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UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering

UnsupervisedR&R: Unsupervised Point Cloud Registration via D...

引用

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

作者： El Banani, Mohamed Gao, Luya Johnson, Justin Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665445092

Aligning partial views of a scene into a single whole is essential to understanding one's environment and is a key component of numerous robotics tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems that can outperform traditional methods by leveraging pose supervision. However, with the rising prevalence of cameras with depth sensors, we can expect a new stream of raw RGB-D data without the annotations needed for supervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to learning point cloud registration from raw RGB-D video. The key idea is to leverage differentiable alignment and rendering to enforce photometric and geometric consistency between frames. We evaluate our approach on indoor scene datasets and find that we outperform existing traditional approaches with classical and learned descriptors while being competitive with supervised geometric point cloud registration approaches.

关键词： Training Simultaneous localization and mapping Robot vision systems Feature extraction Rendering (computer graphics) Cameras Sensors

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Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images

Weakly-supervised Instance Segmentation via Class-agnostic L...

引用

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

作者： Wang, Xinggang Feng, Jiapei Hu, Bin Ding, Qi Ran, Longjin Chen, Xiaoxin Liu, Wenyu Huazhong Univ Sci & Technol Wuhan Peoples R China VIVO Inc Pleasanton CA USA

ISBN: (纸本)9781665445092

Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based solution for weakly-supervised instance segmentation. The BoxCaseg model is jointly trained using box-supervised images and salient images in a multi-task learning manner. The fine-annotated salient images provide class-agnostic and precise object localization guidance for box-supervised images. The object masks predicted by a pretrained BoxCaseg model are refined via a novel merged and dropped strategy as proxy ground truth to train a Mask R-CNN for weakly-supervised instance segmentation. Only using 7991 salient images, the weakly-supervised Mask R-CNN is on par with fully-supervised Mask R-CNN on PASCAL VOC and significantly outperforms previous state-of-the-art box-supervised instance segmentation methods on COCO.

关键词： Training Location awareness Learning systems Image segmentation computer vision Codes Merging

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On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective

On Focal Loss for Class-Posterior Probability Estimation: A ...

引用

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

作者： Charoenphakdee, Nontawat Vongkulbhisal, Jayakorn Chairatanakul, Nuttapong Sugiyama, Masashi Univ Tokyo Tokyo Japan RIKEN AIP Tokyo Japan IBM Res Yorktown Hts NY USA Tokyo Inst Technol Tokyo Japan AIST RWBC OIL Tokyo Japan

ISBN: (纸本)9781665445092

The focal loss has demonstrated its effectiveness in many real-world applications such as object detection and image classification, but its theoretical understanding has been limited so far. In this paper, we first prove that the focal loss is classification-calibrated, i.e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified. However, we also prove a negative fact that the focal loss is not strictly proper, i.e., the confidence score of the classifier obtained by focal loss minimization does not match the true class-posterior probability. This may cause the trained classifier to give an unreliable confidence score, which can be harmful in critical applications. To mitigate this problem, we prove that there exists a particular closed-form transformation that can recover the true class-posterior probability from the outputs of the focal risk minimizer. Our experiments show that our proposed transformation successfully improves the quality of class-posterior probability estimation and improves the calibration of the trained classifier, while preserving the same prediction accuracy.

关键词： computer vision Estimation Object detection Minimization pattern recognition Calibration Image classification

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Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation

Temporally-Weighted Hierarchical Clustering for Unsupervised...

引用

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

作者： Sarfraz, M. Saquib Murray, Naila Sharma, Vivek Diba, Ali van Gool, Luc Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany Facebook AI Res Menlo Pk CA USA MIT Cambridge MA 02139 USA Harvard Med Sch Boston MA 02115 USA Katholieke Univ Leuven Leuven Belgium Swiss Fed Inst Technol Zurich Switzerland Daimler TSS Ulm Germany

ISBN: (纸本)9781665445092

Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks. For this and other video understanding tasks, supervised approaches have achieved encouraging performance but require a high volume of detailed frame-level annotations. We present a fully automatic and unsupervised approach for segmenting actions in a video that does not require any training. Our proposal is an effective temporally-weighted hierarchical clustering algorithm that can group semantically consistent frames of the video. Our main finding is that representing a video with a 1-nearest neighbor graph by taking into account the time progression is sufficient to form semantically and temporally consistent clusters of frames where each cluster may represent some action in the video. Additionally, we establish strong unsupervised baselines for action segmentation and show significant performance improvements over published unsupervised methods on five challenging action segmentation datasets. Our code is available.

关键词： Training Measurement Visualization computer vision Codes Spatiotemporal phenomena pattern recognition

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Understanding Object Dynamics for Interactive Image-to-Video Synthesis

Understanding Object Dynamics for Interactive Image-to-Video...

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

作者： Blattmann, Andreas Milbich, Timo Dorkenwald, Michael Ommer, Bjoern Heidelberg Univ Interdisciplinary Ctr Sci Comp HCI Heidelberg Germany

ISBN: (纸本)9781665445092

What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks. Project page is available st https://***/3cxfa2L.

