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Semi-Supervised Scene Change Detection by Distillation from Feature-metric Alignment

Semi-Supervised Scene Change Detection by Distillation from ...

引用

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

作者： Lee, Seonhoon Kim, Jong-Hwan Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350318920;9798350318937

Scene change detection (SCD) is a critical task for various applications, such as visual surveillance, anomaly detection, and mobile robotics. Recently, supervised methods for SCD have been developed for urban and indoor environments where input image pairs are typically unaligned due to differences in camera viewpoints. However, supervised SCD methods require pixel-wise change labels and alignment labels for the target domain, which can be both timeconsuming and expensive to collect. To tackle this issue, we design an unsupervised loss with regularization methods based on the feature-metric alignment of input image pairs. The proposed unsupervised loss enables the SCD model to jointly learn the flow and the change maps on the target domain. In addition, we propose a semi-supervised learning method based on a distillation loss for the robustness of the SCD model. The proposed learning method is based on the student-teacher structure and incorporates the unsupervised loss of the unlabeled target data and the supervised loss of the labeled synthetic data. Our method achieves considerable performance improvement on the target domain through the proposed unsupervised and distillation loss, using only 10% of the target training dataset without using any labels of the target data.

关键词： Algorithms Image recognition and understanding

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Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration

Do We Still Need Non-Maximum Suppression? Accurate Confidenc...

引用

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

作者： Gilg, Johannes Teepe, Torben Herzog, Fabian Wolters, Philipp Rigoll, Gerhard Tech Univ Munich Munich Germany

ISBN: (纸本)9798350318920;9798350318937

Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessibility and can sometimes produce unreliable predictions. Especially concerning in this regard are the-essentially hand-crafted-non-maximum suppression algorithms that lead to an obfuscated prediction process and biased confidence estimates. We show that we can eliminate classic NMS-style post-processing by using IoU-aware calibration. IoU-aware calibration is a conditional Beta calibration;this makes it parallelizable with no hyperparameters. Instead of arbitrary cutoffs or discounts, it implicitly accounts for the likelihood of each detection being a duplicate and adjusts the confidence score accordingly, resulting in empirically based precision estimates for each detection. Our extensive experiments on diverse detection architectures show that the proposed IoU-aware calibration can successfully model duplicate detections and improve calibration. Compared to the standard sequential NMS and calibration approach, our joint modeling can deliver performance gains over the best NMS-based alternative while producing consistently better-calibrated confidence predictions with less complexity. The code for all our experiments is publicly available(1).

关键词： accountable Algorithms Algorithms ethical computer vision Explainable fair Image recognition and understanding privacy-preserving

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CrashCar101: Procedural Generation for Damage Assessment

CrashCar101: Procedural Generation for Damage Assessment

引用

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

作者： Parslov, Jens Riise, Erik Papadopoulos, Dim P. Tech Univ Denmark Lyngby Denmark Pioneer Ctr AI Copenhagen Denmark

ISBN: (纸本)9798350318920;9798350318937

In this paper, we are interested in addressing the problem of damage assessment for vehicles, such as cars. This task requires not only detecting the location and the extent of the damage but also identifying the damaged part. To train a computer vision system for the semantic part and damage segmentation in images, we need to manually annotate images with costly pixel annotations for both part categories and damage types. To overcome this need, we propose to use synthetic data to train these models. Synthetic data can provide samples with high variability, pixel-accurate annotations, and arbitrarily large training sets without any human intervention. We propose a procedural generation pipeline that damages 3D car models and we obtain synthetic 2D images of damaged cars paired with pixel-accurate annotations for part and damage categories. To validate our idea, we execute our pipeline and render our CrashCar101 dataset. We run experiments on three real datasets for the tasks of part and damage segmentation. For part segmentation, we show that the segmentation models trained on a combination of real data and our synthetic data outperform all models trained only on real data. For damage segmentation, we show the sim2real transfer ability of CrashCar101.

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

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pSTarC: Pseudo Source Guided Target Clustering for Fully Test-Time Adaptation

pSTarC: Pseudo Source Guided Target Clustering for Fully Tes...

