Fine-grained classification of pedestrians in video: Benchmark and state of the art

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

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A new dataset of dog breed images and a benchmark for finegrained classification

Computational Visual Media ◽

10.1007/s41095-020-0184-6 ◽

2020 ◽

Vol 6 (4) ◽

pp. 477-487

Author(s):

Ding-Nan Zou ◽

Song-Hai Zhang ◽

Tai-Jiang Mu ◽

Min Zhang

Keyword(s):

Real World ◽

State Of The Art ◽

Whole Body ◽

Classification Models ◽

Neural Models ◽

Fine Grained ◽

Image Dataset ◽

Dog Breed ◽

Bounding Boxes

AbstractIn this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. It is currently the largest dataset for fine-grained classification of dogs, including 130 dog breeds and 70,428 real-world images. It has only one dog in each image and provides annotated bounding boxes for the whole body and head. In comparison to previous similar datasets, it contains more breeds and more carefully chosen images for each breed. The diversity within each breed is greater, with between 200 and 7000+ images for each breed. Annotation of the whole body and head makes the dataset not only suitable for the improvement of finegrained image classification models based on overall features, but also for those locating local informative parts. We show that dataset provides a tough challenge by benchmarking several state-of-the-art deep neural models. The dataset is available for academic purposes at https://cg.cs.tsinghua.edu.cn/ThuDogs/.

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Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

Journal of Speech Language and Hearing Research ◽

10.1044/jshr.3805.1126 ◽

1995 ◽

Vol 38 (5) ◽

pp. 1126-1142 ◽

Cited By ~ 14

Author(s):

Jeffrey W. Gilger

Keyword(s):

Language Development ◽

Behavioral Genetics ◽

State Of The Art ◽

Genetic Research ◽

Great Promise ◽

Behavioral Genetic ◽

Fine Grained ◽

Future Goals ◽

Current State ◽

Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

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Representation Learning for Fine-Grained Change Detection

Sensors ◽

10.3390/s21134486 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4486

Author(s):

Niall O’Mahony ◽

Sean Campbell ◽

Lenka Krpalkova ◽

Anderson Carvalho ◽

Joseph Walsh ◽

...

Keyword(s):

Deep Learning ◽

Change Detection ◽

Model Calibration ◽

State Of The Art ◽

Representation Learning ◽

Machine Intelligence ◽

The State ◽

Sensor Data ◽

Fine Grained ◽

Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

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Improving Land Cover Classification Using Genetic Programming for Feature Construction

Remote Sensing ◽

10.3390/rs13091623 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1623

Author(s):

João E. Batista ◽

Ana I. R. Cabral ◽

Maria J. P. Vasconcelos ◽

Leonardo Vanneschi ◽

Sara Silva

Keyword(s):

Land Cover ◽

Genetic Programming ◽

Satellite Images ◽

State Of The Art ◽

Binary Classification ◽

Feature Construction ◽

Classification Problems ◽

Construction Methods ◽

Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

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ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

International Journal of Computer Vision ◽

10.1007/s11263-021-01477-5 ◽

2021 ◽

Author(s):

Anil S. Baslamisli ◽

Partha Das ◽

Hoang-An Le ◽

Sezer Karaoglu ◽

Theo Gevers

Keyword(s):

Neural Network ◽

Large Scale ◽

State Of The Art ◽

Image Decomposition ◽

Natural Environments ◽

Decomposition Algorithms ◽

Ambient Light ◽

Fine Grained ◽

Large Scale Dataset ◽

Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

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Review of Control and Energy Management Approaches in Micro-Grid Systems

Energies ◽

10.3390/en14010168 ◽

2020 ◽

Vol 14 (1) ◽

pp. 168

Author(s):

Abdellatif Elmouatamid ◽

Radouane Ouladsine ◽

Mohamed Bakhouya ◽

Najib El Kamoun ◽

Mohammed Khaidar ◽

...

Keyword(s):

Energy Management ◽

Predictive Control ◽

State Of The Art ◽

Household Demand ◽

Grid Systems ◽

Control Approach ◽

Micro Grid ◽

Management Approaches ◽

Air Conditioning Systems

The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

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Datives with psych nouns and adjectives in Basque

Folia Linguistica ◽

10.1515/flin-2020-2050 ◽

2020 ◽

Vol 54 (3) ◽

pp. 647-696

Author(s):

Beatriz Fernández ◽

Fernando Zúñiga ◽

Ane Berro

Keyword(s):

Natural Language ◽

Linguistic Theory ◽

Psychological State ◽

Formal Expression ◽

Fine Grained ◽

Psych Verbs ◽

Other Regarding ◽

Psychological Verbs

Abstract This paper explores the formal expression of two Basque dative argument types in combination with psych nouns and adjectives, in intransitive and transitive clauses: (i) those that express the experiencer, and (ii) those that express the stimulus of the psychological state denoted by the psych noun and adjective. In the intransitive structure involving a dative experiencer (DatExpIS), the stimulus is in the absolutive case, and the intransitive copula izan ‘be’ shows both dative and absolutive agreement. This construction basically corresponds to those built upon the piacere type of psychological verbs typified in (Belletti, Adriana & Luigi Rizzi. 1988. Psych-verbs and θ-theory. Natural Language and Linguistic Theory 6. 291–352) three-way classification of Italian psych verbs. In the intransitive structure involving a dative stimulus (DatStimIS), the experiencer is marked by absolutive case, and the same intransitive copula shows both absolutive and dative agreement (with the latter corresponding to the dative stimulus and not to the experiencer). We show that the behavior of the dative argument in the two constructions is just the opposite of each other regarding a number of morphosyntactic tests, including agreement, constituency, hierarchy and selection. Additionally, we explore two parallel transitive constructions that involve either a dative experiencer and an ergative stimulus (DatExpTS) or a dative stimulus and an ergative experiencer (DatStimTS), which employ the transitive copula *edun ‘have’. Considering these configurations, we propose an extended and more fine-grained typology of psych predicates.

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BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

Computational Intelligence and Neuroscience ◽

10.1155/2019/1910624 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 4

Author(s):

Yikui Zhai ◽

He Cao ◽

Wenbo Deng ◽

Junying Gan ◽

Vincenzo Piuri ◽

...

Keyword(s):

Transfer Learning ◽

Classification Accuracy ◽

Learning Strategy ◽

State Of The Art ◽

Activation Function ◽

Training Data ◽

Fine Grained ◽

Pattern Recognition Problem ◽

Face Features ◽

Facial Beauty

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

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Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6383 ◽

2020 ◽

Vol 34 (05) ◽

pp. 8600-8607

Author(s):

Haiyun Peng ◽

Lu Xu ◽

Lidong Bing ◽

Fei Huang ◽

Wei Lu ◽

...

Keyword(s):

Sentiment Analysis ◽

State Of The Art ◽

Complete Solution ◽

Unified Model ◽

Two Stage ◽

Fine Grained ◽

Aspect Extraction ◽

Second Stage ◽

Opinion Extraction ◽

Complete Story

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

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