bluuebunny/crossref_metadata_2025 huggingface dataset:
Aportes del enfoque de derechos a la formación docente: un estudio de caso con maestros egresados de la Universidad de Costa Rica
Authors: Naranjo Segura Juan Carlos
Published: July 2022

El presente artículo tiene como objetivo un análisis de los aportes que el enfoque de derechos humanos proporciona en la formación de docentes y, por ende, una valoración de su importancia y contribución en el ejercicio de la profesión. Se atiende a un abordaje teórico desde tres ejes centrales: derechos humanos, la educación en derechos y el papel docente desde dicho enfoque. Para desarrollar la investigación, se utilizó una metodología de tipo cualitativo, a través de la cual se evidencian las experiencias, el dominio conceptual y los puntos de vista de maestros en ejercicio, egresados de la Universidad de Costa Rica y que llevaron el curso titulado “La Enseñanza de la Comprensión Internacional y los Derechos Humanos”. La recogida de datos fue realizada por medio de una entrevista semiestructurada a tres docentes de educación primaria que están ejerciendo su profesión, y, un cuestionario cualitativo, con preguntas abiertas aplicado a quince docentes con las características previamente mencionadas. Entre los principales resultados se ubica la visualización del enfoque de derechos como una herramienta teórica que permite la comprensión del propio contexto en relación a cómo se desarrolla el respeto por los derechos humanos, esto permite el desarrollo de destrezas como el pensamiento crítico, el análisis de la realidad social y la participación activa en la sociedad. Además, plantea la labor docente como más allá de un transmisor de contenido, posicionándose, así como un agente de transformación social.
Editorial: La búsqueda de la verdad: un punto de encuentro entre ciencia y religión
Authors: Aragón Vargas Luis Fernando
Published: July 2022

Este editorial es una versión mejorada de la conferencia homónima presentada en el marco de la Cátedra de Estudio sobre Religiones, Escuela de Filosofía, Universidad de Costa Rica. Se comenta sobre la importancia de la verdad y se presenta un mensaje sobre el punto de encuentro entre ciencia y religión, en la misma línea de manuscritos y editoriales previos en esta revista sobre filosofía de la ciencia. Además, se insiste en una responsabilidad moral de las personas que nos dedicamos a la ciencia, la investigación y la academia en el contexto universitario: la responsabilidad de contribuir al conocimiento.
Hero dan Kekerasan pada Masa Agresi Militer Belanda I dan II (1945-1949)
Authors: Sudarman Sudarman , Madora Wewel
Published: July 2022

Tulisan berikut ini merupakan review article (dan bukan book review) dari karya Remy Limpach, Kekerasan Ekstrem Belanda di Indonesia Perang Kemerdekaan Indonesia 1945-1949 sebuah buku berbahasa Belanda pada bulan September 2016. Dr. Remy Limpach bekerja di Nederlands Instituut voor Militairy Historie (NIHMH, Institut Sejarah Militer 1949 yang diterjemahkan ke dalam Bahasa Indonesia De Brandende Kampongs General Spoor (Amsterdam, 2016)
Starting and stopping as energokinematic regimes of electric drives of indus-trial mechanisms
Authors: Rodkin D.I. , Khrebtova О.А. , Zachepa N.V. , Zachepa Yu.V.
Published: July 2022

Purpose. To analyze the current technical terms that correspond to the physical processes in the electric drive system during atypical dynamic modes. Review possible modes in the kinematic parts of the electric drive during dynamic modes that may affect the nature of the transients. To form a definition of technical terms that characterize the real energy-kinematic regimes when moving and starting the electric drive of industrial mechanisms, which were not previously considered as insignificant or as non-existent.
Methodology. In order to solve the set tasks, the analysis of the technical terms defined by the technical literature, GOST, DSTU, etc. was carried out. To formulate new terminology for physical processes of atypical modes, the analysis of physical and energy processes occurring in the electrical and kinematic parts of the electric drive under such dynamic modes is performed..
Findings. Possible cases of atypical modes of operation of kinematic circuits and energy channels of energy conversion during the start-up of electric drives of technological mechanisms under load, for the characteristics of which there is no technical terminology, are identified. Based on the results of the analysis of energy-kinematic regimes, technical terminology is proposed, which fully covers and characterizes the considered atypical processes that take place during the moving and start-up of technological mechanisms.
Originality. For the first time, technical terminology has been proposed that characterizes the energy-kinematic modes of electric drives of technological mechanisms during start-up.
Practical value.  For the first time, the use of technical terms is proposed for a clear and visual representation of energy kinematic modes that occur during the moving and start-up under load of different types of technological mechanisms.
Cognitive Labyrinths of Social Creativity
Authors:   Karaganda University, Zhussupova Bakhyt Zh.
Published: July 2022

