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Online Multiple Object Tracking with Cross-Task Synergy

Online Multiple Object Tracking with Cross-Task Synergy

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

作者： Guo, Song Wang, Jingya Wang, Xinchao Tao, Dacheng Univ Sydney Sydney NSW Australia ShanghaiTech Univ Shanghai Peoples R China Natl Univ Singapore Singapore Singapore Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781665445092

Modern online multiple object tracking (MOT) methods usually focus on two directions to improve tracking performance. One is to predict new positions in an incoming frame based on tracking information from previous frames, and the other is to enhance data association by generating more discriminative identity embeddings. Some works combined both directions within one framework but handled them as two individual tasks, thus gaining little mutual benefits. In this paper, we propose a novel unified model with synergy between position prediction and embedding association. The two tasks are linked by temporal-aware target attention and distractor attention, as well as identity-aware memory aggregation model. Specifically, the attention modules can make the prediction focus more on targets and less on distractors, therefore more reliable embeddings can be extracted accordingly for association. On the other hand, such reliable embeddings can boost identity-awareness through memory aggregation, hence strengthen attention modules and suppress drifts. In this way, the synergy between position prediction and embedding association is achieved, which leads to strong robustness to occlusions. Extensive experiments demonstrate the superiority of our proposed model over a wide range of existing methods on MOTChallenge benchmarks. Our code and models are publicly available at https://***/songguocod/TADAM.

关键词： computer vision Codes Interference Predictive models Benchmark testing Reliability engineering Robustness

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HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding

HCRF-Flow: Scene Flow from Point Clouds with Continuous High...

引用

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

作者： Li, Ruibo Li, Guosheng He, Tong Li, Fayao Shen, Chunhua Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Adelaide Adelaide SA Australia ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781665445092

Scene flow in 3D point clouds plays an important role in understanding dynamic environments. Although significant advances have been made by deep neural networks, the performance is far from satisfactory as only per-point translational motion is considered, neglecting the constraints of the rigid motion in local regions. To address the issue, we propose to introduce the motion consistency to force the smoothness among neighboring points. In addition, constraints on the rigidity of the local transformation are also added by sharing unique rigid motion parameters for all points within each local region. To this end, a high-order CRFs based relation module (Con-HCRFs) is deployed to explore both point-wise smoothness and region-wise rigidity. To empower the CRFs to have a discriminative unary term, we also introduce a position-aware flow estimation module to be incorporated into the Con-HCRFs. Comprehensive experiments on FlyingThings3D and KITTI show that our proposed framework (HCRF-Flow) achieves state-of-the-art performance and significantly outperforms previous approaches substantially.

关键词： Deep learning computer vision Three-dimensional displays Costs Force Dynamics Estimation

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Capturing Omni-Range Context for Omnidirectional Segmentation

Capturing Omni-Range Context for Omnidirectional Segmentatio...

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

作者： Yang, Kailun Zhang, Jiaming Reiss, Simon Hu, Xinxin Stiefelhagen, Rainer Karlsruhe Inst Technol CV HCI Lab Karlsruhe Germany Carl Zeiss Jena Germany Huawei Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving. Enabling an all-encompassing view of street-scenes, omnidirectional cameras present themselves as a perfect fit in such systems. Most segmentation models for parsing urban environments operate on common, narrow Field of View (FoV) images. Transferring these models from the domain they were designed for to 360. perception, their performance drops dramatically, e.g., by an absolute 30.0% (mIoU) on established test-beds. To bridge the gap in terms of FoV and structural distribution between the imaging domains, we introduce Efficient Concurrent Attention Networks (ECANets), directly capturing the inherent long-range dependencies in omnidirectional imagery. In addition to the learned attention-based contextual priors that can stretch across 360. images, we upgrade model training by leveraging multi-source and omni-supervised learning, taking advantage of both: Densely labeled and unlabeled data originating from multiple datasets. To foster progress in panoramic image segmentation, we put forward and extensively evaluate models on Wild PAnoramic Semantic Segmentation (WildPASS), a dataset designed to capture diverse scenes from all around the globe. Our novel model, training regimen and multi-source prediction fusion elevate the performance (mIoU) to new state-of-the-art results on the public PASS (60.2%) and the fresh WildPASS (69.0%) benchmarks.(1)

关键词： Training Image segmentation computer vision Computational modeling Semantics Urban areas Predictive models

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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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All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

All Labels Are Not Created Equal: Enhancing Semi-supervision...

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

作者： Nassar, Islam Herath, Samitha Abbasnejad, Ehsan Buntine, Wray Haffari, Gholamreza Monash Univ Dept Data Sci & AI Fac IT Clayton Vic Australia Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia

ISBN: (纸本)9781665445092

Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against. A common property among its various methods is that they only rely on the model's prediction to make labeling decisions without considering any prior knowledge about the visual similarity among the classes. In this paper, we demonstrate that this degrades the quality of pseudo-labeling as it poorly represents visually similar classes in the pool of pseudo-labeled data. We propose SemCo, a method which leverages label semantics and co-training to address this problem. We train two classifiers with two different views of the class labels: one classifier uses the one-hot view of the labels and disregards any potential similarity among the classes, while the other uses a distributed view of the labels and groups potentially similar classes together. We then co-train the two classifiers to learn based on their disagreements. We show that our method achieves state-of-the-art performance across various SSL tasks including 5.6% accuracy improvement on Mini-ImageNet dataset with 1000 labeled examples. We also show that our method requires smaller batch size and fewer training iterations to reach its best performance.

