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Recurrence-Based Vanishing Point Detection

Recurrence-Based Vanishing Point Detection

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Skanda Bharadwaj Robert T. Collins Yanxi Liu School of EECS The Pennsylvania State University University Park PA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Classical approaches to Vanishing Point Detection (VPD) rely solely on the presence of explicit straight lines in images, while recent supervised deep learning approaches need labeled datasets for training. We propose an alternative unsupervised approach: Recurrence-based Vanishing Point Detection (R-VPD) that uses implicit lines discovered from recurring correspondences in addition to explicit lines. Furthermore, we contribute two Recurring-pattern-for-Vanishing-Point (RPVP) datasets: 1) a Synthetic Image dataset with 3,200 ground truth vanishing points and camera parameters, and 2) a Real-World Image dataset with 1,400 human annotated vanishing points. We compare our method with two classical methods and two state-of-the-art deep learning-based VPD methods. We demonstrate that our unsupervised approach outperforms all the methods on the synthetic images dataset, outperforms the classical methods, and is on par with the supervised learning approaches on real-world images. Code and data can be found here: http://***/data/***

关键词： Deep learning Training computer vision Codes Urban areas Supervised learning Cameras Feature extraction Detection algorithms Unsupervised learning

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CLFace: A Scalable and Resource-Efficient Continual Learning Framework for Lifelong Face recognition

CLFace: A Scalable and Resource-Efficient Continual Learning...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Md Mahedi Hasan Shoaib Meraj Sami Nasser Nasrabadi West Virginia University Morgantown West Virginia USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

An important aspect of deploying face recognition (FR) algorithms in real-world applications is their ability to learn new face identities from a continuous data stream. However, the online training of existing deep neural network-based FR algorithms, which are pre-trained offline on large-scale stationary datasets, encounter two major challenges: (I) catastrophic forgetting of previously learned identities, and (II) the need to store past data for complete retraining from scratch, leading to significant storage constraints and privacy concerns. In this paper, we introduce CLFace, a continual learning framework designed to preserve and incrementally extend the learned knowledge. CLFace eliminates the classification layer, resulting in a resource-efficient FR model that remains fixed throughout lifelong learning and provides label-free supervision to a student model, making it suitable for open-set face recog-nition during incremental steps. We introduce an objective function that employs feature-level distillation to reduce drift between feature maps of the student and teacher models across multiple stages. Additionally, it incorpo-rates a geometry-preserving distillation scheme to maintain the orientation of the teacher model's feature embedding. Furthermore, a contrastive knowledge distillation is incor-porated to continually enhance the discriminative power of the feature representation by matching similarities between new identities. Experiments on several benchmark FR datasets demonstrate that CLFace outperforms baseline approaches and state-of-the-art methods on unseen identities using both in-domain and out-of-domain datasets.

关键词： Training Continuing education Data privacy computer vision Face recognition Benchmark testing Linear programming

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MADation: Face Morphing Attack Detection with Foundation Models

MADation: Face Morphing Attack Detection with Foundation Mod...

引用

ieee Winter Applications and computer vision Workshops (WACVW)

作者： Eduarda Caldeira Guray Ozgur Tahar Chettaoui Marija Ivanovska Peter Peer Fadi Boutros Vitomir Struc Naser Damer Fraunhofer IGD Germany University of Ljubljana Slovenia TU Darmstadt Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to create efficient ways to attack them, posing a threat to their secure deployment. Morphing attack detection (MAD) systems aim to detect a specific type of threat, morphing attacks, at an early stage, preventing them from being considered for verification in critical processes. Foundation models (FM) learn from extensive amounts of unlabelled data, achieving remarkable zero-shot generalization to unseen domains. Although this generalization capacity might be weak when dealing with domain-specific downstream tasks such as MAD, FMs can easily adapt to these settings while retaining the built-in knowledge acquired during pretraining. In this work, we recognize the potential of FMs to perform well in the MAD task when properly adapted to its specificities. To this end, we adapt FM CLIP architectures with LoRA weights while simultaneously training a classification header. The proposed framework, MADation surpasses our alternative FM and transformer-based frameworks and constitutes the first adaption of FMs to the MAD task. MADation presents competitive results with current MAD solutions in the literature and even surpasses them in several evaluation scenarios. To encourage reproducibility and facilitate further research in MAD, we publicly release the implementation of MADation at https://***/gurayozgur/MADation.

关键词： Training computer vision Frequency modulation Foundation models Face recognition conferences Training data Benchmark testing Transformers Reproducibility of results

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Learning to Count from Pseudo-Labeled Segmentation

Learning to Count from Pseudo-Labeled Segmentation

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Jingyi Xu Hieu Le Dimitris Samaras Stony Brook University EPFL

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing, based on only a few annotated exemplars. However, existing methods often count all objects in the image, including those from different categories than the exemplars. To address this issue, we propose localizing the area containing the objects of interest via an exemplar-based segmentation model before counting them. To train this model, we propose a novel method to obtain pseudo-labeled segmentation masks. Specifically, we use an unsupervised image clustering method to generate a set of candidate pseudo object masks, from which we select the optimal one using a pretrained CAC model. We show that the trained segmentation model can effectively localize objects of interest based on the exemplars and prevent the model from counting everything. To properly evaluate the performance of CAC methods in real-world scenarios, we introduce two new benchmarks: a synthetic test set and a new test set of real images containing countable objects from multiple classes. Our proposed method shows a significant advantage over previous CAC methods on these two benchmarks.