The article raises the urgent problem of finding a relevant methodology of modern social cognition. This methodology should be able to adequately reflect modern complex, creative realities of our society which provides a qualitatively different education compared to the previous ones — innovative, super dynamic, polyphonic, paradoxical, and even absurd. We are on the “threshold” of an unknown social reality. The current course of social development categorically refutes the classical principles of social philosophy. It proves their failure in striving for absolutization, idealization, their lifelessness, and even inhumanity, as well as danger, leading to many ideological delusions, social tension, and social cataclysms. It became indisputable that the society of the XXI century needs qualitatively new paradigms of social cognition and understanding. This is a fundamentally different naturalness, a different sociality, a different “humanity.” The transformation of classical social philosophy into nonclassical and post-non-classical socio-philosophical discourses was undoubtedly caused by the need to search for new relevant cognitive paradigms geared towards understanding a person, whose existence in the face of modern civilizational challenges of heterogeneous sociality has become extremely tense and acutely problematic. The article attempts to substantiate a new humanitarian concept of a post-nonclassical socio-philosophical paradigm that focuses on the singularity of human existence, self-organizing within the boundaries of the polystructural social proliferation of modern society, in the cognitive and ontological space that legitimizes uncertainty. “Uncertainty” is a topical word and a concept of modern scientific and everyday culture, a fundamental characteristic of modernity. However, the talk is not about the inflation of truth but about the search for a new social research paradigm capable of reflecting the “turbulent” processes of modern society that eliminate classical social attitudes.
YouTube Video Hosting as a Means of Forming Public Opinion of Russian Youth
Authors:   Ural Federal University, Grunt Elena V.
Published: July 2022

The Internet is of great importance for shaping (and manipulating) the public opinion of young people. This is a means of communication, especially during the pandemic period, when “live” communication, interaction is reduced to a minimum. During this period, the problem of the reliability of information provided by a particular social network appears. Today, YouTube.com video hosting has become one of the main competitors to television and has gained a billionth audience. YouTube’s main audience is youth aged 18 to 34. The studyconducted in 2019 in Yekaterinburg used a quantitative — online questionnaire method (n = 660) and a qualitative strategy (n = 5). The article deals with questions about the types of information consumed by young people, the degree of trust in YouTube, the skills of analyzing information, the means of forming and manipulating of youth’s public opinion.
Neoliberalismo e distribuição de renda na América Latina
Authors: BAER WERNER , MALONEY WILLIAM
Published: July 2022

RESUMO Este artigo analisa as principais medidas de política neoliberal instituídas na América Latina na última década e seu impacto sobre a equidade. Ele primeiro enfatiza a dificuldade de separar o impacto das medidas de liberalização dos ajustes fiscais necessários da década de 1980, e seus efeitos de transição versus longo prazo, e então coloca os movimentos observados na distribuição em um contexto global e histórico. A segunda parte coloca várias inovações dos regimes neoliberais em perspectiva histórica e argumenta que seu impacto geral provavelmente não será regressivo e que os regimes anteriores provavelmente não foram especialmente progressivos. A longo prazo, a evolução dos mercados de fatores provavelmente será de importância primordial - o lado da demanda, impulsionado pelo crescimento industrial reorientado e a crescente importância do setor de serviços, interagindo com a oferta relativa de mão de obra qualificada e não qualificada - provavelmente será de importância primordial na determinação da evolução da distribuição do rendimento.
Salário, câmbio e a competitividade das exportações brasileiras
Authors: ROCHA CARLOS HENRIQUE
Published: July 2022

RESUMO O objetivo deste artigo é investigar empiricamente o papel dos salários nas exportações brasileiras no período de maior preocupação com o front externo, i. e., entre o final dos anos 1960 e meados dos anos 1980. Descobrimos que os salários têm contribuído para que nossas exportações cheguem aos mercados externos.
A comercialização de agrotóxicos e o modelo químico-dependente da agricultura do Brasil
Authors: Ribeiro Suellen Dayse de Moura , Siqueira Marília Teixeira de , Gurgel Idê Gomes Dantas , Diniz George Tadeu Nunes
Published: July 2022

RESUMO Com o objetivo de analisar a comercialização de agrotóxicos no Brasil, suas regiões e estados, desenvolveu-se um estudo ecológico de série temporal no período de 2000 a 2014. Foram utilizados os dados sobre vendas de agrotóxicos do Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis e do Sindicato Nacional da Indústria de Produtos para Defesa Vegetal. A comercialização de agrotóxicos foi calculada como o quociente da quantidade de ingredientes ativos, em quilogramas, e a área plantada das principais lavouras, em hectares, anualmente, nos estados e regiões. Os programas Excel® e R foram utilizados para processamento e análise dos dados. Para análise de tendência, utilizou-se a regressão linear com nível de significância de 5%. Observou-se tendência à elevação da comercialização em todas as regiões do País no período (p<0,001), com o maior registro no Sudeste (4,88 kg/ha/ano); e o maior incremento nas regiões Norte e Nordeste. As maiores médias de vendas, em kg/ha/ano, ocorreram em São Paulo (8,43), Goiás (5,34) e Mato Grosso (4,92). O maior incremento de vendas por estado no período ocorreu no Acre (99,52%), Piauí (94,19%) e Distrito Federal (91,55%). Tornam-se imperativas medidas de regulação, fiscalização e normatização contra o agravamento da situação de saúde da população e contaminação do ambiente.
Impactos da pulverização aérea de agrotóxicos em uma comunidade rural em contexto de conflito
Authors: Freitas Lucinéia Miranda de , Bonfatti Renato , Vasconcellos Luiz Carlos Fadel de
Published: July 2022

