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HR-STAN: High-Resolution Spatio-Temporal Attention Network for 3D Human Motion Prediction

HR-STAN: High-Resolution Spatio-Temporal Attention Network f...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Medjaouri, Omar Desai, Kevin Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

3D human motion prediction requires making sense of the complex spatio-temporal dynamics which underpin human motion to make highly accurate predictions. Part of this complexity is due to the trade-off between long-term (>400ms) and short-term predictions (<400ms) which require different levels of granularity to observe patterns. Several works have explored methods of improving long-term prediction performance by utilizing longer motion histories but this typically comes at the cost of very short-term (<200ms) performance. Inspired by high-resolution network architectures, we propose a novel high-resolution spatio-temporal attention network (HR-STAN) which leverages parallel feature branches and dilated convolutions to observe human motion at different scales. Furthermore, we augment this architecture with split spatial and temporal attention mechanisms to efficiently capture spatio-temporal dependencies within a given motion. We evaluate the ability of our HR-STAN architecture at incorporating long-term motion histories while producing short-term predictions and show that it improves over several state-of-the-art methods on both the AMASS and Human3.6M benchmarks.

关键词： computer vision Three-dimensional displays Costs conferences Dynamics computer architecture Predictive models

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Using Pure Pollen Species When Training a CNN to Segment Pollen Mixtures

Using Pure Pollen Species When Training a CNN to Segment Pol...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Nana Joos, Victor Jacquemart, A. L. Buyens, Christel De Vleeschouwer, C. UCLouvain ICTEAM Inst Ottignies Belgium UCLouvain ELI Inst Ottignies Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recognizing the types of pollen grains and estimating their proportion in pollen mixture samples collected in a specific geographical area is important for agricultural, medical, and ecosystem research. Our paper adopts a convolutional neural network for the automatic segmentation of pollen species in microscopy images, and proposes an original strategy to train such network at reasonable manual annotation cost. Our approach is founded on a large dataset composed of pure pollen images. It first (semi-)manually segments foreground, i.e. pollen grains, and background in a fraction of those images, and use the resulting annotated dataset to train a universal pollen segmentation CNN. In the second step, this model is used to automatically segment a large number of additional pure pollen images, so as to supervise the training of a pollen species segmentation model. Despite the fact that it has been trained from pure images only, the model is shown to provide accurate segmentation of species in pollen mixtures. Our experiments also demonstrate that dedicating a model to the segmentation of a subset of the available pure pollen species makes it possible to train a bin pollen class, corresponding to pollen species that are not in the subset of species recognized by the model. This strategy is useful to cope with unexpected species in a mixture.

关键词： Training Image segmentation computer vision Costs Computational modeling Microscopy pattern recognition

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Facial Expression Classification using Fusion of Deep Neural Network in Video

Facial Expression Classification using Fusion of Deep Neural...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kim Ngan Phan Hong-Hai Nguyen Van-Thong Huynh Kim, Soo-Hyung Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

For computers to recognize human emotions, expression classification is an equally important problem in the human-computer interaction area. In the 3rd Affective Behavior Analysis In-The-Wild competition, the task of expression classification includes eight classes with six basic expressions of human faces from videos. In this paper, we employ a transformer mechanism to encode the robust representation from the backbone. Fusion of the robust representations plays an important role in the expression classification task. Our approach achieves 30.35% and 28.60% for the F-1 score on the validation set and the test set, respectively. This result shows the effectiveness of the proposed architecture based on the Aff-Wild2 dataset and our team archives 5th for the expression classification task in the 3rd Affective Behavior Analysis In-The-Wild competition.

关键词： Human computer interaction Emotion recognition Computational modeling Neural networks Transformers Behavioral sciences pattern recognition

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SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics

SpiderNet: Hybrid Differentiable-Evolutionary Architecture S...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Geada, Rob McGough, Andrew Stephen Red Hat Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a variety of well-known problems. However, these algorithms require a variety of design parameters in the form of user configuration or hard-coded decisions which limit the variety of networks that can be discovered. This means that NAS algorithms do not eliminate model design tuning, they instead merely shift the burden of where that tuning needs to be applied. In this paper, we present SpiderNet, a hybrid differentiable-evolutionary and hardware-aware algorithm that rapidly and efficiently produces state-of-the-art networks. More importantly, SpiderNet is a proof-of-concept of a minimally-configured NAS algorithm;the majority of design choices seen in other algorithms are incorporated into SpiderNet's dynamically-evolving search space, minimizing the number of user choices to just two: reduction cell count and initial channel count. SpiderNet produces models highly-competitive with the state-of-the-art, and outperforms random search in accuracy, runtime, memory size, and parameter count.

