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Time to Shine: Fine-Tuning Object Detection Models with Synthetic Adverse Weather Images

Time to Shine: Fine-Tuning Object Detection Models with Synt...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rothmeier, Thomas Huber, Werner Knoll, Alois C. Univ Appl Sci Ingolstadt Ingolstadt Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9798350318920;9798350318937

The detection of vehicles, pedestrians, and obstacles plays an important role in the decision-making process of autonomous vehicles. While existing methods achieve high detection accuracy under good environmental conditions, they often fail in adverse weather conditions due to limited visibility, blurred contours, and low contrast. These "edge-case" scenarios are not well represented in existing datasets and are not handled properly by object detection algorithms. In our work, we propose a novel approach to synthesising photorealistic and highly diverse scenarios that can be used to fine-tune object detection algorithms in adverse weather conditions such as snow, fog, and rain. The approach uses the Midjourney text-to-image model to create accurate synthetic images of desired weather conditions. Our experiments show that training with our dataset significantly improves detection accuracy in harsh weather conditions. Our results are compared to baseline models and models fine-tuned on augmented clear weather images.

关键词： Algorithms Algorithms Datasets and evaluations Image recognition and understanding

来源：评论

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Self-supervised Learning of Semantic Correspondence Using Web Videos

Self-supervised Learning of Semantic Correspondence Using We...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kwon, Donghyeon Cho, Minsu Kwak, Suha POSTECH Dept CSE Pohang South Korea POSTECH Grad Sch AI Pohang South Korea

ISBN: (纸本)9798350318920;9798350318937

Existing datasets for semantic correspondence are often limited in terms of both the amount of labeled data and diversity of labeled keypoints due to the tremendous cost of manual correspondence labeling. To address this issue, we propose the first self-supervised learning framework that utilizes a large amount of web videos collected and annotated fully automatically. Our main motivation is that smooth changes between consecutive video frames allow to build accurate space-time correspondences with no human intervention. Hence, we establish space-time correspondences within each web video and leverage them for deriving pseudo correspondence labels between two distant frames of the video. In addition, we present a dedicated training strategy that facilitates stable training using web videos with such pseudo labels. Our experiments on public benchmarks demonstrated that the proposed method surpasses existing self-supervised learning models and that our self-supervised learning as pretraining for supervised learning improves performance substantially. Our code-base for web video crawling and pseudo label generation will be released public to promote future research.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

来源：评论

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Instruct Me More! Random Prompting for Visual In-Context Learning

Instruct Me More! Random Prompting for Visual In-Context Lea...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhang, Jiahao Wang, Bowen Li, Liangzhi Nakashima, Yuta Nagahara, Hajime Osaka Univ Suita Osaka Japan

ISBN: (纸本)9798350318920;9798350318937

Large-scale models trained on extensive datasets, have emerged as the preferred approach due to their high generalizability across various tasks. In-context learning (ICL), a popular strategy in natural language processing, uses such models for different tasks by providing instructive prompts but without updating model parameters. This idea is now being explored in computer vision, where an input-output image pair (called an in-context pair) is supplied to the model with a query image as a prompt to exemplify the desired output. The efficacy of visual ICL often depends on the quality of the prompts. We thus introduce a method coined Instruct Me More (InMeMo), which augments in-context pairs with a learnable perturbation (prompt), to explore its potential. Our experiments on mainstream tasks reveal that InMeMo surpasses the current state-of-the-art performance. Specifically, compared to the baseline without learnable prompt, InMeMo boosts mIoU scores by 7.35 and 15.13 for foreground segmentation and single object detection tasks, respectively. Our findings suggest that InMeMo offers a versatile and efficient way to enhance the performance of visual ICL with lightweight training. Code is available at https://***/Jackieam/InMeMo.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector

Debiasing, calibrating, and improving Semi-supervised Learni...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Nguyen, Khanh-Binh Sungkyunkwan Univ Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

Recent studies on semi-supervised learning (SSL) have achieved great success. Despite their promising performance, current state-of-the-art methods tend toward increasingly complex designs at the cost of introducing more network components and additional training procedures. In this paper, we propose a simple method named Ensemble Projectors Aided for Semi-supervised Learning (EPASS), which focuses mainly on improving the learned embeddings to boost the performance of the existing contrastive joint-training semi-supervised learning frameworks. Unlike standard methods, where the learned embeddings from one projector are stored in memory banks to be used with contrastive learning, EPASS stores the ensemble embeddings from multiple projectors in memory banks. As a result, EPASS improves generalization, strengthens feature representation, and boosts performance. For instance, EPASS improves strong baselines for semi-supervised learning by 39.47%/31.39%/24.70% top-1 error rate, while using only 100k/1%/10% of labeled data for SimMatch, and achieves 40.24%/32.64%/25.90% top-1 error rate for CoMatch on the ImageNet dataset. These improvements are consistent across methods, network architectures, and datasets, proving the general effectiveness of the proposed methods. Code is available at https://***/beandkay/EPASS.

