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A Conic Transformation Approach for Solving the Perspective-Three-Point Problem

A Conic Transformation Approach for Solving the Perspective-...

引用

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

作者： Wu, Haidong Bhayani, Snehal Heikkilä, Janne University of Oulu Center for Machine Vision and Signal Analysis Oulu Finland

ISBN: (纸本)9798331510831

We propose a conic transformation method to solve the Perspective-Three-Point (P3P) problem. In contrast to the current state-of-the-art solvers, which formulate the P3P problem by intersecting two conics and constructing a de-generate conic to find the intersection, our approach builds upon a new formulation based on a transformation that maps the two conics to a new coordinate system, where one of the conics becomes a standard parabola in a canonical form. This enables expressing one variable in terms of the other variable, and as a consequence, substantially simpli-fies the problem of finding the conic intersection. Moreover, the polynomial coefficients are fast to compute, and we only need to determine the real-valued intersection points, which avoids the requirement of using computationally expensive complex arithmetic. While the current state-of-the-art methods reduce the conic intersection problem to solving a univariate cubic equation, our approach, despite resulting in a quartic equation, is still faster thanks to this new simplified formulation. Extensive evaluations demonstrate that our method achieves higher speed while maintaining robustness and stability comparable to state-of-the-art methods. © 2025 IEEE.

关键词： Polynomials

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CD-FSOD: A Benchmark For Cross-Domain Few-Shot Object Detection 48

CD-FSOD: A Benchmark For Cross-Domain Few-Shot Object Detect...

引用

48th IEEE International Conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Xiong, Wuti University of Oulu Center for Machine Vision and Signal Analysis Finland

ISBN: (纸本)9781728163277

In this paper, we propose a study of the cross-domain few-shot object detection (CD-FSOD) benchmark, consisting of image data from a diverse data domain. On the proposed benchmark, we evaluate state-of-art FSOD approaches, including meta-learning FSOD approaches and fine-tuning FSOD approaches. The results show that these methods tend to fall, and even underperform the naive fine-tuning model. We analyze the reasons for their failure and introduce a strong baseline that uses a mutually-beneficial manner to alleviate the overfitting problem. Our approach is remarkably superior to existing approaches by significant margins (2.0% on average) on the proposed benchmark. Our code is available at https://***/FSOD/CD-FSOD. © 2023 IEEE.

关键词： Object detection

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Attention-guided Boundary Refinement on Anchor-free Temporal Action Detection 23rd

Attention-guided Boundary Refinement on Anchor-free Tempora...

引用

22nd Scandinavian Conference on Image analysis, SCIA 2023

作者： Shi, Henglin Chen, Haoyu Zhao, Guoying Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland

ISBN: (纸本)9783031314346

Modelling temporal dependencies is important for accurate action detection. In this work, we develop a temporal attention unit to mine the global dependencies among features from different temporal locations. Additionally, based on the developed temporal attention unit, we propose an attention-guided boundary refinement module for revising action prediction results. Besides, we integrate the proposed module into a contemporary anchor-free detector for performing temporal action detection. To evaluate the proposed method, experiments are carried out on two large-scale temporal action detection datasets, namely THUMOS14 and ActivityNet1.3 datasets. Experimental results show that the action detection performance is significantly boosted by the proposed temporal attention module which outperforms several state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Evaluating Text Summarization Techniques and Factual Consistency with Language Models

Evaluating Text Summarization Techniques and Factual Consist...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Islam, Md Moinul Muhammad, Usman Oussalah, Mourad University of Oulu Center for Machine Vision and Signal Analysis Finland Aalto University Department of Computer Science Finland

ISBN: (纸本)9798350362480

Standard evaluation of automated text summarization (ATS) methods relies on manually crafted golden summaries. With the advances in Large Language Models (LLMs), it is legitimate to question whether these models can now potentially complement or replace human-crafted summaries. This study examines the effectiveness of several language models (LMs) in specifically addressing the issue of preserving factual consistency. By conducting a thorough assessment of various conventional and state-of-the-art performance metrics, such as ROUGE, BLEU, BERTScore, FActScore, and LongDocFACTScore across diverse datasets, our findings highlight the important relationship between linguistic eloquence and factual accuracy. The findings suggest that whereas LLMs, such as GPT and LLaMA, demonstrate considerable competence in producing concise and contextually-aware summaries, there remain difficulties in ensuring factual accuracy, particularly in domain-specific situations. Moreover, this work enhances the existing knowledge on summarization dynamics and highlights the need of developing more reliable and tailored evaluation techniques that minimize the probability of factual errors in text generated by ATS. In particular, the findings advance the current domain by providing a rigorous assessment of the balance between linguistic fluency and factual correct- ness, highlighting the limitations of current ATS frameworks and metrics to enhance the factual reliability of LM-generated summaries. © 2024 IEEE.

