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The Multi-Temporal Urban Development SpaceNet Dataset

The Multi-Temporal Urban Development SpaceNet Dataset

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

作者： Van Etten, Adam Hogan, Daniel Manso, Jesus Martinez Shermeyer, Jacob Weir, Nicholas Lewis, Ryan In Q Tel CosmiQ Works Arlington VA 22003 USA Planet San Francisco CA 94107 USA Capella Space San Francisco CA USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 231 United Nations Sustainable Development Goals Indicators, but the World Bank estimates that over 100 countries currently lack effective Civil Registration systems. To help address this deficit and develop novel computer vision methods for time series data, we present the Multi-Temporal Urban Development SpaceNet (MUDS, also known as SpaceNet 7) dataset. This open source dataset consists of medium resolution (4.0m) satellite imagery mosaics, which includes approximate to 24 images (one per month) covering > 100 unique geographies, and comprises > 40,000 km(2) of imagery and exhaustive polygon labels of building footprints therein, totaling over I IM individual annotations. Each building is assigned a unique identifier (i.e. address), which permits tracking of individual objects over time. Label fidelity exceeds image resolution;this "omniscient labeling" is a unique feature of the dataset, and enables surprisingly precise algorithmic models to be crafted. We demonstrate methods to track building footprint construction (or demolition) over time, thereby directly assessing urbanization. Performance is measured with the newly developed SpaceNet Change and Object Tracking (SCOT) metric, which quantifies both object tracking as well as change detection. We demonstrate that despite the moderate resolution of the data, we are able to track individual building identifiers over time.

关键词： Multiuser detection computer vision Satellites Change detection algorithms Buildings Time series analysis Sociology

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Probing Representation Forgetting in Supervised and Unsupervised Continual Learning

Probing Representation Forgetting in Supervised and Unsuperv...

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

作者： Davari, MohammadReza Asadi, Nader Mudur, Sudhir Aljundi, Rahaf Belilovsky, Eugene Concordia Univ Montreal PQ Canada Mila Quebec AI Inst Montreal PQ Canada Toyota Motor Europe Brussels Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Continual Learning (CL) research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks. Catastrophic forgetting is associated with an abrupt loss of knowledge previously learned by a model when the task, or more broadly the data distribution, being trained on changes. In supervised learning problems this forgetting, resulting from a change in the model's representation, is typically measured or observed by evaluating the decrease in old task performance. However, a model's representation can change without losing knowledge about prior tasks. In this work we consider the concept of representation forgetting, observed by using the difference in performance of an optimal linear classifier before and after a new task is introduced. Using this tool we revisit a number of standard continual learning benchmarks and observe that, through this lens, model representations trained without any explicit control for forgetting often experience small representation forgetting and can sometimes be comparable to methods which explicitly control for forgetting, especially in longer task sequences. We also show that representation forgetting can lead to new insights on the effect of model capacity and loss function used in continual learning. Based on our results, we show that a simple yet competitive approach is to learn representations continually with standard supervised contrastive learning while constructing prototypes of class samples when queried on old samples.

关键词： Representation learning Training Measurement Supervised learning Neural networks Prototypes pattern recognition

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UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

UP-DETR: Unsupervised Pre-training for Object Detection with...

引用

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

作者： Dai, Zhigang Cai, Bolun Lin, Yugeng Chen, Junying South China Univ Technol Sch Software Engn Guangzhou Peoples R China Tencent Wechat AI Shenzhen Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China

ISBN: (纸本)9781665445092

Object detection with transformers (DETR) reaches competitive performance with Faster R-CNN via a transformer encoder-decoder architecture. Inspired by the great success of pre-training transformers in natural language processing, we propose a pretext task named random query patch detection to Unsupervisedly Pre-train DETR (UP-DETR) for object detection. Specifically, we randomly crop patches from the given image and then feed them as queries to the decoder. The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade off classification and localization preferences in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask. In our experiments, UP-DETR significantly boosts the performance of DETR with faster convergence and higher average precision on object detection, one-shot detection and panoptic segmentation.

关键词： Location awareness Training data Object detection Transformers Feature extraction Natural language processing pattern recognition

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Training Networks in Null Space of Feature Covariance for Continual Learning

Training Networks in Null Space of Feature Covariance for Co...