关键词： Training computer vision Limiting Shape Computational modeling Video sequences Data models

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Visualizing Adapted Knowledge in Domain Transfer

Visualizing Adapted Knowledge in Domain Transfer

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

作者： Hou, Yunzhong Zheng, Liang Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

A source model trained on source data and a target model learned through unsupervised domain adaptation (UDA) usually encode different knowledge. To understand the adaptation process, we portray their knowledge difference with image translation. Specifically, we feed a translated image and its original version to the two models respectively, formulating two branches. Through updating the translated image, we force similar outputs from the two branches. When such requirements are met, differences between the two images can compensate for and hence represent the knowledge difference between models. To enforce similar outputs from the two branches and depict the adapted knowledge, we propose a source-free image translation method that generates source-style images using only target images and the two models. We visualize the adapted knowledge on several datasets with different UDA methods and find that generated images successfully capture the style difference between the two domains. For application, we show that generated images enable further tuning of the target model without accessing source data.

关键词： Adaptation models computer vision Computational modeling Force Data visualization Data models pattern recognition

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

作者： Collier, Mark Mustafa, Basil Kokiopoulou, Efi Jenatton, Rodolphe Berent, Jesse Google AI Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Large scale image classification datasets often contain noisy labels. We take a principled probabilistic approach to modelling input-dependent, also known as heteroscedastic, label noise in these datasets. We place a multivariate Nor-mal distributed latent variable on the final hidden layer of a neural network classifier. The covariance matrix of this latent variable, models the aleatoric uncertainty due to label noise. We demonstrate that the learned covariance structure captures known sources of label noise between semantically similar and co-occurring classes. Compared to standard neural network training and other baselines, we show significantly improved accuracy on Imagenet ILSVRC 2012 79.3% (+ 2.6%), Imagenet-21k 47.0% (+ 1.1%) and JFT 64.7% (+ 1.6%). We set a new state-of-the-art result on Webvision 1.0 with 76.6% top-1 accuracy. These datasets range from over 1M to over 300M training examples and from 1k classes to more than 21k classes. Our method is simple to use, and we provide an implementation that is a drop-in replacement for the final fully-connected layer in a deep classifier.

关键词： Training Correlation Uncertainty Neural networks Estimation Probabilistic logic pattern recognition

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Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

Joint Generative and Contrastive Learning for Unsupervised P...

引用

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

作者： Chen, Hao Wang, Yaohui Lagadec, Benoit Dantcheva, Antitza Bremond, Francois INRIA Le Chesnay France Univ Cote Azur Nice France European Syst Integrat Le Cannet France

ISBN: (纸本)9781665445092

Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework. While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant features for generation. In this context, we propose a mesh-based view generator. Specifically, mesh projections serve as references towards generating novel views of a person. In addition, we propose a view-invariant loss to facilitate contrastive learning between original and generated views. Deviating from previous GAN-based unsupervised ReID methods involving domain adaptation, we do not rely on a labeled source dataset, which makes our method more flexible. Extensive experimental results show that our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID datsets.

关键词： Training computer vision Adaptation models Three-dimensional displays Codes Image synthesis Generative adversarial networks

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How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tommie Kerssies Daan De Geus Gijs Dubbelman Eindhoven University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are fine-tuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to fine-tune the ViT-B variants of VFMs with a 16 × 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page 1 .

关键词： Training computer vision Analytical models Codes Semantic segmentation conferences Benchmark testing

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Multi-View Spatial-Temporal Learning for Understanding Unusual Behaviors in Untrimmed Naturalistic Driving Videos

Multi-View Spatial-Temporal Learning for Understanding Unusu...

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ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Huy-Hung Nguyen Chi Dai Tran Long Hoang Pham Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Duong Khac Vu Quoc Pham-Nam Ho Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The task of Naturalistic Driving Action recognition aims to detect and temporally localize distracting driving behavior in untrimmed videos. In this paper, we introduce our framework for Track 3 of the 8 th AI City Challenge in 2024. The approach is primarily based on large model fine-tuning and ensemble techniques to train a set of action recognition models on a small-scale dataset. Starting with raw videos, we segment them into individual action sequences based on their annotation. We then fine-tune four different action recognition models, with K-fold cross-validation applied to the segmented data. Following this, we execute a multi-view ensemble, selecting the most visible camera views for each action class to generate clip-level classification results for each video. Finally, a multi-step post-processing algorithm, which is designed for the AI City Challenge dataset’s specific features, is employed to perform temporal action localization and produce temporal segments for the actions. Our solution achieves a final mOS score of 0.7798 and attains the 5 th rank on the public leaderboard for the test set A2 of the challenge. The source code will be publicly available at https://***/SKKUAutoLab/AIC24-Track03.

关键词： Location awareness Source coding Urban areas X3D Prediction algorithms Classification algorithms pattern recognition

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