关键词： Measurement Runtime Heuristic algorithms Microprocessors Neural networks computer architecture Manuals

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A Categorized Reflection Removal Dataset with Diverse Real-world Scenes

A Categorized Reflection Removal Dataset with Diverse Real-w...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lei, Chenyang Huang, Xuhua Qi, Chenyang Zhao, Yankun Sun, Wenxiu Yan, Qiong Chen, Qifeng HKUST Hong Kong Peoples R China CMU Pittsburgh PA USA SenseTime Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly. Furthermore, existing real-world benchmarks and datasets do not categorize image data based on the types and appearances of reflection (e.g., smoothness, intensity), making it hard to analyze reflection removal methods. Hence, we construct a new reflection removal dataset that is categorized, diverse, and real-world (CDR). A pipeline based on RAW data is used to capture perfectly aligned input images and transmission images. The dataset is constructed using diverse glass types under various environments to ensure diversity. By analyzing several reflection removal methods and conducting extensive experiments on our dataset, we show that state-of-the-art reflection removal methods generally perform well on blurry reflection but fail in obtaining satisfying performance on other types of real-world reflection. We believe our dataset can help develop novel methods to remove real-world reflection better.

关键词： computer vision conferences Pipelines Glass Benchmark testing Reflection pattern recognition

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An Empirical study of Data-Free Quantization's Tuning Robustness

An Empirical study of Data-Free Quantization's Tuning Robust...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Hong Wen, Yuxuan Ding, Yifu Yang, Zhen Guo, Yufei Qin, Haotong Beihang Univ Beijing Peoples R China Shanghai Aerosp Elect Technol Inst Shanghai Peoples R China China Aerosp Sci & Ind Corp Acad 2 Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep convolutional neural networks are now performing increasingly superior in various fields, while the network parameters are getting massive as the advanced neural networks tend to be deeper. Among various model compression methods, quantization is one of the most potent approaches to compress neural networks by compacting model weights and activations to lower bit-width. The data-free quantization method is also proposed, which is specialized for some privacy and security scenarios and enables quantization without access to real data. In this work, we find that the tuning robustness of existing data-free quantization is flawed, progressing an empirical study and determining some hyperparameter settings that can converge the model stably in the data-free quantization process. Our study aims to evaluate the overall tuning robustness of the current data-free quantization system, which is existing methods are significantly affected by parameter fluctuations in tuning. We also expect data-free quantification methods with tuning robustness to appear in the future.

关键词： Data privacy Quantization (signal) Fluctuations conferences Neural networks Robustness pattern recognition

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Few-Shot Supervised Prototype Alignment for Pedestrian Detection on Fisheye Images

Few-Shot Supervised Prototype Alignment for Pedestrian Detec...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wiedemer, Thaddaeus Wolf, Stefan Schumann, Arne Ma, Kaisheng Beyerer, Juergen Tsinghua Univ Beijing Peoples R China Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Germany

ISBN: (纸本)9781665487399

Complete and pre-trained models are readily available for download for object detection and can perform well on datasets containing everyday images. Domain adaptation is used to transfer models to more specific datasets with characteristics not present in pre-training. We propose the novel adaptation setting of pedestrian detection in fisheye images, where target samples are scarce but annotated. Our setting provides interesting new challenges for adaptation due to global perspective changes and geometric distortions not found in existing adaptation tasks. To this end, we introduce loss coupling for unsupervised adversarial adaptation and boost prototype-based adaptation with ground-truth information. We additionally propose a novel supervised adaptation head for features in the bounding box regressor. Our method leads to more stable adversarial training and outperforms supervised and unsupervised baselines. Our method requires half the amount of training samples for small datasets to achieve the same performance as supervised fine-tuning.