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

作者： Wang, Shipeng Li, Xiaorong Sun, Jian Xu, Zongben Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Natl Engn Lab Big Data Algorithm & Anal Technol Xian 710049 Peoples R China Pazhou Lab Guangzhou 510335 Guangdong Peoples R China

ISBN: (纸本)9781665445092

In the setting of continual learning, a network is trained on a sequence of tasks, and suffers from catastrophic forgetting. To balance plasticity and stability of network in continual learning, in this paper, we propose a novel network training algorithm Adam-NSCL which sequentially optimizes network parameters in the null space of all previous tasks. We first propose two mathematical conditions respectively for achieving network stability and plasticity in continual learning. Based on them, the network training for sequential tasks without forgetting can be simply achieved by projecting the candidate parameter update into the approximate null space of all previous tasks in the network training process, where the candidate parameter update can be generated by Adam. The approximate null space can be derived by applying singular value decomposition to the uncentered covariance matrix of all input features of previous tasks for each linear layer. For efficiency, the uncentered covariance matrix can be incrementally computed after learning each task. We also empirically verify the rationality of the approximate null space at each linear layer. We apply our approach to training networks for continual learning on benchmark datasets of CIFAR-100 and TinyImageNet, and the results suggest that the proposed approach outperforms or matches the state-ot-the-art continual learning approaches.

关键词： Training computer vision Null space Benchmark testing Approximation algorithms Stability analysis pattern recognition

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Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes

Canonical Voting: Towards Robust Oriented Bounding Box Detec...

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

作者： You, Yang Ye, Zelin Lou, Yujing Li, Chengkun Li, Yong-Lu Ma, Lizhuang Wang, Weiming Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

3D object detection has attracted much attention thanks to the advances in sensors and deep learning methods for point clouds. Current state-of-the-art methods like VoteNet regress direct offset towards object centers and box orientations with an additional Multi-Layer-Perceptron network Both their offset and orientation predictions are not accurate due to the fundamental difficulty in rotation classification. In the work, we disentangle the direct offset into Local Canonical Coordinates (LCC), box scales and box orientations. Only LCC and box scales are regressed, while box orientations are generated by a canonical voting scheme. Finally, an LCC-aware back-projection checking algorithm iteratively cuts out bounding boxes from the generated vote maps, with the elimination of false positives. Our model achieves state-of-the-art performance on three standard real-world benchmarks: ScanNet, SceneNN and SUN RGB-D. Our code is available on hups://***/qq456evb/CanonicalVoting.

关键词： Point cloud compression Deep learning computer vision Three-dimensional displays Machine vision Object detection Sensor systems and applications

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Per-Clip Video Object Segmentation

Per-Clip Video Object Segmentation

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

作者： Park, Kwanyong Woo, Sanghyun Oh, Seoung Wug Kweon, In So Lee, Joon-Young Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, memory-based approaches show promising results on semi-supervised video object segmentation. These methods predict object masks frame-by-frame with the help of frequently updated memory of the previous mask. Different from this per-frame inference, we investigate an alternative perspective by treating video object segmentation as clip-wise mask propagation. In this per-clip inference scheme, we update the memory with an interval and simultaneously process a set of consecutive frames (i.e. clip) between the memory updates. The scheme provides two potential benefits: accuracy gain by clip-level optimization and efficiency gain by parallel computation of multiple frames. To this end, we propose a new method tailored for the perclip inference. Specifically, we first introduce a clip-wise operation to refine the features based on intra-clip correlation. In addition, we employ a progressive matching mechanism for efficient information-passing within a clip. With the synergy of two modules and a newly proposed perclip based training, our network achieves state-of-the-art performance on Youtube-VOS 2018/2019 val (84.6% and 84.6%) and DAVIS 2016/2017 val (91.9% and 86.1%). Furthermore, our model shows a great speed-accuracy tradeoff with varying memory update intervals, which leads to huge flexibility.

关键词： Training computer vision Correlation Tracking Shape Memory management Object segmentation

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Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation

Learning Local-Global Contextual Adaptation for Multi-Person...