RESUMO Este trabalho buscou analisar o contexto social, institucional e ambiental da contaminação humana e ambiental por agrotóxicos, via pulverização aérea, por meio do estudo de caso de uma comunidade atingida, incluindo a possibilidade de os agrotóxicos serem utilizados como arma química em região onde há conflitos por terras e territórios. Como metodologia, utilizou-se o estudo de caso sobre a pulverização ocorrida no Assentamento Raimundo Vieira III, Gleba Gama, Nova Guarita-MT. No caso estudado, há indícios de intencionalidade no processo de contaminação das famílias, principalmente considerando que estas já vinham sofrendo outras agressões. O estudo mostrou a ineficiência, intencional ou não, dos aparelhos públicos tanto na investigação e responsabilização dos envolvidos quanto no atendimento das pessoas contaminadas. Conclui-se que o método de pulverização aérea é ineficiente com perdas elevadas, só podendo justificar sua adoção por razões econômicas em detrimento da racionalidade técnica e dos princípios de prevenção e precaução. O Estado não consegue manter efetivamente a fiscalização do uso de agrotóxicos, e há uma ampliação dos riscos vinculados aos agrotóxicos com as mudanças legais que têm sistematicamente ocorrido desde 2015.
Toxicologia crítica aplicada aos agrotóxicos – perspectivas em defesa da vida
Authors: Friedrich Karen , Gurgel Aline do Monte , Sarpa Marcia , Bedor Cheila Nataly Galindo , Siqueira Marília Teixeira de , Gurgel Idê Gomes Dantas , Augusto Lia Giraldo da Silva
Published: July 2022

RESUMO A toxicologia é aplicada aos processos regulatórios tendo como base central a linearidade das relações entre a dose e o efeito e a possibilidade de estabelecer condições de exposição seguras. Isso ocorre apesar das limitações apontadas pela literatura cientifica. A concepção, a definição das metodologias e a condução da avaliação de risco dos agrotóxicos acabam por atender aos interesses econômicos e à definição de cenários de segurança distantes da realidade. As limitações metodológicas dos estudos exigidos para fins de registro de um agrotóxico envolvem: a desconsideração das interações entre as misturas utilizadas; a não previsão de curvas dose-resposta não lineares (horméticas); a compartimentalização dos desfechos analisados; a exposição nos períodos críticos do desenvolvimento; e a desconsideração do contexto, das diversidades individuais, coletivas e dos territórios expostos aos agrotóxicos, entre outros aspectos discutido nesse ensaio. A toxicologia crítica propõe que a avaliação toxicológica parta da integralidade do problema no contexto apresentando propostas que podem ser adotadas nos processos de regulação de agrotóxicos e outras substâncias potencialmente perigosas.
Feiras Orgânicas enquanto política de abastecimento alimentar e promoção da saúde: um estudo de caso
Authors: carvalho Soraia Martins de , Bezerra Islandia , Rigon Silvia do Amaral , Cassarino Julian Perez
Published: July 2022

RESUMO Estudo de caso com o objetivo de compreender o processo organizacional das Feiras Orgânicas em Curitiba-PR, enquanto política de abastecimento alimentar e de promoção da saúde. Pesquisa de caráter qualitativo, realizada mediante análise de documentos institucionais e entrevistas semiestruturadas com feirantes e técnicos da área de abastecimento, informantes-chave. Destacou-se a importância social desse local enquanto espaço de comercialização de alimentos produzidos sem agrotóxicos e provenientes da agricultura familiar. Um vínculo de solidariedade construído com os consumidores foi observado, constituindo um ambiente de cooperação para a alimentação saudável, de socialização de saberes e de empoderamento dos envolvidos. No entanto, a população de baixo poder aquisitivo manteve-se distante desse consumo por causa dos preços elevados e da localização distanciada da periferia da cidade. Identificou-se também um risco para a continuidade da comercialização direta com o consumidor em função de nova política de pagamento pelo uso do espaço a partir de valores considerados elevados para os agricultores. Conclui-se que a busca pela inclusão da agricultura familiar nas Feiras Orgânicas e pela democratização do acesso a esses alimentos devem nortear as políticas de abastecimento, considerando sobretudo, a necessidade de soberania e segurança alimentar e nutricional tanto no campo quanto na cidade.
Combates e continuidades do legado modernista em algumas publicações contemporâneas
Authors: Magri Ieda
Published: July 2022

