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VATUN: visual Analytics for Testing and Understanding Convolutional Neural Networks

VATUN: Visual Analytics for Testing and Understanding Convol...

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23rd Eurographics conference on visualization, EuroVis 2021

作者： Park, Cheonbok Yang, Soyoung Na, Inyoup Chung, Sunghyo Shin, Sungbok Kwon, Bum Chul Park, Deokgun Choo, Jaegul NAVER Corp KAIST Skelter Labs Kakao Corp University of Maryland United States IBM Research University of Texas Arlington United States

ISBN: (纸本)9783038681434

Convolutional neural networks (CNNs) are popularly used in a wide range of applications, such as computer vision, natural language processing, and human-computer interaction. However, testing and understanding a trained model is difficult and very time-consuming. This is because their inner mechanisms are often considered as a 'black-box' due to difficulty in understanding the causal relationships between processes and results. To help the testing and understanding of such models, we present a user-interactive visual analytics system, VATUN, to analyze a CNN-based image classification model. Users can accomplish the following four tasks in our integrated system: (1) detect data instances in which the model confuses classification, (2) compare outcomes of the model by manipulating the conditions of the image, (3) understand reasons for the prediction of the model by highlighting highly influential parts from the image, and (4) analyze the overall what-if scenarios when augmenting the instances for each class. Moreover, by combining multiple techniques, our system lets users analyze behavior of the model from various perspectives. We conduct a user study of an image classification scenario with three domain experts. Our study will contribute to reducing the time cost for testing and understanding the CNN-based models in several industrial areas. © 2021 Eurographics Association. All rights reserved.

关键词： image classification

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TLRM: Task-level Relation Module for GNN-based Few-Shot Learning

TLRM: Task-level Relation Module for GNN-based Few-Shot Lear...

引用

IEEE visual communications and image processing (VCIP)

作者： Yurong Guo Zhanyu Ma Xiaoxu Li Yuan Dong Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China Beijing Academy of Artificial Intelligence Beijing China Lanzhou University of Technology Lanzhou China

ISBN: (纸本)9781728173221

Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classification problem, which aims at classifying unseen samples when trained with limited labeled samples per class. GNN-based few-shot learning architectures mostly replace traditional metric with a learnable GNN. In the GNN, the nodes are set as the samples' embedding, and the relationship between two connected nodes can be obtained by a network, the input of which is the difference of their embedding features. We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks. That is, this method of measuring relation does not take the task-level information into account. To this end, we propose a new relation measure method, namely the task-level relation module (TLRM), to explicitly model the task-level relation of one sample to all the others. The proposed module captures the relation representations between nodes by considering the sample-to-task instead of sample-to-sample embedding features. We conducted extensive experiments on four benchmark datasets: mini-imageNet, tiered-imageNet, CUB-200-2011, and CIFAR-FS. Experimental results demonstrate that the proposed module is effective for GNN-based few-shot learning.

关键词： Measurement visual communication image processing Benchmark testing Graph neural networks Task analysis

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MultiTune: Adaptive Integration of Multiple Fine-Tuning Models for image Classification 27th

MultiTune: Adaptive Integration of Multiple Fine-Tuning Mode...

引用

27th International conference on Neural Information processing

作者： Wang, Yu Plested, Jo Gedeon, Tom Australian Natl Univ Res Sch Comp Sci Canberra Australia

ISBN: (纸本)9783030638191;9783030638207

Transfer learning has been widely used as a deep learning technique to solve computer vision related problems, especially when the problem is image classification employing Convolutional Neural Networks (CNN). In this paper, a novel transfer learning approach that can adaptively integrate multiple models with different fine-tuning settings is proposed, which is denoted as MultiTune. To evaluate the performance of MultiTune, we compare it to SpotTune, a state-of-the-art transfer learning technique. Two image datasets from the visual Decathlon Challenge are used to evaluate the performance of MultiTune. The FGVC-Aircraft dataset is a fine-grained task and the CIFAR100 dataset is a more general task. Results obtained in this paper show that MultiTune outperforms SpotTune on both tasks. We also evaluate MultiTune on a range of target datasets with smaller numbers of images per class. MultiTune outperforms SpotTune on most of these smaller-sized datasets as well. MultiTune is also less computational than SpotTune and requires less time for training for each dataset used in this paper.

关键词： Transfer learning image classification Convolutional neural networks

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Object-Based Resolution Selection for Efficient Edge-Assisted Multi-Task Video Analytics

Object-Based Resolution Selection for Efficient Edge-Assiste...