关键词： Linguistics

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Learning Binary-Antithetical Information Bottleneck for Generalizable Face Anti-Spoofing

Learning Binary-Antithetical Information Bottleneck for Gene...

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

作者： Yu, Hao Chen, Haoyu Zhao, Guoying Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Computer Science Aalto University Finland

ISBN: (纸本)9798350368741

We investigate generalizable face anti-spoofing (FAS) using information bottleneck theory. As generalizable FAS aims to detect spoofing in unseen scenarios, it has recently gained significant attention. Existing methods often use adversarial strategies or auxiliary modules to learn domain-invariant features by mining data relationships from distinct source domains. However, their learned feature space may still shift for unseen data due to the spurious correlations overfitted from training domains. Our rationale is that the problem of generalized pattern learning in FAS can be framed as a unified binary-antithetical information transition process, grounded in information bottleneck theory. Specifically, we leverage mutual-information optimization to preserve the instance-level spoof-aware information while compressing domain-related information modeled from the antithetical identity distribution. This enables the model to dynamically identify domain-agnostic, minimal sufficient representations that consistently describe the live/spoof distributions while mitigating spurious correlations through cross-identity compression. In light of this, we propose a novel learning framework for FAS, named Binary-Antithetical Information Bottleneck (BIB)-FAS, which is proven to be effectively generalized to unseen scenarios without using auxiliary information (e.g., domain labels) for training. Extensive cross-domain evaluations show that BIB-FAS significantly outperforms state-of-the-art methods. The code is available at: ***/CV-AC/BIB-FAS. © 2025 IEEE.

关键词： Binary-antithetical transition Domain generalization Face anti-spoofing Information bottleneck

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Demonstration of a Continuously Updated, Radio-Compatible Digital Twin for Robotic Integrated Sensing and Communications 5

Demonstration of a Continuously Updated, Radio-Compatible Di...

引用

5th IEEE International Symposium on Joint Communications and Sensing, JC and S 2025

作者： Andrei, Vlad C. Susarla, Praneeth Djuhera, Aladin Vaara, Niklas Mustaniemi, Janne Casado, Constantino Álvarez Li, Xinyang Monich, Ullrich J. Boche, Holger López, Miguel Bordallo Technical University of Munich Munich Germany 6G Flagship University of Oulu Center for Machine Vision and Signal Analysis Finland

ISBN: (纸本)9798331531652

Integrated sensing and communications (ISAC) is essential for future 6 G, bridging physical and digital worlds by enabling wireless systems to sense and respond to their environment. Digital twins (DTs) enhance ISAC by providing real-time, data-driven models for applications like localization and autonomous navigation. However, existing DT frameworks lack real-time modeling and multimodal sensing, limiting their use for high-resolution ISAC. This demo paper extends our recently proposed real-time DT framework for indoor ISACenabled robotics and introduces an LLM-driven speech interface for control and planning, a continuous 3D reconstruction scheme using RGB-D data, and a novel ray tracing approach for wireless channel modeling from point clouds. These innovations address key limitations, supporting advanced ISAC applications and immersive human-computer interaction. © 2025 IEEE.

关键词： Digital elevation model

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Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing 12

Deep Ensemble Learning with Frame Skipping for Face Anti-Spo...

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12th International Conference on Image Processing Theory, Tools and Applications, IPTA 2023

作者： Muhammad, Usman Hoque, Md Ziaul Oussalah, Mourad Laaksonen, Jorma University of Oulu Center for Machine Vision and Signal Analysis Finland Aalto University Department of Computer Science Finland

ISBN: (纸本)9798350325416

Face presentation attacks, also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. In particular, the proposed frame skipping adopts a uniform sampling approach by dividing the original video into video clips of fixed size. By doing so, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of individual RNNs, a meta-model is developed to improve the overall detection performance by combining the prediction of individual RNNs. Extensive experiments were performed on four datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%), Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total error rates (HTERs) in the most challenging cross-dataset testing scenario. © 2023 IEEE.