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

作者： Xue, Nan Wu, Tianfu Xia, Gui-Song Zhang, Liangpei Wuhan Univ Sch Comp Sci Wuhan Peoples R China NC State Univ Dept ECE Raleigh NC 27695 USA Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper studies the problem of multi-person pose estimation in a bottom-up fashion. With a new and strong observation that the localization issue of the center-offset formulation can be remedied in a local-window search scheme in an ideal situation, we propose a multi-person pose estimation approach, dubbed as LOGO-CAP, by learning the LOcal-GlObal Contextual Adaptation for human Pose. Specifically, our approach learns the keypoint attraction maps (KAMs) from the local keypoints expansion maps (KEMs) in small local windows in the first step, which are subsequently treated as dynamic convolutional kernels on the keypoints-focused global heatmaps for contextual adaptation, achieving accurate multi-person pose estimation. Our method is end-to-end trainable with near real-time inference speed in a single forward pass, obtaining state-of-the-art performance on the COCO keypoint benchmark for bottom-up human pose estimation. With the COCO trained model, our method also outperforms prior arts by a large margin on the challenging OCHuman dataset.

关键词： Location awareness Heating systems computer vision Message passing Computational modeling Pose estimation Benchmark testing

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DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

DAFormer: Improving Network Architectures and Training Strat...

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

作者： Hoyer, Lukas Dai, Dengxin Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland MPI Informat Saarbrucken Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapted to real images without requiring their annotations. This process is studied in unsupervised domain adaptation (UDA). Even though a large number of methods propose new adaptation strategies, they are mostly based on outdated network architectures. As the influence of recent network architectures has not been systematically studied, we first benchmark different network architectures for UDA and newly reveal the potential of Transformers for UDA semantic segmentation. Based on the findings, we propose a novel UDA method, DAFormer. The network architecture of DAFormer consists of a Transformer encoder and a multilevel context-aware feature fusion decoder. It is enabled by three simple but crucial training strategies to stabilize the training and to avoid overfitting to the source domain: While (1) Rare Class Sampling on the source domain improves the quality of the pseudo-labels by mitigating the confirmation bias of self-training toward common classes, (2) a Thing-Class ImageNet Feature Distance and (3) a learning rate warmup promote feature transfer from ImageNet pretraining. DAFormer represents a major advance in UDA. It improves the state of the art by 10.8 mIoU for GTA -> Cityscapes and 5.4 mIoU for Synthia -> Cityscapes and enables learning even difficult classes such as train, bus, and truck well. The implementation is available at https://***/lhoyer/DAFormer.

关键词： Training Image segmentation computer vision Annotations Semantics Network architecture Transformers

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Volumetric Environment Representation for vision-Language Navigation

Volumetric Environment Representation for Vision-Language Na...

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

作者： Liu, Rui Wang, Wenguan Yan, Yi Zhejiang Univ CCAI ReLER Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350353006

vision-language navigation (VLN) requires an agent to navigate through an 3D environment based on visual observations and natural language instructions. It is clear that the pivotal factor for successful navigation lies in the comprehensive scene understanding. Previous VLN agents employ monocular frameworks to extract 2D features of perspective views directly. Though straightforward, they struggle for capturing 3D geometry and semantics, leading to a partial and incomplete environment representation. To achieve a comprehensive 3D representation with fine-grained details, we introduce a Volumetric Environment Representation (VER), which voxelizes the physical world into structured 3D cells. For each cell, VER aggregates multi-view 2D features into such a unified 3D space via 2D-3D sampling. Through coarse-to-fine feature extraction and multi-task learning for VER, our agent predicts 3D occupancy, 3D room layout, and 3D bounding boxes jointly. Based on online collected VERs, our agent performs volume state estimation and builds episodic memory for predicting the next step. Experimental results show our environment representations from multi-task learning lead to evident performance gains on VLN. Our model achieves state-of-the-art performance across VLN benchmarks (R2R, REVERIE, and R4R).

关键词： Semantics

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Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning

Local Learning Matters: Rethinking Data Heterogeneity in Fed...

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

作者： Mendieta, Matias Yang, Taojiannan Wang, Pu Lee, Minwoo Ding, Zhengming Chen, Chen Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ N Carolina Dept Comp Sci Charlotte NC USA Tulane Univ Dept Comp Sci New Orleans LA 70118 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distribution among clients is often non-HD in nature, making efficient optimization difficult. To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model. Instead, we consider rethinking solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction. To this end, we first present a systematic study informed by second-order indicators to better understand algorithm effectiveness in FL. Interestingly, we find that standard regularization methods are surprisingly strong performers in mitigating data heterogeneity effects. Based on our findings, we further propose a simple and effective method, FedAlign, to overcome data heterogeneity and the pitfalls of previous methods. FedAlign achieves competitive accuracy with state-of-the-art FL methods across a variety of settings while minimizing computation and memory overhead.

关键词： Performance evaluation computer vision computer aided instruction Systematics Federated learning Distance learning Data models

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