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Weakly Supervised Visual Question Answer Generation

Weakly Supervised Visual Question Answer Generation

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Alampalle, Charani Hegde, Shamanthak Jahagirdar, Soumya Gangisetty, Shankar AlphaICs Bengaluru India Kle Technological University Hubballi India Cvit Iiit Hyderabad Hyderabad India Iiit Hyderabad Hyderabad India

ISBN: (纸本)9798350302493

Growing interest in conversational agents promote two-way human-computer communications involving asking and answering visual questions have become an active area of research in AI. Thus, generation of visual question-answer pair(s) becomes an important and challenging task. To address this issue, we propose a weakly-supervised visual question answer generation method that generates a relevant question-answer pairs for a given input image and associated caption. Most of the prior works are supervised and depend on the annotated question-answer datasets. In our work, we present a weakly supervised method that synthetically generates question-answer pairs procedurally from visual information and captions. The proposed method initially extracts list of answer words, then does nearest question generation that uses the caption and answer word to generate synthetic question. Next, the relevant question generator converts the nearest question to relevant language question by dependency parsing and in-order tree traversal, finally, fine-tune a ViLBERT model with the question-answer pair(s) generated at end. We perform an exhaustive experimental analysis on VQA dataset and see that our model significantly outperform SOTA methods on BLEU scores. We also show the results wrt baseline models and ablation study. © 2023 ieee.

关键词： computer vision

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Exploring Joint Embedding Architectures and Data Augmentations for Self-Supervised Representation Learning in Event-Based vision

Exploring Joint Embedding Architectures and Data Augmentatio...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Barchid, Sami Mennesson, José Djéraba, Chaabane Univ. Lille Cnrs Centrale Lille Umr 9189 CRIStAL France Univ. Lille Centre for Digital Systems Imt Nord Europe Institut Mines-Télécom LilleF-59000 France

ISBN: (纸本)9798350302493

This paper proposes a self-supervised representation learning (SSRL) framework for event-based vision, which leverages various lightweight convolutional neural networks (CNNs) including 2D-, 3D-, and Spiking CNNs. The method uses a joint embedding architecture to maximize the agreement between features extracted from different views of the same event sequence. Popular event data augmentation techniques are employed to design an efficient augmentation policy for event-based SSRL, and we provide novel data augmentation methods to enhance the pretraining pipeline. Given the novelty of SSRL for event-based vision, we elaborate standard evaluation protocols and use them to evaluate our approach. Our study demonstrates that pretrained CNNs acquire effective and transferable features, enabling them to achieve competitive performance in object or action recognition across various commonly used event-based datasets, even in a low-data regime. This paper also conducts an experimental analysis of the extracted features regarding the Uniformity-Tolerance tradeoff to assess their quality, and measure the similarity of representations using linear Center Kernel Alignement. These quantitative measurements reinforce our observations from the performance benchmarks and show substantial differences between the learned representations of all types of CNNs despite being optimized with the same approach. © 2023 ieee.

关键词： Embeddings

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Continuous Hand Gesture recognition for Human-Robot Collaborative Assembly

Continuous Hand Gesture Recognition for Human-Robot Collabor...

引用

ieee/cvf International conference on computer vision (ICCV)

作者： Kwolek, Bogdan AGH Univ Krakow 30 Mickiewicza Av PL-30059 Krakow Poland

ISBN: (纸本)9798350307443

In this work, we present a framework for dynamic hand gesture recognition on RGB images acquired by an overhead camera. The recognition is realized for Methods Time Measurement-based planning of human-robot collaborative workspace. The 3D hand posture is estimated by MediaPipe. The recognition is done by a neural network in which a layer-wise feature combination takes place. We combine features extracted by basic blocks of Spatio-Temporal Adaptive Graph Convolutional Neural Network and by basic spatio-temporal self-attention blocks. We recorded and manually annotated 12 videos consisting of 54,659 RGB images with five basic motion sequences: grasp, move, position, release, and reach. We demonstrate experimentally that results of our networks are superior to results achieved by RNNs, ST-GCN, ST-AGCN, and CTR-GCN networks.

关键词： Assistive computer vision Continuous Hand Gesture recognition Self Attention ST GCN

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Privacy Leakage of Adversarial Training Models in Federated Learning Systems

Privacy Leakage of Adversarial Training Models in Federated ...

引用

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

作者： Zhang, Jingyang Chen, Yiran Li, Hai Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users' private training images even when the training batch size is large. Code is available at https://***/zjysteven/PrivayAttack_AT_FL.

关键词： Training Deep learning Privacy computer vision conferences Neural networks Collaborative work

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Where are they looking in the 3D space?

Where are they looking in the 3D space?

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Horanyi, Nora Zheng, Linfang Chong, Eunji Leonardis, Aleš Chang, Hyung Jin University of Birmingham School of Computer Science United Kingdom *** Inc.

ISBN: (纸本)9798350302493

We propose a novel depth-aware joint attention target estimation framework that estimates the attention target in 3D space. Our goal is to mimic human's ability to understand where each person is looking in their proximity. In this work, we tackle the previously unexplored problem of utilising a depth prior along with a 3D joint FOV probability map to estimate the joint attention target of people in the scene. We leverage the insight that besides the 2D image content, strong gaze-related constraints exist in the depth order of the scene and different subject-specific attributes. Extensive experiments show that our method outperforms favourably against existing joint attention target estimation methods on the VideoCoAtt benchmark dataset. Despite the proposed framework being designed for joint attention target estimation, we show that it outperforms single attention target estimation methods on both the GazeFollow image and the VideoAttentionTarget video benchmark datasets. © 2023 ieee.