关键词： Face recognition

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Exploring Facial Kinship Verification through Contactless Heart Activity analysis

Exploring Facial Kinship Verification through Contactless He...

引用

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

作者： Wu, Xiaoting Feng, Xiaoyi Casado, Constantino Álvarez Liu, Lili López, Miguel Bordallo Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Facial Kinship Verification (FKV) aims at automatically determining whether two subjects have a kinship relation based on human faces. It has potential applications in finding missing children and social media analysis. Traditional FKV faces challenges as it is vulnerable to spoof attacks and raises privacy issues. In this paper, we explore for the first time the FKV by analyzing cardiac activity through physiological signals, with a specific focus on remote Photoplethysmography (rPPG). rPPG signals are extracted from facial videos, resulting in a one-dimensional signal that measures the changes in visible light reflection emitted to and detected from the skin caused by the heartbeat. Specifically, in this paper, we employed a straightforward one-dimensional Convolutional Neural Network (1DCNN) with a 1DCNN-Attention module and kinship contrastive loss to learn the kin similarity from rPPGs. The network takes multiple rPPG signals extracted from various facial Regions of Interest (ROIs) as inputs. Additionally, the 1DCNN attention module is designed to learn and capture the discriminative kin features from feature embeddings. Finally, we demonstrate the feasibility of rPPG to detect kinship with the experiment evaluation on the UvANEMO Smile Database from different kin relations. © 2025 IEEE.

关键词： 1DCNN Biosignals Channel attention Kinship verification Remote Photoplethysmography

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LOOKING BACK ON LEARNED EXPERIENCES FOR CLASS/TASK INCREMENTAL LEARNING 10

LOOKING BACK ON LEARNED EXPERIENCES FOR CLASS/TASK INCREMENT...

引用

10th International Conference on Learning Representations, ICLR 2022

作者： PourKeshavarz, Mozhgan Zhao, Guoying Sabokrou, Mohammad Iran Center for Machine Vision and Signal Analysis University of Oulu Finland

Classical deep neural networks are limited in their ability to learn from emerging streams of training data. When trained sequentially on new or evolving tasks, their performance degrades sharply, making them inappropriate in real-world use cases. Existing methods tackle it by either storing old data samples or only updating a parameter set of deep neural networks, which, however, demands a large memory budget or spoils the flexibility of models to learn the incremented task distribution. In this paper, we shed light on an on-call transfer set to provide past experiences whenever a new task arises in the data stream. In particular, we propose a Cost-Free Incremental Learning (CF-IL) not only to replay past experiences the model has learned but also to perform this in a cost free manner. Towards this end, we introduced a memory recovery paradigm in which we query the network to synthesize past exemplars whenever a new task emerges. Thus, our method needs no extra memory for data buffering or network growing, besides calls the proposed memory recovery paradigm to provide past exemplars, named a transfer set in order to mitigate catastrophically forgetting the former tasks in the Incremental Learning (IL) setup. Moreover, in contrast with recently proposed methods, the suggested paradigm does not desire a parallel architecture since it only relies on the learner network. Compared to the state-of-the-art data techniques without buffering past data samples, CF-IL demonstrates significantly better performance on the well-known datasets whether a task oracle is available in test time (Task-IL) or not (Class-IL). © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Deep neural networks

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Exemplar-Free Incremental Deepfake Detection 27

Exemplar-Free Incremental Deepfake Detection

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xiong, Wuti Zhao, Guoying Li, Xiaobai Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9781643685489

Incremental Deepfake Detection (IDD) aims to continuously update models with new domain data, adapting to evolving forgery techniques. Existing works require extra buffers to store old exemplars for maintaining previously learned knowledge. However, it is infeasible when previous data is unavailable due to storage and privacy issues. This paper focuses on a more challenging but practical exemplar-free IDD problem that requests zero old exemplars when updating the model. To address this problem, we design a domain-adaptive module that uses independent adapters to learn domain-specific knowledge for each domain, avoiding using old exemplars. Besides, we introduce an uncertainty optimization strategy to optimize the adapters more efficiently. With excellent scalability, our method can be easily deployed to various models. To simulate the practical scenarios, we designed two new protocols based on diverse deepfake datasets. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods by a large margin. The code is available at https://***/woody-panda/EF-IDD. © 2024 The Authors.

关键词： Differential privacy

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