3% by training CAL, a state-of-the-art (SOTA) method, with DLCR-generated data. We publicly release our code and generated data for each dataset here: https://***/CroitoruAlin/dlcr. © 2025 IEEE.

关键词： Interdiffusion (solids)

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MSRFormer: Hybrid Scale Self-Attention and Local Fast Convolution Transformer for Facial Expression Recognition

MSRFormer: Hybrid Scale Self-Attention and Local Fast Convol...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shen, Zi-Qiang Tang, Yu-Yi Yan, Jun-Feng Li, Yang Zhao, Guo-Ying Hunan University of Chinese Medicine China Hunan Digital Chinese Medicine Engineering Technology Research Center China Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Finland

ISBN: (纸本)9798350368741

With the emergence of the Transformer architecture, the accuracy of deep learning within the domain of facial emotion recognition has seen further enhancement. However, Transformer comes with increased training complexity and time due to the large parameter count. Additionally, the global receptive field in Transformer's attention leads to unnecessary computations for features with limited spatial extent in image sentiment analysis. In this paper we presents a MSRFormer model, which combines Hybrid-scale self-attention and local fast convolution to address existing issues. The Hybrid-scale self-attention enables precise focus on salient regions of the image, where key features are located. A Local Fast Convolution module was integrated into the MLP head of the Transformer model, enhancing training speed and reducing parameters while maintaining feature learning. we conducted experiments on FERPlus, RAF-DB, and KDEF datasets and confirming its strong performance and broad applicability in facial expression recognition. © 2025 IEEE.

关键词： facial expression recognition hybrid scale self attention local fast convolution transformer

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Diffusion-based Identity-Preserving Facial Privacy Protection

Diffusion-based Identity-Preserving Facial Privacy Protectio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Han, Dong Mohamed, Salaheldin Li, Yong Denzler, Joachim Huawei Munich Research Center Friedrich Schiller University Jena Jena Germany Huawei Munich Research Center Munich Germany Computer Vision Group Friedrich Schiller University Jena Jena Germany

ISBN: (纸本)9798350368741

The efficacy of facial recognition systems that utilize deep learning techniques has led to significant concerns over privacy, since they possess the capability to facilitate unauthorized monitoring of individuals in the digital realm. Current techniques for improving privacy are ineffective in producing "naturalistic" photographs that can safeguard facial features and fail to ensure privacy while maintaining an optimal user experience. We present an innovative text-agnostic method for protecting facial privacy. Our method depends on manipulating the sampling process of a pretrained diffusion model utilizing the guidance from a target image face together with the original image and face guidance in an adversarial manner to produce a protected face image. We preserve the original visual information from the input face image for identity preservation while extracting general embedding information from the target face image for soft facial attribute transfer. The output protected face image from our method has imperceptible facial changes with enhanced privacy protection against state-of-the-art (SOTA) face recognition (FR) systems. Our extensive studies have shown that the faces generated using our method have a higher level of black-box adaptability, resulting in an absolute improvement of 6.4% on CelebA-HQ compared to the current most effective SOTA facial privacy protection technique in the face verification task while maintaining high image fidelity. © 2025 IEEE.

关键词： Diffusion Face Recognition Facial Privacy

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A Semiautomatic Image Processing-Based Method for Binary Segmentation of Lungs in Computed Tomography Images

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SN computer Science 2024年第6期5卷 689页

作者： Ramos, Leo Pineda, Israel School of Mathematical and Computational Sciences Yachay Tech University Deep Learning for Autonomous Driving Robotics and Computer Vision (DeepARC) Research Group Yachay Tech University Kauel Inc. Computer Vision Center Universitat Autònoma de Barcelona College of Sciences and Engineering Universidad San Francisco de Quito

Precise biomedical image segmentation is pivotal in medical diagnosis and treatment. Among various methodologies, image processing-based techniques are useful due to their swift and consistent results, free from the dependency on extensive training data required by deep learning approaches. This study introduces a tri-phase, semi-automated pipeline for lung computed tomography image segmentation, beginning with a preprocessing phase that includes contrast enhancement, noise reduction, and edge detection to improve image quality. This is followed by the application of the region growing method for segmentation and is completed with morphological closing to refine the final output. Comparative tests reveal that our technique surpasses prevalent methods like split and merge, Otsu’s technique, and basic region growing in terms of the dice coefficient (DC) and intersection over union (IoU). Specifically, our approach achieved an average DC of 0.9633, contrasting with 0.4668, 0.005, and 0.4067 recorded by Otsu’s, split and merge, and basic region growing techniques respectively. Similarly, the average IoU for our method was 0.9341, in comparison to 0.3105, 0.0660, and 0.2605 for the aforementioned methods. Additionally, comparative analysis with contemporary segmentation models demonstrates that our approach is competitive, even without relying on large training datasets. These results highlight our method’s efficiency and practicality, distinguishing it as a compelling option in medical scenarios where access to extensive training data is limited. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image analysis Image processing Image segmentation Medical image Pattern recognition Region growing

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Burst Image Super-Resolution with Mamba

arXiv

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arXiv 2025年

作者： Unal, Ozan Marty, Steven Dai, Dengxin Computer Vision Lab Huawei Research Center Zurich Switzerland

Burst image super-resolution (BISR) aims to enhance the resolution of a keyframe by leveraging information from multiple low-resolution images captured in quick succession. In the deep learning era, BISR methods have evolved from fully convolutional networks to transformer-based architectures, which, despite their effectiveness, suffer from the quadratic complexity of self-attention. We see Mamba as the next natural step in the evolution of this field, offering a comparable global receptive field and selective information routing with only linear time complexity. In this work, we introduce BurstMamba, a Mamba-based architecture for BISR. Our approach decouples the task into two specialized branches: a spatial module for keyframe super-resolution and a temporal module for subpixel prior extraction, striking a balance between computational efficiency and burst information integration. To further enhance burst processing with Mamba, we propose two novel strategies: (i) optical flow-based serialization, which aligns burst sequences only during state updates to preserve subpixel details, and (ii) a wavelet-based reparameterization of the state-space update rules, prioritizing high-frequency features for improved burst-to-keyframe information passing. Our framework achieves SOTA performance on public benchmarks of SyntheticSR, RealBSR-RGB, and RealBSR-RAW. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical flows

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Activity-Biometrics: Person Identification from Daily Activities

Activity-Biometrics: Person Identification from Daily Activi...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Shehreen Azad Yogesh Singh Rawat Center for Research in Computer Vision University of Central Florida

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this work, we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of ap-pearance biases such as clothing color and background. We propose ABNet, a novel framework which leverages dis-entanglement of biometric and non-biometric features to perform effective person identification from daily activities. ABNet relies on a bias-less teacher to learn biometric features from RGB videos and explicitly disentangle non-biometric features with the help of biometric distortion. In addition, ABNet also exploits activity prior for biometrics which is enabled by joint biometric and activity learning. We perform comprehensive evaluation of the proposed approach across five different datasets which are derived from existing activity recognition benchmarks. Furthermore, we extensively compare ABNet with existing works in person identification and demonstrate its effectiveness for activity-based biometrics across all five datasets. The code and dataset can be accessed at: https: //***/ sacrcv/Activity-Biometrics/

关键词： Biometrics computer vision Accuracy Biological system modeling Benchmark testing Activity recognition Distortion

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