Resumo O presente texto parte de duas críticas à centralidade do projeto modernista brasileiro, externadas uma, a de Franklin Távora, na ocasião da comemoração dos 70 anos da Semana de Arte Moderna de 22, e outra, a de Luís Augusto Fischer, em Duas formações, uma história: das ideias fora do lugar ao perspectivismo ameríndio, por ocasião das comemorações do centenário, neste ano de 2022. O livro de Fischer não trata propriamente do centenário, mas se organiza a partir da crítica da constituição da Semana de Arte Moderna como marco zero e inultrapassável de nossa modernidade, que obliteraria outros modos possíveis para pensar de maneira diversa da atual a história da literatura brasileira. O artigo responde às duas críticas com alguns exemplos de releituras de Mário e de Oswald de Andrade que mostram como seus trabalhos ainda reverberam (por suas falhas e por seus acertos) na literatura e na arte contemporâneas.
Context-Aware Transfer Attacks for Object Detection
Authors: Cai Zikui , Xie Xinxin , Li Shasha , Yin Mingjun , Song Chengyu , Krishnamurthy Srikanth V. , Roy-Chowdhury Amit K. , Asif M. Salman
Published: July 2022

Blackbox transfer attacks for image classifiers have been extensively studied in recent years. In contrast, little progress has been made on transfer attacks for object detectors. Object detectors take a holistic view of the image and the detection of one object (or lack thereof) often depends on other objects in the scene. This makes such detectors inherently context-aware and adversarial attacks in this space are more challenging than those targeting image classifiers. In this paper, we present a new approach to generate context-aware attacks for object detectors. We show that by using co-occurrence of objects and their relative locations and sizes as context information, we can successfully generate targeted mis-categorization attacks that achieve higher transfer success rates on blackbox object detectors than the state-of-the-art. We test our approach on a variety of object detectors with images from PASCAL VOC and MS COCO datasets and demonstrate up to 20 percentage points improvement in performance compared to the other state-of-the-art methods.
OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression
Authors: Cao Linfeng , Jiang Aofan , Li Wei , Wu Huaying , Ye Nanyang
Published: July 2022

Recently, deep learning has been proven to be a promising approach in standard dynamic range (SDR) image compression. However, due to the wide luminance distribution of high dynamic range (HDR) images and the lack of large standard datasets, developing a deep model for HDR image compression is much more challenging. To tackle this issue, we view HDR data as distributional shifts of SDR data and the HDR image compression can be modeled as an out-of-distribution generalization (OoD) problem. Herein, we propose a novel out-of-distribution (OoD) HDR image compression framework (OoDHDR-codec). It learns the general representation across HDR and SDR environments, and allows the model to be trained effectively using a large set of SDR datases supplemented with much fewer HDR samples. Specifically, OoDHDR-codec consists of two branches to process the data from two environments. The SDR branch is a standard blackbox network. For the HDR branch, we develop a hybrid system that models luminance masking and tone mapping with white-box modules and performs content compression with black-box neural networks. To improve the generalization from SDR training data on HDR data, we introduce an invariance regularization term to learn the common representation for both SDR and HDR compression. Extensive experimental results show that the OoDHDR codec achieves strong competitive in-distribution performance and state-of-the-art OoD performance. To the best of our knowledge, our proposed approach is the first work to model HDR compression as OoD generalization problems and our OoD generalization algorithmic framework can be applied to any deep compression model in addition to the network architectural choice demonstrated in the paper. Code available at https://github.com/caolinfeng/OoDHDR-codec.
Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations
Authors: Chang Seunggyu , Cho Jungchan , Oh Songhwai
Published: July 2022

We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide sufficient information for estimating a complete texture map, the proposed model simultaneously generates multi-view hallucinations in the image domain and an estimated texture map in the texture domain. During the generating process, each domain generator exchanges features to the other by a flow-based local attention mechanism. In this manner, the proposed model can estimate a texture map utilizing abundant multi-view image features from which multiview hallucinations are generated. As a result, the estimated texture map contains consistent colors and patterns over the entire region. Experiments show the superiority of our model for estimating a directly render-able texture map, which is applicable to 3D animation rendering. Furthermore, our model also improves an overall generation quality in the image domain for pose and viewpoint transfer tasks.
Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation
Authors: Chen Chao , Zhan Yibing , Yu Baosheng , Liu Liu , Luo Yong , Du Bo
Published: July 2022

Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks. However, current SGG methods usually suffer from sub-optimal scene graph generation because of the long-tailed distribution of training data. To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation. Specifically, RTPB uses a distributed-based prior bias to improve models' detecting ability on less frequent relationships during training, thus improving the model generalizability on tail categories. In addition, to further explore the contextual information of objects and relationships, we design a contextual encoding backbone network, termed as Dual Transformer (DTrans). We perform extensive experiments on a very popular benchmark, VG150, to demonstrate the effectiveness of our method for the unbiased scene graph generation. In specific, our RTPB achieves an improvement of over 10% under the mean recall when applied to current SGG methods. Furthermore, DTrans with RTPB outperforms nearly all state-of-the-art methods with a large margin. Code is available at https://github.com/ChCh1999/RTPB
TransZero: Attribute-Guided Transformer for Zero-Shot Learning
Authors: Chen Shiming , Hong Ziming , Liu Yang , Xie Guo-Sen , Sun Baigui , Li Hao , Peng Qinmu , Lu Ke , You Xinge
Published: July 2022

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descriptions shared between different classes, which are strong prior for localization of object attribute for representing discriminative region features enabling significant visual-semantic interaction. Although few attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected. In this paper, we propose an attribute-guided Transformer network to learn the attribute localization for discriminative visual-semantic embedding representations in ZSL, termed TransZero. Specifically, TransZero takes a feature augmentation encoder to alleviate the cross-dataset bias between ImageNet and ZSL benchmarks and improve the transferability of visual features by reducing the entangled relative geometry relationships among region features. To learn locality-augmented visual features, TransZero employs a visual-semantic decoder to localize the most relevant image regions to each attributes from a given image under the guidance of attribute semantic information. Then, the locality-augmented visual features and semantic vectors are used for conducting effective visual-semantic interaction in a visual-semantic embedding network. Extensive experiments show that TransZero achieves a new state-of-the-art on three ZSL benchmarks. The codes are available at: https://github.com/shiming-chen/TransZero.
Structured Semantic Transfer for Multi-Label Recognition with Partial Labels
Authors: Chen Tianshui , Pu Tao , Wu Hefeng , Xie Yuan , Lin Liang
Published: July 2022

Multi-label image recognition is a fundamental yet practical task because real-world images inherently possess multiple semantic labels. However, it is difficult to collect large-scale multi-label annotations due to the complexity of both the input images and output label spaces. To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i.e., merely some labels are known while other labels are missing (also called unknown labels) per image. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations to transfer knowledge of known labels to generate pseudo labels for unknown labels. Specifically, an intra-image semantic transfer module learns image-specific label co-occurrence matrix and maps the known labels to complement unknown labels based on this matrix. Meanwhile, a cross-image transfer module learns category-specific feature similarities and helps complement unknown labels with high similarities. Finally, both known and generated labels are used to train the multi-label recognition models. Extensive experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.
Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition
Authors: Chen Yingjie , Chen Diqi , Wang Tao , Wang Yizhou , Liang Yun
Published: July 2022

Subject-invariant facial action unit (AU) recognition remains challenging for the reason that the data distribution varies among subjects. In this paper, we propose a causal inference framework for subject-invariant facial action unit recognition. To illustrate the causal effect existing in AU recognition task, we formulate the causalities among facial images, subjects, latent AU semantic relations, and estimated AU occurrence probabilities via a structural causal model. By constructing such a causal diagram, we clarify the causal-effect among variables and propose a plug-in causal intervention module, CIS, to deconfound the confounder Subject in the causal diagram. Extensive experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, show the effectiveness of our CIS, and the model with CIS inserted, CISNet, has achieved state-of-the-art performance.
Denoised Maximum Classiﬁer Discrepancy for Source-Free Unsupervised Domain Adaptation
Authors: Chu Tong , Liu Yahao , Deng Jinhong , Li Wen , Duan Lixin
Published: July 2022

Source-Free Unsupervised Domain Adaptation(SFUDA) aims to adapt a pre-trained source model to an unlabeled target domain without access to the original labeled source domain samples. Many existing SFUDA approaches apply the self-training strategy, which involves iteratively selecting confidently predicted target samples as pseudo-labeled samples used to train the model to fit the target domain. However, the self-training strategy may also suffer from sample selection bias and be impacted by the label noise of the pseudo-labeled samples. In this work, we provide a rigorous theoretical analysis on how these two issues affect the model generalization ability when applying the self-training strategy for the SFUDA problem. Based on this theoretical analysis, we then propose a new Denoised Maximum Classifier Discrepancy (D-MCD) method for SFUDA to effectively address these two issues. In particular, we first minimize the distribution mismatch between the selected pseudo-labeled samples and the remaining target domain samples to alleviate the sample selection bias. Moreover, we design a strong-weak self-training paradigm to denoise the selected pseudo-labeled samples, where the strong network is used to select pseudo-labeled samples while the weak network helps the strong network to filter out hard samples to avoid incorrect labels. In this way, we are able to ensure both the quality of the pseudo-labels and the generalization ability of the trained model on the target domain. We achieve state-of-the-art results on three domain adaptation benchmark datasets, which clearly validates the effectiveness of our proposed approach. Full code is available at https://github.com/kkkkkkon/D-MCD.
InsCLR: Improving Instance Retrieval with Self-Supervision
Authors: Deng Zelu , Zhong Yujie , Guo Sheng , Huang Weilin
Published: July 2022