关键词： computer vision

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The Manifold Hypothesis for Gradient-Based Explanations

The Manifold Hypothesis for Gradient-Based Explanations

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Bordt, Sebastian Upadhyay, Uddeshya Akata, Zeynep Von Luxburg, Ulrike University of Tübingen Germany Tübingen Ai Center Germany

ISBN: (纸本)9798350302493

When do gradient-based explanation algorithms provide perceptually-aligned explanations? We propose a criterion: the feature attributions need to be aligned with the tangent space of the data manifold. To provide evidence for this hypothesis, we introduce a framework based on variational autoencoders that allows to estimate and generate image manifolds. Through experiments across a range of different datasets - MNIST, EMNIST, CIFAR10, X-ray pneumonia and Diabetic Retinopathy detection - we demonstrate that the more a feature attribution is aligned with the tangent space of the data, the more perceptually-aligned it tends to be. We then show that the attributions provided by popular post-hoc methods such as Integrated Gradients and SmoothGrad are more strongly aligned with the data manifold than the raw gradient. Adversarial training also improves the alignment of model gradients with the data manifold. As a consequence, we suggest that explanation algorithms should actively strive to align their explanations with the data manifold. An extended version of this paper is available at https://***/abs/2206.07387. © 2023 ieee.

关键词： computer vision

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Dense Multitask Learning to Reconfigure Comics

Dense Multitask Learning to Reconfigure Comics

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Bhattacharjee, Deblina Susstrunk, Sabine Salzmann, Mathieu Epfl School of Computer and Communication Sciences Switzerland

ISBN: (纸本)9798350302493

In this paper, we develop a MultiTask Learning (MTL) model to achieve dense predictions for comics panels to, in turn, facilitate the transfer of comics from one publication channel to another by assisting authors in the task of reconfiguring their narratives. Our MTL method can successfully identify the semantic units as well as the embedded notion of 3D in comics panels. This is a significantly challenging problem because comics comprise disparate artistic styles, illustrations, layouts, and object scales that depend on the author's creative process. Typically, dense image-based prediction techniques require a large corpus of data. Finding an automated solution for dense prediction in the comics domain, therefore, becomes more difficult with the lack of ground-truth dense annotations for the comics images. To address these challenges, we develop the following solutions- we leverage a commonly-used strategy known as unsupervised image-to-image translation, which allows us to utilize a large corpus of real-world annotations;- we utilize the results of the translations to develop our multitasking approach that is based on a vision transformer backbone and a domain transferable attention module;-we study the feasibility of integrating our MTL dense-prediction method with an existing retargeting method, thereby reconfiguring comics. © 2023 ieee.

关键词： Forecasting

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350302493

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5. © 2023 ieee.

关键词： Graphic methods

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A Temporal Encoder-Decoder Approach to Extracting Blood Volume Pulse Signal Morphology from Face Videos

A Temporal Encoder-Decoder Approach to Extracting Blood Volu...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Li, Fulan Thapa, Surendrabikram Bhat, Shreyas Sarkar, Abhijit Abbott, A. Lynn Virginia Tech Bradley Department of Electrical and Computer Engineering United States Virginia Tech Department of Computer Science United States Virginia Tech Transportation Institute United States

ISBN: (纸本)9798350302493

This paper considers methods for extracting blood volume pulse (BVP) representations from video of the human face. Whereas most previous systems have been concerned with estimating vital signs such as average heart rate, this paper addresses the more difficult problem of recovering BVP signal morphology. We present a new approach that is inspired by temporal encoder-decoder architectures that have been used for audio signal separation. As input, this system accepts a temporal sequence of RGB (red, green, blue) values that have been spatially averaged over a small portion of the face. The output of the system is a temporal sequence that approximates a BVP signal. In order to reduce noise in the recovered signal, a separate processing step extracts individual pulses and performs normalization and outlier removal. After these steps, individual pulse shapes have been extracted that are sufficiently distinct to support biometric authentication. Our findings demonstrate the effectiveness of our approach in extracting BVP signal morphology from facial videos, which presents exciting opportunities for further research in this area. The source code is available at https://***/Adleof/CVPM-2023-Temporal-Encoder-Decoder-iPPG. © 2023 ieee.

关键词： Morphology

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Unsupervised Automatic Defect Inspection based on Image Matching and Local One-class Classification

Unsupervised Automatic Defect Inspection based on Image Matc...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Lv, Chengkan Zhang, Zhengtao Shen, Fei Zhang, Feng Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences The School of Artificial Intelligence Beijing China CASI Vision Technology Co. Ltd. Luoyang China

ISBN: (纸本)9798350302493

In this paper, an unsupervised defect inspection method based on anomaly detection is proposed to inspect various kinds of surface defects in the field of industrial production. This method consists of two modules: (i) An image matching module is utilized to align the input image with a pre-specified template image. Specifically, all objects to be detected will be adjusted to the same position and angle. The aligned images can reduce the difficulty of the training stage, facilitating the subsequent feature extraction and anomaly localization. (ii) After the image matching procedure, an anomaly localization module is trained to learn a mapping that concentrates normal samples in feature space. In particular, each local image region is assigned a feature center by adopting a feature map as the mapping target. Therefore, the compactness of the features extracted from the same region can be improved, which is beneficial to detect potential anomalous targets. Moreover, various artificial defective images are synthesized during the training stage to further improve the discriminatory ability of the anomaly localization module. A series of experiments are conducted on MAD dataset and the industrial production line. The experimental results verify the efficiency and versatility of the proposed method. © 2023 ieee.

关键词： Inspection

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