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GLOBECOM 2022 - 2022 IEEE Global communications conference

作者： Chengzhi Wang Peng Yang Jie Lin Wen Wu Ning Zhang School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Peng Cheng Laboratory Frontier Research Center Shenzhen China Department of Electrical and Computer Engineering University of Windsor Windsor ON Canada

ISBN: (纸本)9781665435413

Camera-based monitoring is becoming increasingly popular, as multi-objective detection tasks can be enabled by video analytics over captured frames. Yet, video frames have to be delivered to computation-capable edge nodes for further processing, because the amount of required resources exceeds the capacity of built-in hardware of video cameras. In this paper, observing that video resolution directly determines the subsequent bandwidth and computing resource consumption, as well as the analytic accuracy, we propose an edge-assisted object-based resolution configuration algorithm to achieve efficient multi-task video analytics. The proposed algorithm harnesses the diversity of neural networks used for detecting different objects in one frame, which brings about two-fold possibility for bandwidth saving. On one hand, background information cannot be indiscriminately transmitted, as is unlikely to contribute to improving the analytics accuracy. On the other hand, fine-grained resolution selection allows object-level optimal resolution that minimizes the transmitted data volume under accuracy and latency constraints. Simulation results demonstrate that the proposed method can effectively reduce up to 50% of the transmitted data volume, compared to existing benchmarks.

关键词： Sensitivity visual analytics Simulation image edge detection Neural networks Bandwidth Multitasking

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NeCH: Neural Clothed Human Model

NeCH: Neural Clothed Human Model

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IEEE visual communications and image processing (VCIP)

作者： Sheng Liu Liangchen Song Yi Xu Junsong Yuan

ISBN: (纸本)9781728173221

Existing human models, e.g., SMPL and STAR, represent 3D geometry of a human body in the form of a polygon mesh obtained by deforming a template mesh according to a set of shape and pose parameters. The appearance, however, is not directly modeled by most existing human models. We present a novel 3D human model that faithfully models both the 3D geometry and the appearance of a clothed human body with a continuous volumetric representation, i.e., volume densities and emitted colors of continuous 3D locations in the volume encompassing the human body. In contrast to the mesh-based representation whose resolution is limited by a mesh's fixed number of polygons, our volumetric representation does not limit the resolution of our model. Moreover, our volumetric represen-tation can be rendered via differentiable volume rendering, thus enabling us to train the model only using 2D images (without using ground truth 3D geometries of human bodies) by minimizing a loss function which measures the differences between rendered images and ground truth images. On the contrary, existing human models are trained using ground truth 3D geometries of human bodies. Thanks to the ability of our model to jointly model both the geometries and the appearances of clothed people, our model can benefit applications including human image synthesis, gaming and 3D television and telepresence.

关键词： Geometry Video coding Solid modeling Three-dimensional displays image resolution Biological system modeling Volume measurement

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A Multi-dimensional Aesthetic Quality Assessment Model for Mobile Game images

A Multi-dimensional Aesthetic Quality Assessment Model for M...

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IEEE visual communications and image processing (VCIP)

作者： Tao Wang Wei Sun Xiongkuo Min Wei Lu Zicheng Zhang Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

With the development of the game industry and the popularization of mobile devices, mobile games have played an important role in people's entertainment life. The aesthetic quality of mobile game images determines the users' Quality of Experience (QoE) to a certain extent. In this paper, we propose a multi-task deep learning based method to evaluate the aesthetic quality of mobile game images in multiple dimensions (i.e. the fineness, color harmony, colorfulness, and overall quality). Specifically, we first extract the quality-aware feature representation through integrating the features from all intermediate layers of the convolution neural network (CNN) and then map these quality-aware features into the quality score space in each dimension via the quality regressor module, which consists of three fully connected (FC) layers. The proposed model is trained through a multi-task learning manner, where the quality-aware features are shared by different quality dimension prediction tasks, and the multi-dimensional quality scores of each image are regressed by multiple quality regression modules respectively. We further introduce an uncertainty principle to balance the loss of each task in the training stage. The experimental results show that our proposed model achieves the best performance on the Multi-dimensional Aesthetic assessment for Mobile Game image database (MAMG) among state-of-the-art image quality assessment (IQA) algorithms and aesthetic quality assessment (AQA) algorithms.

关键词： Deep learning Uncertainty Games Feature extraction Multitasking Quality assessment Quality of experience

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Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution

Improving Per-title Encoding for HTTP Adaptive Streaming by ...

引用

IEEE visual communications and image processing (VCIP)

作者： Hadi Amirpour Hannaneh Barahouei Pasandi Christian Timmerer Mohammad Ghanbari Christian Doppler Laboratory ATHENA Alpen-Adria-Universität Klagenfurt Austria Virginia Commonwealth University Richmond VA USA School of Computer Science and Electronic Engineering University of Essex Colchester UK

ISBN: (纸本)9781728173221

In per-title encoding, to optimize a bitrate ladder over spatial resolution, each video segment is downscaled to a set of spatial resolutions, and they are all encoded at a given set of bitrates. To find the highest quality resolution for each bitrate, the low-resolution encoded videos are upscaled to the original resolution, and a convex hull is formed based on the scaled qualities. Deep learning-based video super-resolution (VSR) approaches show a significant gain over traditional upscaling approaches, and they are becoming more and more efficient over time. This paper improves the per-title encoding over the upscaling methods by using deep neural network-based VSR algorithms. Utilizing a VSR algorithm by improving the quality of low-resolution encodings can improve the convex hull. As a result, it will lead to an improved bitrate ladder. To avoid bandwidth wastage at perceptually lossless bitrates, a maximum threshold for the quality is set, and encodings beyond it are eliminated from the bitrate ladder. Similarly, a minimum threshold is set to avoid low-quality video delivery. The encodings between the maximum and minimum thresholds are selected based on one Just Noticeable Difference. Our experimental results show that the proposed per-title encoding results in a 24% bitrate reduction and 53% storage reduction compared to the state-of-the-art method.