This work aims at improving instance retrieval with self-supervision. We find that fine-tuning using the recently developed self-supervised learning (SSL) methods, such as SimCLR and MoCo, fails to improve the performance of instance retrieval. In this work, we identify that the learnt representations for instance retrieval should be invariant to large variations in viewpoint and background etc., whereas self-augmented positives applied by the current SSL methods can not provide strong enough signals for learning robust instance-level representations. To overcome this problem, we propose InsCLR, a new SSL method that builds on the instance-level contrast, to learn the intra-class invariance by dynamically mining meaningful pseudo positive samples from both mini-batches and a memory bank during training. Extensive experiments demonstrate that InsCLR achieves similar or even better performance than the state-of-the-art SSL methods on instance retrieval. Code is available at https://github.com/zeludeng/insclr.
Perceptual Quality Assessment of Omnidirectional Images
Authors: Fang Yuming , Huang Liping , Yan Jiebin , Liu Xuelin , Liu Yang
Published: July 2022

Omnidirectional images, also called 360◦images, have attracted extensive attention in recent years, due to the rapid development of virtual reality (VR) technologies. During omnidirectional image processing including capture, transmission, consumption, and so on, measuring the perceptual quality of omnidirectional images is highly desired, since it plays a great role in guaranteeing the immersive quality of experience (IQoE). In this paper, we conduct a comprehensive study on the perceptual quality of omnidirectional images from both subjective and objective perspectives. Specifically, we construct the largest so far subjective omnidirectional image quality database, where we consider several key influential elements, i.e., realistic non-uniform distortion, viewing condition, and viewing behavior, from the user view. In addition to subjective quality scores, we also record head and eye movement data. Besides, we make the first attempt by using the proposed database to train a convolutional neural network (CNN) for blind omnidirectional image quality assessment. To be consistent with the human viewing behavior in the VR device, we extract viewports from each omnidirectional image and incorporate the user viewing conditions naturally in the proposed model. The proposed model is composed of two parts, including a multi-scale CNN-based feature extraction module and a perceptual quality prediction module. The feature extraction module is used to incorporate the multi-scale features, and the perceptual quality prediction module is designed to regress them to perceived quality scores. The experimental results on our database verify that the proposed model achieves the competing performance compared with the state-of-the-art methods.
DuMLP-Pin: A Dual-MLP-Dot-Product Permutation-Invariant Network for Set Feature Extraction
Authors: Fei Jiajun , Zhu Ziyu , Liu Wenlei , Deng Zhidong , Li Mingyang , Deng Huanjun , Zhang Shuo
Published: July 2022

Existing permutation-invariant methods can be divided into two categories according to the aggregation scope, i.e. global aggregation and local one. Although the global aggregation methods, e. g., PointNet and Deep Sets, get involved in simpler structures, their performance is poorer than the local aggregation ones like PointNet++ and Point Transformer. It remains an open problem whether there exists a global aggregation method with a simple structure, competitive performance, and even much fewer parameters. In this paper, we propose a novel global aggregation permutation-invariant network based on dual MLP dot-product, called DuMLP-Pin, which is capable of being employed to extract features for set inputs, including unordered or unstructured pixel, attribute, and point cloud data sets. We strictly prove that any permutation-invariant function implemented by DuMLP-Pin can be decomposed into two or more permutation-equivariant ones in a dot-product way as the cardinality of the given input set is greater than a threshold. We also show that the DuMLP-Pin can be viewed as Deep Sets with strong constraints under certain conditions. The performance of DuMLP-Pin is evaluated on several different tasks with diverse data sets. The experimental results demonstrate that our DuMLP-Pin achieves the best results on the two classification problems for pixel sets and attribute sets. On both the point cloud classification and the part segmentation, the accuracy of DuMLP-Pin is very close to the so-far best-performing local aggregation method with only a 1-2% difference, while the number of required parameters is significantly reduced by more than 85% in classification and 69% in segmentation, respectively. The code is publicly available on https://github.com/JaronTHU/DuMLP-Pin.
Feature Distillation Interaction Weighting Network for Lightweight Image Super-resolution
Authors: Gao Guangwei , Li Wenjie , Li Juncheng , Wu Fei , Lu Huimin , Yu Yi
Published: July 2022

