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Hierarchical Video Prediction using Relational Layouts for Human-Object Interactions

Hierarchical Video Prediction using Relational Layouts for H...

引用

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

作者： Bodla, Navaneeth Shrivastava, Gaurav Chellappa, Rama Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781665445092

Learning to model and predict how humans interact with objects while performing an action is challenging, and most of the existing video prediction models are ineffective in modeling complicated human-object interactions. Our work builds on hierarchical video prediction models, which disentangle the video generation process into two stages: predicting a high-level representation, such as pose sequence, and then learning a pose-to-pixels translation model for pixel generation. An action sequence for a human-object interaction task is typically very complicated, involving the evolution of pose, person's appearance, object locations, and object appearances over time. To this end, we propose a Hierarchical Video Prediction model using Relational Layouts. In the first stage, we learn to predict a sequence of layouts. A layout is a high-level representation of the video containing both pose and objects' information for every frame. The layout sequence is learned by modeling the relationships between the pose and objects using relational reasoning and recurrent neural networks. The layout sequence acts as a strong structure prior to the second stage that learns to map the layouts into pixel space. Experimental evaluation of our method on two datasets, UMD-HOI and Bimanual, shows significant improvements in standard video evaluation metrics such as LPIPS, PSNR, and SSIM. We also perform a detailed qualitative analysis of our model to demonstrate various generalizations.

关键词： Measurement computer vision Recurrent neural networks Computational modeling Layout Predictive models Cognition

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Towards Extremely Compact RNNs for Video recognition with Fully Decomposed Hierarchical Tucker Structure

Towards Extremely Compact RNNs for Video Recognition with Fu...

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

作者： Yin, Miao Liao, Siyu Liu, Xiao-Yang Wang, Xiaodong Yuan, Bo Rutgers State Univ Newark NJ 07101 USA Amazon Seattle WA USA Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781665445092

Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges. Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in the resource-restricted environments is still a very challenging problem. In this paper, we propose to develop extremely compact RNN models with fully decomposed hierarchical Tucker (FDHT) structure. The HT decomposition does not only provide much higher storage cost reduction than the other tensor decomposition approaches, but also brings better accuracy performance improvement for the compact RNN models. Meanwhile, unlike the existing tensor decomposition-based methods that can only decompose the input-to-hidden layer of RNNs, our proposed fully decomposition approach enables the comprehensive compression for the entire RNN models with maintaining very high accuracy. Our experimental results on several popular video recognition datasets show that, our proposed fully decomposed hierarchical tucker-based LSTM (FDHT-LSTM) is extremely compact and highly efficient. To the best of our knowledge, FDHT-LSTM, for the first time, consistently achieves very high accuracy with only few thousand parameters (3,132 to 8,808) on different datasets. Compared with the state-of-the-art compressed RNN models, such as TT-LSTM, TR-LSTM and BT-LSTM, our FDHT-LSTM simultaneously enjoys both order-of-magnitude (3,985x to 10,711x) fewer parameters and significant accuracy improvement (0.6% to 12.7%).

关键词： computer vision Analytical models Tensors Recurrent neural networks Sequences Costs Computational modeling

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M3DSSD: Monocular 3D Single Stage Object Detector

M3DSSD: Monocular 3D Single Stage Object Detector

引用

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

作者： Luo, Shujie Dai, Hang Shao, Ling Ding, Yong Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Univ Sch Micronano Elect Hangzhou Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

In this paper, we propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. Current anchor-based monocular 3D object detection methods suffer from feature mismatching. To overcome this, we propose a two-step feature alignment approach. In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores. In the second step, the center alignment is used to align the features at 2D/3D centers. Further, it is often difficult to learn global information and capture long-range relationships, which are important for the depth prediction of objects. Therefore, we propose a novel asymmetric non-local attention block with multi-scale sampling to extract depth-wise features. The proposed M3DSSD achieves significantly better performance than the monocular 3D object detection methods on the KITH dataset, in both 3D object detection and bird's eye view tasks.

