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Learning Contextual Causality between Daily Events from Time-consecutive Images

Learning Contextual Causality between Daily Events from Time...

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

作者： Zhang, Hongming Huo, Yintong Zhao, Xinran Song, Yangqiu Roth, Dan HKUST Hong Kong Peoples R China CUHK Hong Kong Peoples R China UPenn Philadelphia PA 19104 USA

ISBN: (纸本)9781665448994

Conventional textual-based causal knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causal knowledge records (e.g., ConceptNet) typically do not consider the context. In this paper, we move out of the textual domain to explore a more scalable way of acquiring causal knowledge and investigate the possibility of learning contextual causality from the visual signal. Specifically, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representations, it is possible to discover meaningful causal knowledge from the videos. Further analysis also shows that the contextual property of causal relations indeed exists and considering the contextual property can help better predict the causal relation between events. The Vis-Causal dataset and experiment code are available at https://***/HKUST-KnowComp/Vis_Causal.

关键词： Training Visualization computer vision Annotations Knowledge acquisition conferences pattern recognition

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Learning A Cascaded Non-Local Residual Network for Super-resolving Blurry Images

Learning A Cascaded Non-Local Residual Network for Super-res...

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

作者： Bai, Haoran Cheng, Songsheng Tang, Jinhui Pan, Jinshan Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665448994

Deblurring low-resolution images is quite challenging as blur exists in the images and the resolution of the images is low. Existing deblurring methods usually require high-resolution input while the super-resolution methods usually assume that the blur is known or small. Simply applying the deblurring and super-resolution does not solve this problem well. In this paper, we develop an effective cascaded non-local residual network which cascades the deblurring module and super-resolution module to estimate latent high-resolution images from blurry low-resolution ones. The network first uses the deblurring module to generate intermediate clear features and then develops a non-local residual network (NLRN) as the super-resolution module to generate clear high-resolution images from the intermediate clear features. To better constrain the network and reduce the training difficulty, we develop an effective constraint based on image gradients for edge preservation and adopt the progressive upsampling mechanism. We train the proposed network in an end-to-end manner. Both quantitative and qualitative results on the benchmarks demonstrate the effectiveness of the proposed method. Moreover, the proposed method achieves top-3 performance on the low-resolution track of the NTIRE 2021 Image Deblurring Challenge.

关键词： Training computer vision Image edge detection conferences Superresolution Benchmark testing Image restoration

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Ternary Feature Masks: zero-forgetting for task-incremental learning

Ternary Feature Masks: zero-forgetting for task-incremental ...

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

作者： Masana, Marc Tuytelaars, Tinne Van de Weijer, Joost Comp Vis Ctr Barcelona Spain Katholieke Univ Leuven ESAT PSI Leuven Belgium

ISBN: (纸本)9781665448994

We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue - and show experimentally - that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task;with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches.

关键词： computer vision Scalability conferences Encoding pattern recognition Computational efficiency Task analysis

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An Empirical Study of Vehicle Re-Identification on the AI City Challenge

An Empirical Study of Vehicle Re-Identification on the AI Ci...

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

作者： Luo, Hao Chen, Weihua Xu, Xianzhe Gu, Jianyang Zhang, Yuqi Liu, Chong Jiang, Yiqi He, Shuting Wang, Fan Li, Hao Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, i.e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge. (1) Both cropping training data and using synthetic data can help the model learn more discriminative features. (2) Since there is a new scenario in the test set that dose not appear in the training set, UDA methods perform well in the challenge. (3) Post-processing techniques including re-ranking, image-to-track retrieval, inter-camera fusion, etc, significantly improve final performance. (4) We ensemble CNN-based models and transformer-based models which provide different representation diversity. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition. Codes are available at https://***/michuanhaohao/AICITY2021_Track2_DMT.

关键词： Training computer vision conferences Computational modeling Urban areas Training data Data models

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Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation

Efficient Pre-trained Features and Recurrent Pseudo-Labeling...

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

作者： Zhang, Youshan Davison, Brian D. Lehigh Univ Comp Sci & Engn Bethlehem PA 18015 USA

ISBN: (纸本)9781665448994

Domain adaptation (DA) mitigates the domain shift problem when transferring knowledge from one annotated domain to another similar but different unlabeled domain. However, existing models often utilize one of the ImageNet models as the backbone without exploring others, and fine-tuning or retraining the backbone ImageNet model is also time-consuming. Moreover, pseudo-labeling has been used to improve the performance in the target domain, while how to generate confident pseudo labels and explicitly align domain distributions has not been well addressed. In this paper, we show how to efficiently opt for the best pre-trained features from seventeen well-known ImageNet models in unsupervised DA problems. In addition, we propose a recurrent pseudo-labeling model using the best pre-trained features (termed PRPL) to improve classification performance. To show the effectiveness of PRPL, we evaluate it on three benchmark datasets, Office+Caltech-10, Office31, and Office-Home. Extensive experiments show that our model reduces computation time and boosts the mean accuracy to 98.1%, 92.4%, and 81.2%, respectively, substantially outperforming the state of the art.

关键词： Training Adaptation models computer vision Computational modeling conferences Optimized production technology Benchmark testing

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Connecting Language and vision for Natural Language-Based Vehicle Retrieval

Connecting Language and Vision for Natural Language-Based Ve...