Convolutional neural networks based single-image superresolution (SISR) has made great progress in recent years. However, it is difficult to apply these methods to real-world
scenarios due to the computational and memory cost. Meanwhile, how to take full advantage of the intermediate features under the constraints of limited parameters and calculations
is also a huge challenge. To alleviate these issues, we propose a lightweight yet efficient Feature Distillation Interaction Weighted Network (FDIWN). Specifically, FDIWN utilizes a series of specially designed Feature Shuffle Weighted
Groups (FSWG) as the backbone, and several novel mutual Wide-residual Distillation Interaction Blocks (WDIB) form an FSWG. In addition, Wide Identical Residual Weighting
(WIRW) units and Wide Convolutional Residual Weighting (WCRW) units are introduced into WDIB for better feature distillation. Moreover, a Wide-Residual Distillation Connection (WRDC) framework and a Self-Calibration Fusion
(SCF) unit are proposed to interact features with different scales more flexibly and efficiently. Extensive experiments show that our FDIWN is superior to other models to strike a good balance between model performance and efficiency.
The code is available at https://github.com/IVIPLab/FDIWN.
Weakly-Supervised Salient Object Detection Using Point Supervision
Authors: Gao Shuyong , Zhang Wei , Wang Yan , Guo Qianyu , Zhang Chenglong , He Yangji , Zhang Wenqiang
Published: July 2022

Current state-of-the-art saliency detection models rely heavily on large datasets of accurate pixel-wise annotations, but manually labeling pixels is time-consuming and labor-intensive. There are some weakly supervised methods developed for alleviating the problem, such as image label, bounding box label, and scribble label, while point label still has not been explored in this field. In this paper, we propose a novel weakly-supervised salient object detection method using point supervision. To infer the saliency map, we first design an adaptive masked flood filling algorithm to generate pseudo labels. Then we develop a transformer-based point-supervised saliency detection model to produce the first round of saliency maps. However, due to the sparseness of the label, the weakly supervised model tends to degenerate into a general foreground detection model. To address this issue, we propose a Non-Salient Suppression (NSS) method to optimize the erroneous saliency maps generated in the first round and leverage them for the second round of training. Moreover, we build a new point-supervised dataset (P-DUTS) by relabeling the DUTS dataset. In P-DUTS, there is only one labeled point for each salient object. Comprehensive experiments on five largest benchmark datasets demonstrate our method outperforms the previous state-of-the-art methods trained with the stronger supervision and even surpass several fully supervised state-of-the-art models. The code is available at: https://github.com/shuyonggao/PSOD.
Shadow Generation for Composite Image in Real-World Scenes
Authors: Hong Yan , Niu Li , Zhang Jianfu
Published: July 2022

Image composition targets at inserting a foreground object into a background image. Most previous image composition methods focus on adjusting the foreground to make it compatible with background while ignoring the shadow effect of foreground on the background. In this work, we focus on generating plausible shadow for the foreground object in the composite image. First, we contribute a real-world shadow generation dataset DESOBA by generating synthetic composite images based on paired real images and deshadowed images. Then, we propose a novel shadow generation network SGRNet, which consists of a shadow mask prediction stage and a shadow filling stage. In the shadow mask prediction stage, foreground and background information are thoroughly interacted to generate foreground shadow mask. In the shadow filling stage, shadow parameters are predicted to fill the shadow area. Extensive experiments on our DESOBA dataset and real composite images demonstrate the effectiveness of our proposed method. Our dataset and code are available at https://github.com/bcmi/Object-Shadow-Generation- Dataset-DESOBA.
Shape-Adaptive Selection and Measurement for Oriented Object Detection
Authors: Hou Liping , Lu Ke , Xue Jian , Li Yuqiu
Published: July 2022

The development of detection methods for oriented object detection remains a challenging task. A considerable obstacle is the wide variation in the shape (e.g., aspect ratio) of objects. Sample selection in general object detection has been widely studied as it plays a crucial role in the performance of the detection method and has achieved great progress. However, existing sample selection strategies still overlook some issues: (1) most of them ignore the object shape information; (2) they do not make a potential distinction between selected positive samples; and (3) some of them can only be applied to either anchor-free or anchor-based methods and cannot be used for both of them simultaneously. In this paper, we propose novel flexible shape-adaptive selection (SA-S) and shape-adaptive measurement (SA-M) strategies for oriented object detection, which comprise an SA-S strategy for sample selection and SA-M strategy for the quality estimation of positive samples. Specifically, the SA-S strategy dynamically selects samples according to the shape information and characteristics distribution of objects. The SA-M strategy measures the localization potential and adds quality information on the selected positive samples. The experimental results on both anchor-free and anchor-based baselines and four publicly available oriented datasets (DOTA, HRSC2016, UCAS-AOD, and ICDAR2015) demonstrate the effectiveness of the proposed method.
Diagnostics-Guided Explanation Generation
Authors: Atanasova Pepa , Simonsen Jakob Grue , Lioma Christina , Augenstein Isabelle
Published: July 2022