关键词： accountable Algorithms Algorithms Algorithms and algorithms ethical computer vision Explainable fair formulations Image recognition and understanding Machine learning architectures privacy-preserving

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RSMPNet: Relationship Guided Semantic Map Prediction

RSMPNet: Relationship Guided Semantic Map Prediction

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Sun, Jingwen Wu, Jing Ji, Ze Lai, Yu-Kun Cardiff Univ Cardiff Wales

ISBN: (纸本)9798350318920;9798350318937

In semantic navigation, a top-down map with accurate and complete semantic information is vital to subsequent decision-making. However, due to occlusions and limitations of the robot's field of view (FOV), there are often unobserved areas in the top-down maps. To address this problem, recent works have studied semantic map prediction to complete the top-down maps. In this work, we propose to improve map prediction by integrating relational information. We propose RSMPNet, a relationship-guided semantic map prediction network, which makes use of semantic and spatial relationships to predict unobserved areas from accumulated semantic maps. Specifically, we propose a Relationship Reasoning Layer that includes two modules, namely 1) the Semantic Relationship Graph Reasoning Module (SeGRM) to capture the semantic relationship and 2) the Spatial Relationship Graph Reasoning Module (SpGRM) to utilize the spatial relationship. We also design a semantic relationship enhanced loss to enhance our model to learn semantic relationship information. Experiments show the effectiveness of our proposed network which achieves state-of-the-art performance in semantic map prediction. Our code and dataset are publicly available at https://***/jws39/semantic-map-prediction

关键词： Algorithms Image recognition and understanding

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CHAI: Craters in Historical Aerial Images

CHAI: Craters in Historical Aerial Images

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Burges, Marvin Zambanini, Sebastian Pirker, Philipp TU Wien Comp Vis Lab Vienna Austria Luftbilddatenbank Dr Carls GmbH Vienna Austria

ISBN: (纸本)9798350318920;9798350318937

In this paper we highlight the importance of historical aerial images in better understanding past events and their impact on their surroundings. More specifically, we are interested in studying bomb craters from World War II in Central Europe. We note the scarcity of publicly accessible datasets that provide labeled bomb craters and subsequently introduce a novel, domain-expert-annotated dataset comprised of 99 historical aerial images of Austria and Germany. We divide said data into training, validation, and test sets, and conduct training and evaluation using different object detectors - both general-purpose and specifically designed for remote sensing applications. This dataset thus serves as a benchmark for developing and evaluating (several) algorithms dedicated to the automated detection and analysis of bomb craters in historical aerial images. We underscore the uniqueness of this dataset as the first publicly available resource containing annotated bomb craters, thereby offering researchers a valueable and novel opportunity for future exploration. Lastly, we investigate possibilities for extending and enriching our data to enhance future studies, particularly within the context of preliminary risk estimation for unexploded bombs.

关键词： Algorithms Applications Applications Environmental monitoring / climate change / ecology Image recognition and understanding Remote Sensing

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Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection

Beyond Classification: Definition and Density-based Estimati...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Popordanoska, Teodora Tiulpin, Aleksei Blaschko, Matthew B. Katholieke Univ Leuven ESAT PSI Leuven Belgium Univ Oulu HST Res Unit Oulu Finland

ISBN: (纸本)9798350318920;9798350318937

Despite their impressive predictive performance in various computer vision tasks, deep neural networks (DNNs) tend to make overly confident predictions, which hinders their widespread use in safety-critical applications. While there have been recent attempts to calibrate DNNs, most of these efforts have primarily been focused on classification tasks, thus neglecting DNN-based object detectors. Although several recent works addressed calibration for object detection and proposed differentiable penalties, none of them are consistent estimators of established concepts in calibration. In this work, we tackle the challenge of defining and estimating calibration error specifically for this task. In particular, we adapt the definition of classification calibration error to handle the nuances associated with object detection, and predictions in structured output spaces more generally. Furthermore, we propose a consistent and differentiable estimator of the detection calibration error, utilizing kernel density estimation. Our experiments demonstrate the effectiveness of our estimator against competing train-time and post-hoc calibration methods, while maintaining similar detection performance.