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

作者： Sreenivas, Manogna Chakrabarty, Goirik Biswas, Soma IISc Bangalore Bangalore Karnataka India IISER Pune Pune Maharashtra India

ISBN: (纸本)9798350318920;9798350318937

Test Time Adaptation (TTA) is a pivotal concept in machine learning, enabling models to perform well in real-world scenarios, where test data distribution differs from training. In this work, we propose a novel approach called pseudo Source guided Target Clustering (pSTarC) addressing the relatively unexplored area of TTA under real-world domain shifts. This method draws inspiration from target clustering techniques and exploits the source classifier for generating pseudo-source samples. The test samples are strategically aligned with these pseudo-source samples, facilitating their clustering and thereby enhancing TTA performance. pSTarC operates solely within the fully test-time adaptation protocol, removing the need for actual source data. Experimental validation on a variety of domain shift datasets, namely VisDA, Office-Home, DomainNet-126, CIFAR-100C verifies pSTarC's effectiveness. This method exhibits significant improvements in prediction accuracy along with efficient computational requirements. Furthermore, we also demonstrate the universality of the pSTarC framework by showing its effectiveness for the continuous TTA framework.

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

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LongFormer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs

LongFormer: Longitudinal Transformer for Alzheimer's Disease...

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

作者： Chen, Qiuhui Fu, Qiang Bai, Hao Hong, Yi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Structural magnetic resonance imaging (sMRI), especially longitudinal sMRI, is often used to monitor and capture disease progression during the clinical diagnosis of Alzheimer's Disease (AD). However, current methods neglect AD's progressive nature and have mostly relied on a single image for recognizing AD. In this paper, we consider the problem of leveraging the longitudinal MRIs of a subject for AD classification. To address the challenges of missing data, data demand, and subtle changes over time in learning longitudinal 3D MRIs, we propose a novel model LongFormer, which is a hybrid 3D CNN and transformer design to learn from image and longitudinal flow pairs. Our model can fully leverage all images in a dataset and effectively fuse spatiotemporal features for classification. We evaluate our model on three datasets, i.e., ADNI, OASIS, and AIBL, and compare it to eight baseline algorithms. Our proposed LongFormer achieves state-of-the-art performance in classifying AD and NC subjects from all three public datasets. Our source code is available online at https://***/Qybc/LongFormer.

关键词： 3D computer vision Algorithms Algorithms Image recognition and understanding

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Enhancing Multi-view Pedestrian Detection Through Generalized 3D Feature Pulling

Enhancing Multi-view Pedestrian Detection Through Generalize...

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

作者： Aung, Sithu Park, Haesol Jung, Hyungjoo Cho, Junghyun KIST Seongnam South Korea UST Daejeon South Korea Yonsei KIST Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

The main challenge in multi-view pedestrian detection is integrating view-specific features into a unified space for comprehensive end-to-end perception. Prior multi-view detection methods have focused on projecting perspective-view features onto the ground plane, creating a "bird's eye view" (BEV) representation of the scene. This paper proposes a simple but effective architecture that utilizes a non-parametric 3D feature-pulling strategy. This strategy directly extracts the corresponding 2D features for each valid voxel within the 3D feature volume, addressing the feature loss that may arise in previous methods. The proposed framework introduces three novel modules, each crafted to bolster the generalization capabilities of multi-view detection systems. Through extensive experiments, the efficacy of the proposed model is demonstrated. The results show a new state-of-the-art accuracy, both in conventional scenarios and particularly in the context of scene generalization benchmarks.

关键词： 3D computer vision Algorithms Algorithms Image recognition and understanding

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SCENIC: A JAX Library for computer vision Research and Beyond

SCENIC: A JAX Library for Computer Vision Research and Beyon...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Dehghani, Mostafa Gritsenko, Alexey Arnab, Anurag Minderer, Matthias Tay, Yi Google Brain Mountain View CA 94043 USA Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

SCENIC is an open-source' JAX library with a focus on transformer-based models for computer vision research and beyond. The goal of this toolkit is to facilitate rapid experimentation, prototyping, and research of new architectures and models. SCENIC supports a diverse range of tasks (e.g., classification, segmentation, detection) and facilitates working on multi-modal problems, along with GPU/TPU support for large-scale, multi-host and multi-device training. SCENIC also offers optimized implementations of stateof-the-art research models spanning a wide range of modalities. SCENIC has been successfully used for numerous projects and published papers and continues serving as the library of choice for rapid prototyping and publication of new research ideas.