关键词： Training Visualization Semantics Semisupervised learning Predictive models pattern recognition Labeling

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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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Adaptive Image Transformer for One-Shot Object Detection

Adaptive Image Transformer for One-Shot Object Detection

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

作者： Chen, Ding-Jie Hsieh, He-Yen Liu, Tyng-Luh Acad Sinica Inst Informat Sci Taipei Taiwan Taiwan AI Labs Taipei Taiwan

ISBN: (纸本)9781665445092

One-shot object detection tackles a challenging task that aims at identifying within a target image all object instances of the same class, implied by a query image patch. The main difficulty lies in the situation that the class label of the query patch and its respective examples are not available in the training data. Our main idea leverages the concept of language translation to boost metric-learning-based detection methods. Specifically, we emulate the language translation process to adaptively translate the feature of each object proposal to better correlate the given query feature for discriminating the class-similarity among the proposal-query pairs. To this end, we propose the Adaptive Image Transformer (AIT) module that deploys an attention-based encoder-decoder architecture to simultaneously explore intra-coder and inter-coder (i.e., each proposal-query pair) attention. The adaptive nature of our design turns out to be flexible and effective in addressing the one-shot learning scenario. With the informative attention cues, the proposed model excels in predicting the class-similarity between the target image proposals and the query image patch. Though conceptually simple, our model significantly outperforms a state-of-the-art technique, improving the unseenclass object classification from 63.8 mAP and 22.0 AP50 to 72.2 mAP and 24.3 AP50 on the PASCAL-VOC and MS-COCO benchmark datasets, respectively.

关键词： Adaptation models Training data Object detection Predictive models Feature extraction Transformers pattern recognition

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Neural Side-By-Side: Predicting Human Preferences for No-Reference Super-Resolution Evaluation

Neural Side-By-Side: Predicting Human Preferences for No-Ref...

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

作者： Khrulkov, Valentin Babenko, Artem Yandex Moscow Russia Natl Res Univ Higher Sch Econ Moscow Russia

ISBN: (纸本)9781665445092

Super-resolution based on deep convolutional networks is currently gaining much attention from both academia and industry. However, lack of proper evaluation measures makes it difficult to compare approaches, hampering progress in the field. Traditional measures, such as PSNR or SSIM, are known to poorly correlate with the human perception of image quality. Therefore, in existing works common practice is also to report Mean-Opinion-Score (MOS) - the results of human evaluation of super-resolved images. Unfortunately, the MOS values from different papers are not directly comparable, due to the varying number of raters, their subjectivity, etc. By this paper, we introduce Neural Side-By-Side - a new measure that allows super-resolution models to be compared automatically, effectively approximating human preferences. Namely, we collect a large dataset of aligned image pairs, which were produced by different super-resolution models. Then each pair is annotated by several raters, who were instructed to choose a more visually appealing image. Given the dataset and the labels, we trained a CNN model that obtains a pair of images and for each image predicts a probability of being more preferable than its counterpart. In this work, we show that Neural Side-By-Side generalizes across both new models and new data. Hence, it can serve as a natural approximation of human preferences, which can be used to compare models or tune hyperparameters 'without raters' assistance. We open-source the dataset and the pretrained model' and expect that it will become a handy tool for researchers and practitioners.

关键词： Industries Image quality computer vision Computational modeling Superresolution Tools Predictive models

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Space-Time Distillation for Video Super-Resolution

Space-Time Distillation for Video Super-Resolution

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

作者： Xiao, Zeyu Fu, Xueyang Huang, Jie Cheng, Zhen Xiong, Zhiwei Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9781665445092

Compact video super-resolution (VSR) networks can be easily deployed on resource-limited devices, e.g., smartphones and wearable devices, but have considerable performance gaps compared with complicated VSR networks that require a large amount of computing resources. In this paper, we aim to improve the performance of compact VSR networks without changing their original architectures, through a knowledge distillation approach that transfers knowledge from a complicated VSR network to a compact one. Specifically, we propose a space-time distillation (STD) scheme to exploit both spatial and temporal knowledge in the VSR task. For space distillation, we extract spatial attention maps that hint the high-frequency video content from both networks, which are further used for transferring spatial modeling capabilities. For time distillation, we narrow the performance gap between compact models and complicated models by distilling the feature similarity of the temporal memory cells, which are encoded from the sequence of feature maps generated in the training clips using ConvLSTM. During the training process, STD can be easily incorporated into any network without changing the original network architecture. Experimental results on standard benchmarks demonstrate that, in resource-constrained situations, the proposed method notably improves the performance of existing VSR networks without increasing the inference time.

关键词： Training Performance evaluation Knowledge engineering Wearable computers Superresolution Network architecture 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...

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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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