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

作者： Bai, Shuai Zheng, Zhedong Wang, Xiaohan Lin, Junyang Zhang, Zhu Zhou, Chang Yang, Hongxia Yang, Yi Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Technol Sydney ReLER Lab Sydney NSW Australia Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Vehicle search is one basic task for the efficient traffic management in terms of the AI City. Most existing practices focus on the image-based vehicle matching, including vehicle re-identification and vehicle tracking. In this paper, we apply one new modality, i.e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario. The natural language-based vehicle search poses one new challenge of fine-grained understanding of both vision and language modalities. To connect language and vision, we propose to jointly train the state-of-the-art vision models with the transformer-based language model in an end-to-end manner. Except for the network structure design and the training strategy, several optimization objectives are also revisited in this work. The qualitative and quantitative experiments verify the effectiveness of the proposed method. Our proposed method has achieved the 1st place on the 5th AI City Challenge, yielding competitive performance 18.69% MRR accuracy on the private test set. We hope this work can pave the way for the future study on using language description effectively and efficiently for real-world vehicle retrieval systems. The code will be available at https://***/ShuaiBai623/AIC2021-T5-CLV.

关键词： Training Smart cities computer architecture Search problems Robustness pattern recognition Task analysis

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Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning

Supervised Contrastive Replay: Revisiting the Nearest Class ...

引用

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

作者： Mai, Zheda Li, Ruiwen Kim, Hyunwoo Sanner, Scott Univ Toronto Toronto ON Canada LG AI Res Seoul South Korea

ISBN: (纸本)9781665448994

Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data while mitigating catastrophic forgetting. While memory replay has shown promising results, the recency bias in online learning caused by the commonly used Softmax classifier remains an unsolved challenge. Although the Nearest-Class-Mean (NCM) classifier is significantly undervalued in the CL community, we demonstrate that it is a simple yet effective substitute for the Softmax classifier. It addresses the recency bias and avoids structural changes in the fully-connected layer for new classes. Moreover, we observe considerable and consistent performance gains when replacing the Softmax classifier with the NCM classifier for several state-of-the-art replay methods. To leverage the NCM classifier more effectively, data embeddings belonging to the same class should be clustered and well-separated from those with a different class label. To this end, we contribute Supervised Contrastive Replay (SCR), which explicitly encourages samples from the same class to cluster tightly in embedding space while pushing those of different classes further apart during replay-based training. Overall, we observe that our proposed SCR substantially reduces catastrophic forgetting and outperforms state-of-the-art CL methods by a significant margin on a variety of datasets.

关键词： Training computer vision conferences Performance gain pattern recognition

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Real-time and Robust System for Counting Movement-Specific Vehicle at Crowded Intersections

Real-time and Robust System for Counting Movement-Specific V...

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

作者： Vu-Hoang Tran Le-Hoai-Hieu Dang Chinh-Nghiep Nguyen Ngoc-Hoang-Lam Le Khanh-Phong Bui Lam-Truong Dam Quang-Thang Le Dinh-Hiep Huynh Ho Chi Minh City Univ Technol & Educ Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

In order to reduce traffic congestion and improve the efficiency of traffic light signals, intelligent traffic systems are being developed by researchers, and vehicle counting is one of the key techniques in the system. The traditional methods mostly focus on increasing the vehicle counting effectiveness without regard to the program execution efficiency. The practical value of these systems will be reduced if they cannot be operated in real-time on compact IoT device. Therefore, in this paper, we mainly focus on designing a real-time and robust system for the problem of counting specific-movement vehicles. The system is able to detect and track objects in the area of interest, then count those tracked trajectories using the movements. To improve performance of tracking multiple objects, a high recall detection method and an efficient feature matching strategy were proposed. Moreover, to minimize the wrong direction of movement prediction and improve the results of vehicle counting, a cosine similarity-based vehicle counting scheme is applied. Experiments are conducted on AI City 2021 Track-1 dataset. Our method is evaluated on both sides of efficiency and effectiveness.

关键词： Performance evaluation computer vision Tracking conferences Urban areas Feature extraction Real-time systems

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Single Image Dehazing Using Bounded Channel Difference Prior

Single Image Dehazing Using Bounded Channel Difference Prior

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

作者： Zhao, Xuan Air To Air Missile Res Inst Luoyang Henan Peoples R China

ISBN: (纸本)9781665448994

The single image dehazing task has made significant progress recently, aiming to recover the contrast and color of the scattered image. Many patch prior based dehazing methods have been explored, and this paper proposes another single image dehazing method by analyzing the prior information of local dehazed patches. With our observation, when the estimated transmission value varies from the ground-truth transmission value to 1, the output value of a metric function decrease correspondingly, which is defined based on the difference maps among three RGB channels of local dehazed patches normalized using global atmospheric light. Under additional bounding, the local transmission value can be estimated accurately. To reduce computation time, the whole image is divided into many small patches, and within each patch, we estimate a transmission value accurately. We further use weighted interpolation and guided filtering to refine the edges and details of the rough transmission map. Finally, we evaluate the proposed method using Fattal's synthetic haze images, SOTS dataset, and a wide variety of real-world haze images. Experiments show that our method outperforms other state-of-the-art dehazing algorithms by a large margin, especially on synthetic noisy haze images.

关键词： Interpolation computer vision Image color analysis Filtering Image edge detection conferences Estimation

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SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data

SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object De...

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

作者： Hu, Yuan-Ting Wang, Jiahong Yeh, Raymond A. Schwing, Alexander G. Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665448994

Extracting detailed 3D information of objects from video data is an important goal for holistic scene understanding. While recent methods have shown impressive results when reconstructing meshes of objects from a single image, results often remain ambiguous as part of the object is unobserved. Moreover, existing image-based datasets for mesh reconstruction don't permit to study models which integrate temporal information. To alleviate both concerns we present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh annotations which extends SAIL-VOS. We also develop first baselines for reconstruction of 3D meshes from video data via temporal models. We demonstrate efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that temporal information improves reconstruction quality. Resources and additional information are available at http://***.

关键词： Solid modeling computer vision Three-dimensional displays Annotations Object detection Data models Data mining

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