关键词： Training Image segmentation computer vision Annotations Computational modeling Clustering methods Benchmark testing Data models Object recognition Synthetic data

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Enhancing vision-Language Few-Shot Adaptation with Negative Learning

Enhancing Vision-Language Few-Shot Adaptation with Negative ...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Ce Zhang Simon Stepputtis Katia Sycara Yaqi Xie School of Computer Science Carnegie Mellon University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Large-scale pre-trained vision-Language Models (VLMs) have exhibited impressive zero-shot performance and transferability, allowing them to adapt to downstream tasks in a data-efficient manner. However, when only a few labeled samples are available, adapting VLMs to distinguish subtle differences between similar classes in specific downstream tasks remains challenging. In this work, we propose a Simple yet effective Negative Learning approach, SimNL, to more efficiently exploit the task-specific knowledge from few-shot labeled samples. Unlike previous methods that focus on identifying a set of representative positive features defining “what is a {CLASS} ”, SimNL discovers a complementary set of negative features that define “what is not a {CLASS}”, providing additional insights that supplement the positive features to enhance task-specific recognition capability. Further, we identify that current adaptation approaches are particularly vulnerable to potential noise in the fewshot sample set. To mitigate this issue, we introduce a plug-and-play few-shot instance reweighting technique to suppress noisy outliers and amplify clean samples for more stable adaptation. Our extensive experimental results across 15 datasets validate that the proposed SimNL outperforms existing state-of-the-art methods on both few-shot learning and domain generalization tasks while achieving competitive computational efficiency. Code is available at https://***/zhangce01/SimNL.

关键词： Adaptation models computer vision Codes Accuracy Computational modeling Noise Transforms Computational efficiency Noise measurement Few shot learning

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Towards Unbiased Continual Learning: Avoiding Forgetting in the Presence of Spurious Correlations

Towards Unbiased Continual Learning: Avoiding Forgetting in ...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Giacomo Capitani Lorenzo Bonicelli Angelo Porrello Federico Bolelli Simone Calderara Elisa Ficarra Università degli Studi di Modena e Reggio Emilia Italy

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Continual Learning (CL) has emerged as a paramount area in Artificial Intelligence (AI) because of its ability to learn multiple tasks sequentially without significant performance degradation. Despite the growing interest in CL frameworks, a critical aspect must be addressed: the inherent biases within training data. In this work, we show that, if overlooked, these biases can significantly impair the efficacy of continual learning models by inducing reliance on suboptimal shortcuts during data stream and memory retention, exacerbating catastrophic forgetting. In response, we present Learning without Shortcuts (LwS), which sets forth two primary objectives: (i) to identify and mitigate the exploitation of spurious correlations within the data stream and (ii) to develop a novel mechanism that constructs a fair memory buffer used in replay-based CL strategies. Our buffer construction strategy exploits the model confidence in a given example to balance the portion of samples per class, hence their contribution when replay activates. Unlike existing methods, LwS is agnostic to protected attributes, and results highlight that the proposed solution is indeed resilient to spurious correlations in CL settings. Code is available at https://***/aimagelab/mammoth.

关键词： Continuing education Degradation computer vision Correlation Neural networks Training data Solids Data models Object recognition Artificial intelligence

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Evaluating Multimodal vision-Language Model Prompting Strategies for Visual Question Answering in Road Scene Understanding

Evaluating Multimodal Vision-Language Model Prompting Strate...

引用

ieee Winter Applications and computer vision Workshops (WACVW)

作者： Aryan Keskar Srinivasa Perisetla Ross Greer University of California Merced

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Understanding complex traffic scenes is a crucial challenge in advancing autonomous driving systems. Visual Question Answering (VQA) tasks have emerged as a promising approach to extracting actionable insights from multimodal traffic data, enabling vehicles to make accurate, real-time decisions. The MAPLM-QA dataset, introduced as part of the 2025 WACV Large Language vision Models Challenge for Autonomous Driving (LLVM-AD), offers a robust benchmark for this task, comprising 14,000 multi-modal frames combining high-resolution panoramic images and rendered Bird's Eye View (BEV) depictions of LiDAR 3D point clouds. In this work, we explore the application of NVIDIA's vision-Language Model (ViLA) to address VQAs in MAPLM-QA. By employing detailed prompt engineering tailored to the dataset, we systematically evaluate ViLA's performance, identifying strengths in certain metrics such as quality assessment while highlighting challenges in lane counting, intersection recognition, and nuanced scene understanding. Our findings illustrate the potential of vision-Language Models (VLMs) in enhancing traffic scene analysis and autonomous driving, establishing a strong foundation and analysis of limitations for future research in leveraging VLMs and multimodal datasets toward scalable, robust traffic scene understanding.