关键词： image coding visual communication Bit rate Superresolution Bandwidth Streaming media Spatial resolution

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On the Impact of Viewing Distance on Perceived Video Quality

On the Impact of Viewing Distance on Perceived Video Quality

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IEEE visual communications and image processing (VCIP)

作者： Hadi Amirpour Raimund Schatz Christian Timmerer Mohammad Ghanbari Christian Doppler Laboratory ATHENA Alpen-Adria-Universität Klagenfurt Austria AIT Austrian Institute of Technology Austria School of Computer Science and Electronic Engineering University of Essex Colchester UK

ISBN: (纸本)9781728173221

Due to the growing importance of optimizing the quality and efficiency of video streaming delivery, accurate assessment of user-perceived video quality becomes increasingly important. However, due to the wide range of viewing distances encountered in real-world viewing settings, the perceived video quality can vary significantly in everyday viewing situations. In this paper, we investigate and quantify the influence of viewing distance on perceived video quality. A subjective experiment was conducted with full HD sequences at three different fixed viewing distances, with each video sequence being encoded at three different quality levels. Our study results confirm that the viewing distance has a significant influence on the quality assessment. In particular, they show that an increased viewing distance generally leads to increased perceived video quality, especially at low media encoding quality levels. In this context, we also provide an estimation of potential bitrate savings that knowledge of actual viewing distance would enable in practice. Since current objective video quality metrics do not systematically take into account viewing distance, we also analyze and quantify the influence of viewing distance on the correlation between objective and subjective metrics. Our results confirm the need for distance-aware objective metrics when the accurate prediction of perceived video quality in real-world environments is required.

关键词： Measurement image coding visual communication Video sequences Estimation Streaming media Media

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End-to-end Saliency-Guided Deep image Retrieval 27th

End-to-end Saliency-Guided Deep Image Retrieval

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27th International conference on Neural Information processing

作者： Ma, Jinyu Gu, Xiaodong Fudan Univ Dept Elect Engn Shanghai 200433 Peoples R China

ISBN: (纸本)9783030638191;9783030638207

A challenging issue of content-based image retrieval (CBIR) is to distinguish the target object from cluttered backgrounds, resulting in more discriminative image embeddings, compared to situations where feature extraction is distracted by irrelevant objects. To handle the issue, we propose a saliency-guided model with deep image features. The model is fully based on convolution neural networks (CNNs) and it incorporates a visual saliency detection module, making saliency detection a preceding step of feature extraction. The resulted saliency maps are utilized to refine original inputs and then compatible image features suitable for ranking are extracted from refined inputs. The model suggests a working scheme of involving saliency information into existing CNN-based CBIR systems with minimum impacts on the them. Some work assist image retrieval with other methods like object detection or semantic segmentation, but they are not so fine-grained as saliency detection, meanwhile some of them require additional annotations to train. In contrast, we train the saliency module in weak-supervised end-to-end style and do not need saliency ground truth. Extensive experiments are conducted on standard image retrieval benchmarks and our model shows competitive retrieval results.

关键词： Content-based image retrieval Saliency detection Convolutional neural network Weak-supervised

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Food Cuisine Classification by Convolutional Neural Network based Transfer Learning Approach

Food Cuisine Classification by Convolutional Neural Network ...

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Mobile Networks and Wireless communications (ICMNWC), IEEE International conference on

作者： Priyadarshini C. Patil Vishwanath C. Burkapalli P D A College of Engg. Kalaburgi India

ISBN: (纸本)9781665446075

Food image classification is considered as a one of the uplift applications of visual food object recognition in the area of food image processing. Deep learning provides great outcomes in various challenging domains with multiple layers to constitute the inattention of data to build computational models. With this success, many studies have put forward deep-learning-based food image classification models and attained better performances collated with conventional machine learning models. We proposed a deep CNN-based food classification method for food identification with transfer learning and the fine-tuning based on the ResNet and InceptionV3 models. Comparisons of both networks are performed with sixteen and three classes of own Indian food image datasets. Inception V3 achieved more accuracy compared to ResNet-50 when more numbers of food image classes are considered.

关键词： Measurement Wireless communication visualization Computational modeling image processing Transfer learning Convolutional neural networks

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