关键词： Solid modeling computer vision Three-dimensional displays Shape Computational modeling Object detection Detectors

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A Closer Look at Self-training for Zero-Label Semantic Segmentation

A Closer Look at Self-training for Zero-Label Semantic Segme...

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

作者： Pastore, Giuseppe Cermelli, Fabio Xian, Yongqin Mancini, Massimiliano Akata, Zeynep Caputo, Barbara Politecn Torino Turin Italy Italian Inst Technol Genoa Italy MPI Informat Saarbrucken Germany Univ Tubingen Tubingen Germany MPI Intelligent Syst Saarbrucken Germany

ISBN: (纸本)9781665448994

Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. However, they are prone to overfitting on the seen classes because there is no training signal for them. In this paper, we study the challenging generalized zero-label semantic segmentation task where the model has to segment both seen and unseen classes at test time. We assume that pixels of unseen classes could be present in the training images but without being annotated. Our idea is to capture the latent information on unseen classes by supervising the model with self-produced pseudo-labels for unlabeled pixels. We propose a consistency regularizer to filter out noisy pseudolabels by taking the intersections of the pseudo-labels generated from different augmentations of the same image. Our framework generates pseudo-labels and then retrain the model with human-annotated and pseudo-labelled data. This procedure is repeated for several iterations. As a result, our approach achieves the new state-of-the-art on PascalVOC12 and COCO-stuff datasets in the challenging generalized zero-label semantic segmentation setting, surpassing other existing methods addressing this task with more complex strategies. Code can be found at https: //***/giuseppepastore10/STRICT.

关键词： Training Image segmentation Semantics Pipelines Predictive models Information filters pattern recognition

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LAFEAT: Piercing Through Adversarial Defenses with Latent Features

LAFEAT: Piercing Through Adversarial Defenses with Latent Fe...

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

作者： Yu, Yunrui Gao, Xitong Xu, Cheng-Zhong Univ Macau Macau Sar Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This presents a great challenge in making CNNs robust against such attacks. An influx of new defense techniques have been proposed to this end. In this paper, we show that latent features in certain "robust" models are surprisingly susceptible to adversarial attacks. On top of this, we introduce a unified l(infinity)-norm white-box attack algorithm which harnesses latent features in its gradient descent steps, namely LAFEAT. We show that not only is it computationally much more efficient for successful attacks, but it is also a stronger adversary than the current state-of-the-art across a wide range of defense mechanisms. This suggests that model robustness could be contingent on the effective use of the defender's hidden components, and it should no longer be viewed from a holistic perspective.

关键词： Degradation Schedules computer vision Computational modeling Perturbation methods Lattices Robustness

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ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning

ECKPN: Explicit Class Knowledge Propagation Network for Tran...

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

作者： Chen, Chaofan Yang, Xiaoshan Xu, Changsheng Huang, Xuhui Ma, Zhe Univ Sci & Technol China USTC Sch Informat Sci & Technol Hefei Anhui Peoples R China Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR Beijing Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China Second Acad CASIC X Lab Beijing Peoples R China

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Recently, the transductive graph-based methods have achieved great success in the few-shot classification task. However, most existing methods ignore exploring the class-level knowledge that can be easily learned by humans from just a handful of samples. In this paper, we propose an Explicit Class Knowledge Propagation Network (ECKPN), which is composed of the comparison, squeeze and calibration modules, to address this problem. Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph. Then, we squeeze the instance-level graph to generate the class-level graph, which can help obtain the class-level visual knowledge and facilitate modeling the relations of different classes. Next, the calibration module is adopted to characterize the relations of the classes explicitly to obtain the more discriminative class-level knowledge representations. Finally, we combine the class-level knowledge with the instance-level sample representations to guide the inference of the query samples. We conduct extensive experiments on four few-shot classification benchmarks, and the experimental results show that the proposed ECKPN significantly outperforms the state-of-the art methods.