关键词： Training Couplings Adaptation models Schedules Surveillance Prototypes computer architecture

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Semi-Supervised Few-Shot Learning from A Dependency-Discriminant Perspective

Semi-Supervised Few-Shot Learning from A Dependency-Discrimi...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hou, Zejiang Kung, Sun-Yuan Princeton Univ Princeton NJ 08544 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We study the few-shot learning (FSL) problem, where a model learns to recognize new objects with extremely few labeled training data per category. Most of previous FSL approaches resort to the meta-learning paradigm, where the model accumulates inductive bias through learning from many training tasks, in order to solve new unseen few-shot tasks. In contrast, we propose a simple semi-supervised FSL approach to exploit unlabeled data accompanying the few-shot task to improve FSL performance. More exactly, to train a classifier, we propose a Dependency Maximization loss based on the Hilbert-Schmidt norm of the cross-covariance operator, which maximizes the statistical dependency between the embedded feature of the unlabeled data and their label predictions, together with the supervised loss over the support set. The obtained classifier is used to infer the pseudo-labels of the unlabeled data. Furthermore, we propose an Instance Discriminant Analysis to evaluate the credibility of the pseudo-labeled examples and select the faithful ones into an augmented support set, which is used to retrain the classifier. We iterate the process until the pseudo-labels of the unlabeled data becomes stable. Through extensive experiments on four widely used few-shot classification benchmarks, including mini-ImageNet, tiered-ImageNet, CUB, and CIFARFS, the proposed method outperforms previous state-of-the-art FSL methods.

关键词： Training computer vision conferences Training data Benchmark testing Data models pattern recognition

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Multi-Dimensional vision Transformer Compression via Dependency Guided Gaussian Process Search

Multi-Dimensional Vision Transformer Compression via Depende...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hou, Zejiang Kung, Sun-Yuan Princeton Univ Princeton NJ 08544 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

vision transformers (ViT) have recently attracted considerable attentions, but the huge computational cost remains an issue for practical deployment. Previous ViT pruning methods tend to prune the model along one dimension solely, which may suffer from excessive reduction and lead to sub-optimal model quality. In contrast, we advocate a multi-dimensional ViT compression paradigm, and propose to harness the redundancy reduction from attention head, neuron and sequence dimensions jointly. Firstly, we propose a statistical dependence based pruning criterion that is generalizable to different dimensions for identifying the deleterious components. Moreover, we cast the multi-dimensional ViT compression as an optimization problem, objective of which is to learn an optimal pruning policy across the three dimensions while maximizing the compressed model's accuracy under a computational budget. The problem is solved by an adapted Gaussian process search with expected improvement. Experimental results show that our method effectively reduces the computational cost of various ViT models. For example, our method reduces 40% FLOPs without top-1 accuracy loss for DeiT and T2T-ViT models on the ImageNet dataset, outperforming previous state-of-the-art ViT pruning methods.

关键词： Adaptation models Image coding Head Computational modeling Neurons Gaussian processes Transformers

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Faster, Lighter, Robuster: A Weakly-Supervised Crowd Analysis Enhancement Network and A Generic Feature Extraction Framework

Faster, Lighter, Robuster: A Weakly-Supervised Crowd Analysi...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Shaokai Liu, Zhaogeng Pei, Wencheng Hong, Jianbo Li, Zhanshan Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Sch Artificial Intelligence Changchun Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With bounding box labels needed for training, object detection is viewed unfavorably in terms of crowd analysis, due to the intensive labor for labeling and the unsatisfactory performance in clutters and severe occlusions. Another feasible method, density-based regression, despite its proficiency in counting and only point-level labels used for training, cannot get the location of each person, and the time and space consumption is relatively high. In this paper, we propose a generic feature extraction framework, Adaptive Pyramid Score (APS), based on object detection and designed specifically for extracting quantitative and spatial-semantic features. Moreover, as an intuitive and feasible solution regarding crowd analysis, we propose the weakly-supervised Confidence-Threshold-Foresight Network (CTFNet) under our APS feature extraction framework, which only needs count-level labels for training and improves the performance of various methods dramatically. Our system realizes the triple enhancement of counting, localization, and detection, which is also proved to be faster than advanced crowd analysis methods, lighter to be transplanted to various object detection methods, and robuster to tackle tasks of extreme scenes. Furthermore, the weakly-supervised paradigm leverage the intensive labor for labeling profoundly.

关键词： Training Location awareness computer vision conferences Object detection Feature extraction pattern recognition

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