关键词： Training computer vision Computational modeling Machine learning Transformers Rapid prototyping Libraries

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LP-DIF: Learning Local pattern-specific Deep Implicit Function for 3D Objects and Scenes

LP-DIF: Learning Local Pattern-specific Deep Implicit Functi...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Meng Liu, Yu-Shen Gao, Yue Shi, Kanle Fang, Yi Han, Zhizhong Tsinghua Univ Sch Software BNRist Beijing Peoples R China Kuaishou Technol Beijing Peoples R China New York Univ Abu Dhabi Ctr Artificial Intelligence & Robot Abu Dhabi U Arab Emirates Wayne State Univ Dept Comp Sci Detroit MI USA

ISBN: (纸本)9798350301298

Deep Implicit Function (DIF) has gained much popularity as an efficient 3D shape representation. To capture geometry details, current mainstream methods divide 3D shapes into local regions and then learn each one with a local latent code via a decoder. Such local methods can capture more local details due to less diversity among local regions than global shapes. Although the diversity of local regions has been decreased compared to global approaches, the diversity in different local regions still poses a challenge in learning an implicit function when treating all regions equally using only a single decoder. What is worse, these local regions often exhibit imbalanced distributions, where certain regions have significantly fewer observations. This leads that fine geometry details could not be preserved well. To solve this problem, we propose a novel Local pattern-specific Implicit Function, named LP-DIF, to represent a shape with clusters of local regions and multiple decoders, where each decoder only focuses on one cluster of local regions which share a certain pattern. Specifically, we first extract local codes for all regions, and then cluster them into multiple groups in the latent space, where similar regions sharing a common pattern fall into one group. After that, we train multiple decoders for mining local patterns of different groups, which simplifies the learning of fine geometric details by reducing the diversity of local regions seen by each decoder. To further alleviate the data-imbalance problem, we introduce a region re-weighting module to each pattern-specific decoder using a kernel density estimator, which dynamically re-weights the regions during learning. Our LP-DIF can restore more geometry details, and thus improve the quality of 3D reconstruction. Experiments demonstrate that our method can achieve the state-of-the-art performance over previous methods. Code is available at https://***/gtyxyz/lpdif.

关键词： 3D from multi-view and sensors

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iBARLE: imBalance-Aware Room Layout Estimation

iBARLE: imBalance-Aware Room Layout Estimation

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

作者： Jing, Taotao Wang, Lichen Khosravan, Naji Wan, Zhiqiang Bessinger, Zachary Ding, Zhengming Kang, Sing Bing Tulane Univ New Orleans LA 70118 USA Zillow Grp Seattle WA USA

ISBN: (纸本)9798350318920;9798350318937

Room layout estimation predicts layouts from a single panorama. It requires datasets with large-scale and diverse room shapes to well train the models. However, there are significant imbalances in real-world datasets including the dimensions of layout complexity, camera locations, and variation in scene appearance. These issues considerably influence the model training performance. In this work, we propose imBalance-Aware Room Layout Estimation (iBARLE) framework to address these issues. iBARLE consists of: (1) Appearance Variation Generation (AVG) module, which promotes visual appearance domain generalization, (2) Complex Structure Mix-up (CSMix) module, which enhances generalizability w.r.t. room structure, and (3) a gradient-based layout objective function, which allows more effective accounting for occlusions in complex layouts. All modules are jointly trained and help each other to achieve the best performance. Experiments and ablation studies based on ZInD [6] dataset illustrate that iBARLE has state-of-the-art performance compared with other layout estimation baselines.

关键词： 3D Algorithms Algorithms Applications etc. Generative models for image Image recognition and understanding video Virtual / augmented reality

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RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection

RPCANet: Deep Unfolding RPCA Based Infrared Small Target Det...

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

作者： Wu, Fengyi Zhang, Tianfang Li, Lei Huang, Yian Peng, Zhenming Univ Elect Sci & Technol China Chengdu Peoples R China Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9798350318920;9798350318937

Deep learning (DL) networks have achieved remarkable performance in infrared small target detection (ISTD). However, these structures exhibit a deficiency in interpretability and are widely regarded as black boxes, as they disregard domain knowledge in ISTD. To alleviate this issue, this work proposes an interpretable deep network for detecting infrared dim targets, dubbed RPCANet. Specifically, our approach formulates the ISTD task as sparse target extraction, low-rank background estimation, and image reconstruction in a relaxed Robust Principle Component Analysis (RPCA) model. By unfolding the iterative optimization updating steps into a deep-learning framework, time-consuming and complex matrix calculations are replaced by theory-guided neural networks. RPCANet detects targets with clear interpretability and preserves the intrinsic image feature, instead of directly transforming the detection task into a matrix decomposition problem. Extensive experiments substantiate the effectiveness of our deep unfolding framework and demonstrate its trustworthy results, surpassing baseline methods in both qualitative and quantitative evaluations. Our source code is available at https://***/fengyiwu98/RPCANet.

关键词： accountable Algorithms Algorithms ethical computer vision Explainable fair Image recognition and understanding privacy-preserving

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