关键词： Visualization computer vision Art Computational modeling Knowledge representation Benchmark testing Calibration

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Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings

Combining Semantic Guidance and Deep Reinforcement Learning ...

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

作者： Singh, Jaskirat Zheng, Liang Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint", in a manner similar to a human painter. However, the applicability of previous methods has been limited to datasets with little variation in position, scale and saliency of the foreground object. As a consequence, we find that these methods struggle to cover the granularity and diversity possessed by real world images. To this end, we propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time. 2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance. 3) The distinguishing features of the in-focus object are then amplified by maximizing a novel guided backpropagation based focus reward. The proposed agent does not require any supervision on human stroke-data and successfully handles variations in foreground object attributes, thus, producing much higher quality canvases for the CUB-200 Birds [29] and Stanford Cars-196 [17] datasets. Finally, we demonstrate the further efficacy of our method on complex datasets with multiple foreground object instances by evaluating an extension of our method on the challenging Virtual-KITTI [2] dataset. Source code and models are available at https://***/1jsingh/semantic-guidance.

关键词： Backpropagation Training computer vision Semantics Pipelines Reinforcement learning Stroke (medical condition)

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Generalized Foggy-Scene Semantic Segmentation by Frequency Decoupling

Generalized Foggy-Scene Semantic Segmentation by Frequency D...

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

作者： Qi Bi Shaodi You Theo Gevers Computer Vision Research Group University of Amsterdam Amsterdam The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foggy-scene semantic segmentation (FSSS) is highly challenging due to the diverse effects of fog on scene properties and the limited training data. Existing research has mainly focused on domain adaptation for FSSS, which has practical limitations when dealing with new scenes. In our paper, we introduce domain-generalized FSSS, which can work effectively on unknown distributions without extensive training. To address domain gaps, we propose a frequency decoupling (FreD) approach that separates fog-related effects (amplitude) from scene semantics (phase) in feature representations. Our method is compatible with both CNN and vision Transformer backbones and outperforms existing approaches in various scenarios.

关键词： Training computer vision Frequency-domain analysis Semantic segmentation conferences Semantics Training data

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Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

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

作者： Octavio Arriaga Sebastian Palacio Matias Valdenegro-Toro University of Bremen German Research Center for Artificial Intelligence University of Groningen

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

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Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings

Enriching ImageNet with Human Similarity Judgments and Psych...

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

作者： Roads, Brett D. Love, Bradley C. UCL Dept Expt Psychol London England

ISBN: (纸本)9781665445092

Advances in supervised learning approaches to object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. However, these benchmarks-such as ILSVRC-are relatively task-specific, focusing predominately on predicting class labels. We introduce a publicly-available dataset that embodies the task-general capabilities of human perception and reasoning. The Human Similarity Judgments extension to ImageNet (ImageNet-HSJ) is composed of a large set of human similarity judgments that supplements the existing ILSVRC validation set. The new dataset supports a range of task and performance metrics, including evaluation of unsupervised algorithms. We demonstrate two methods of assessment: using the similarity judgments directly and using a psychological embedding trained on the similarity judgments. This embedding space contains an order of magnitude more points (i.e., images) than previous efforts based on human judgments. We were able to scale to the MI 50,000 image ILSVRC validation set through a selective sampling process that used variational Bayesian inference and model ensembles to sample aspects of the embedding space that were most uncertain. To demonstrate the utility of ImageNet-HSJ, we used the similarity ratings and the embedding space to evaluate how well several popular models conform to human similarity judgments. One finding is that the more complex models that perform better on task-specific benchmarks do not better conform to human semantic judgments. In addition to the human similarity judgments, pre-trained psychological embeddings and code for inferring variational embeddings are made publicly available. ImageNet-HSJ supports the appraisal of internal representations and the development of more humanlike models.

关键词： Measurement computer vision Supervised learning Semantics Psychology Focusing Benchmark testing

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