Explanations shed light on a machine learning model's rationales and can aid in identifying deficiencies in its reasoning process. Explanation generation models are typically trained in a supervised way given human explanations. When such annotations are not available, explanations are often selected as those portions of the input that maximise a downstream task's performance, which corresponds to optimising an explanation's Faithfulness to a given model. Faithfulness is one of several so-called diagnostic properties, which prior work has identified as useful for gauging the quality of an explanation without requiring annotations. Other diagnostic properties are Data Consistency, which measures how similar explanations are for similar input instances, and Confidence Indication, which shows whether the explanation reflects the confidence of the model. In this work, we show how to directly optimise for these diagnostic properties when training a model to generate sentence-level explanations, which markedly improves explanation quality, agreement with human rationales, and downstream task performance on three complex reasoning tasks.
On the Transferability of Pre-trained Language Models: A Study from Artificial Datasets
Authors: Chiang Cheng-Han , Lee Hung-yi
Published: July 2022

Pre-training language models (LMs) on large-scale unlabeled text data makes the model much easier to achieve exceptional downstream performance than their counterparts directly trained on the downstream tasks.
In this work, we study what specific traits in the pre-training data, other than the semantics, make a pre-trained LM superior to their counterparts trained from scratch on downstream tasks.
We propose to use artificially constructed datasets as the pre-training data to exclude the effect of semantics, and further control what characteristics the pre-training corpora have.
By fine-tuning the pre-trained models on GLUE benchmark, we can learn how beneficial it is to transfer the knowledge from the model trained on the dataset possessing that specific trait.
We define and discuss three different characteristics in the artificial dataset: 1) matching the token's uni-gram or bi-gram distribution between pre-training and downstream fine-tuning, 2) the presence of the explicit dependencies among the tokens in a sequence, 3) the length of the implicit dependencies among the tokens in a sequence.
Our experiments show that the explicit dependencies in the sequences of the pre-training data are critical to the downstream performance.
Our results also reveal that models achieve better downstream performance when pre-trained on a dataset with a longer range of implicit dependencies.
Based on our analysis, we find that models pre-trained with artificial datasets are prone to learn spurious correlation in downstream tasks.
Our work reveals that even if the LMs are not pre-trained on natural language, they still gain transferability on certain human language downstream tasks once the LMs learn to model the token dependencies in the sequences.
This result helps us understand the exceptional transferability of pre-trained LMs.
AutoBERT-Zero: Evolving BERT Backbone from Scratch
Authors: Gao Jiahui , Xu Hang , Shi Han , Ren Xiaozhe , Yu Philip L. H. , Liang Xiaodan , Jiang Xin , Li Zhenguo
Published: July 2022

Transformer-based pre-trained language models like BERT and its variants have recently achieved promising performance in various natural language processing (NLP) tasks. However, the conventional paradigm constructs the backbone by purely stacking the manually designed global self-attention layers, introducing inductive bias and thus leads to sub-optimal. In this work, we make the first attempt to automatically discover novel pre-trained language model (PLM) backbone on a flexible search space containing the most fundamental operations from scratch. Specifically, we propose a well-designed search space which (i) contains primitive math operations in the intra-layer level to explore novel attention structures, and (ii) leverages convolution blocks to be the supplementary for attentions in the inter-layer level to better learn local dependency. To enhance the efficiency for finding promising architectures, we propose an Operation-Priority Neural Architecture Search (OP-NAS) algorithm, which optimizes both the search algorithm and evaluation of candidate models. Specifically, we propose Operation-Priority (OP) evolution strategy to facilitate model search via balancing exploration and exploitation. Furthermore, we design a Bi-branch Weight-Sharing (BIWS) training strategy for fast model evaluation. Extensive experiments show that the searched architecture (named AutoBERT-Zero) significantly outperforms BERT and its variants of different model capacities in various downstream tasks, proving the architecture's transfer and scaling abilities. Remarkably, AutoBERT-Zero-base outperforms RoBERTa-base (using much more data) and BERT-large (with much larger model size) by 2.4 and 1.4 higher score on GLUE test set.
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection
Authors: He Wanwei , Dai Yinpei , Zheng Yinhe , Wu Yuchuan , Cao Zheng , Liu Dermot , Jiang Peng , Yang Min , Huang Fei , Si Luo , Sun Jian , Li Yongbin
Published: July 2022

Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and generation tasks while neglecting the exploitation of dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning. Specifically, we introduce a dialog act prediction task for policy optimization during pre-training and employ a consistency regularization term to refine the learned representation with the help of unlabeled dialogs. We also implement a gating mechanism to weigh suitable unlabeled dialog samples. Empirical results show that GALAXY substantially improves the performance of task-oriented dialog systems, and achieves new state-of-the-art results on benchmark datasets: In-Car, MultiWOZ2.0 and MultiWOZ2.1, improving their end-to-end combined scores by 2.5, 5.3 and 5.5 points, respectively. We also show that GALAXY has a stronger few-shot ability than existing models under various low-resource settings. For reproducibility, we release the code and data at https://github.com/siat-nlp/GALAXY.