关键词： Algorithms Applications Applications Autonomous Driving Biomedical / healthcare / medicine Image recognition and understanding

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Deep Metric Learning with Chance Constraints

Deep Metric Learning with Chance Constraints

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Gurbuz, Yeti Z. Can, Ogul Alatan, A. Aydin MetaDialog Dubai U Arab Emirates Cerebrate AI Dubai U Arab Emirates OGAM & METU Ankara Turkiye

ISBN: (纸本)9798350318920;9798350318937

Deep metric learning (DML) aims to minimize empirical expected loss of the pairwise intra-/inter- class proximity violations in the embedding space. We relate DML to feasibility problem of finite chance constraints. We show that minimizer of proxy-based DML satisfies certain chance constraints, and that the worst case generalization performance of the proxy-based methods can be characterized by the radius of the smallest ball around a class proxy to cover the entire domain of the corresponding class samples, suggesting multiple proxies per class helps performance. To provide a scalable algorithm as well as exploiting more proxies, we consider the chance constraints implied by the minimizers of proxybased DML instances and reformulate DML as finding a feasible point in intersection of such constraints, resulting in a problem to be approximately solved by iterative projections. Simply put, we repeatedly train a regularized proxy-based loss and re-initialize the proxies with the embeddings of the deliberately selected new samples. We applied our method with 4 well-accepted DML losses and show the effectiveness with extensive evaluations on 4 popular DML benchmarks. Code is available at: https://***/yetigurbuz/ccp-dml

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Fixed pattern Noise Removal For Multi-View Single-Sensor Infrared Camera

Fixed Pattern Noise Removal For Multi-View Single-Sensor Inf...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Barral, Arnaud Arias, Pablo Davy, Axel Univ Paris Saclay Ctr Borelli ENS Paris Saclay CNRS Gif Sur Yvette France Univ Pompeu Fabra Dept Informat & Commun Technol Barcelona Spain

ISBN: (纸本)9798350318920;9798350318937

Fixed pattern noise (FPN) is a temporally coherent noise present on videos due to the non-uniformities in the response of the imaging sensor. It is a common problem for infrared videos which degrades the quality of the observation and hinders subsequent applications. In this work we introduce a generalization of the FPN removal problem where the input data consists of several different sequences with the same FPN. This is motivated by infrared cameras that capture multiple views with a single sensor via a periodic motion pattern of a mirror or the camera itself, such as those used in surveillance. This multi-view setting allows for a much more accurate estimation of the FPN in comparison with the standard FPN removal problem from a single view. We propose a novel energy minimization approach for multi-view FPN removal, and two optimization algorithms that can be applied both in an off-line and on-line manner. In addition, we show that the proposed energy can be adapted to the problem of FPN removal from a single view with a rolling window approach, obtaining a significant improvement over the state of the art. We demonstrate the performance of the proposed method with synthetic data and real data from surveillance infrared cameras.

关键词： Algorithms Applications Embedded sensing / real-time techniques Low-level and physics-based vision

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Robust Feature Learning and Global Variance-Driven Classifier Alignment for Long-Tail Class Incremental Learning

Robust Feature Learning and Global Variance-Driven Classifie...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kalla, Jayateja Biswas, Soma Indian Inst Sci Dept Elect Engn Bangalore Karnataka India

ISBN: (纸本)9798350318920;9798350318937

This paper introduces a two-stage framework designed to enhance long-tail class incremental learning, enabling the model to progressively learn new classes, while mitigating catastrophic forgetting in the context of long-tailed data distributions. Addressing the challenge posed by the under-representation of tail classes in long-tail class incremental learning, our approach achieves classifier alignment by leveraging global variance as an informative measure and class prototypes in the second stage. This process effectively captures class properties and eliminates the need for data balancing or additional layer tuning. Alongside traditional class incremental learning losses in the first stage, the proposed approach incorporates mixup classes to learn robust feature representations, ensuring smoother boundaries. The proposed framework can seamlessly integrate as a module with any class incremental learning method to effectively handle long-tail class incremental learning scenarios. Extensive experimentation on the CIFAR-100 and ImageNet-Subset datasets validates the approach's efficacy, showcasing its superiority over state-of-the-art techniques across various long-tail CIL settings. Code is available at https://***/JAYATEJAK/GVAlign.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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