关键词： Point cloud compression Visualization Solid modeling Three-dimensional displays Roads Question answering (information retrieval) Real-time systems Quality assessment Prompt engineering Autonomous vehicles

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Mixed Patch Visible-Infrared Modality Agnostic Object Detection

Mixed Patch Visible-Infrared Modality Agnostic Object Detect...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Heitor R. Medeiros David Latortue Eric Granger Marco Pedersoli Dept. of Systems Engineering Laboratoire d'imagerie de vision et d'intelligence artificielle (LIVIA) International Laboratory on Learning Systems (ILLS) ETS Montreal Canada

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In real-world scenarios, using multiple modalities like visible (RGB) and infrared (IR) can greatly improve the performance of a predictive task such as object detection (OD). Multimodal learning is a common way to leverage these modalities, where multiple modality-specific encoders and a fusion module are used to improve performance. In this paper, we tackle a different way to employ RGB and IR modalities, where only one modality or the other is observed by a single shared vision encoder. This realistic setting requires a lower memory footprint and is more suitable for applications such as autonomous driving and surveillance, which commonly rely on RGB and IR data. However, when learning a single encoder on multiple modalities, one modality can dominate the other, producing un-even recognition results. This work investigates how to efficiently leverage RGB and IR modalities to train a common transformer-based OD vision encoder while countering the effects of modality imbalance. For this, we introduce a novel training technique to Mix Patches (MiPa)from the two modalities, in conjunction with a patch-wise modality agnostic module, for learning a common representation of both modalities. Our experiments show that MiPa can learn a representation to reach competitive results on traditional RGB/IR benchmarks while only requiring a single modality during inference. Our code is available at: https://***/heitorrapela/MiPa.

关键词： Training computer vision Codes Surveillance Memory management Object detection Detectors Transformers Encoding Mutual information

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Exploiting Inter-Sample Information for Long-Tailed Out-of-Distribution Detection

Exploiting Inter-Sample Information for Long-Tailed Out-of-D...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Nimeshika Udayangani Hadi M. Dolatabadi Sarah Erfani Christopher Leckie The University of Melbourne

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Detecting out-of-distribution (OOD) data is essential for safe deployment of deep neural networks (DNNs). This problem becomes particularly challenging in the presence of long-tailed in-distribution (ID) datasets, often leading to high false positive rates (FPR) and low tail-class ID classification accuracy. In this paper, we demonstrate that exploiting inter-sample relationships using a graph-based representation can significantly improve OOD detection in long-tailed recognition of vision datasets. To this end, we use the feature space of a pre-trained model to initialize our graph structure. We account for the differences between the activation layer distribution of the pre-training vs. training data, and actively introduce Gaussianization to alleviate any deviations from a standard normal distribution in the activation layers of the pre-trained model. We then refine this initial graph representation using graph convolutional networks (GCNs) to arrive at a feature space suitable for long-tailed OOD detection. This leads us to address the inferior performance observed in ID tail-classes within existing OOD detection methods. Experiments over three benchmarks CIFAR10-LT, CIFAR100-LT, and ImageNet-LT demonstrate that our method outperforms the state-of-the-art approaches by a large margin in terms of FPR and tail-class ID classification accuracy.

关键词： Representation learning computer vision Heavily-tailed distribution Accuracy Graph convolutional networks Training data Gaussian distribution Feature extraction Data models Standards

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Learning to Identify Seen, Unseen and Unknown in the Open World: A Practical Setting for Zero-Shot Learning

Learning to Identify Seen, Unseen and Unknown in the Open Wo...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Sethupathy Parameswaran Yuan Fang Chandan Gautam Savitha Ramasamy Xiaoli Li Singapore Management University Institute for Infocomm Research A*STAR Centre for Frontier AI Research A*STAR

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As vision-language models advance, addressing the Zero-Shot Learning (ZSL) problem in the open world becomes increasingly crucial. Specifically, a robust model must handle three types of samples during inference: seen classes with visual and semantic information provided in training, unseen classes with only the semantic information in training, and unknown samples with no prior information from training. Existing methods either handle seen and unseen classes together (ZSL) or seen and unknown classes (known as Open-Set recognition, OSR). However, none addresses the simultaneous handling of all three, which we term Open-Set Zero-Shot Learning (OZSL). To address this problem, we propose a two-stage approach for OZSL that recognizes seen, unseen, and unknown samples. The first stage classifies samples as either seen or not, while the second stage distinguishes unseen from unknown. Furthermore, we introduce a cross-stage knowledge transfer mechanism that leverages semantic relationships between seen and unseen classes to enhance learning in the second stage. Extensive experiments demonstrate the efficacy of the proposed approach compared to naívely combining existing ZSL and OSR methods. The code is available at https://***/smufang/OZSL.

关键词： Training Visualization computer vision Codes Computational modeling Zero shot learning Semantics Benchmark testing Knowledge transfer

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