Object Categories Research Articles

An image dataset for analyzing tea picking behavior in tea plantations

Tea is an important economic product in China, and tea picking is a key agricultural activity. As the practice of tea picking in China gradually shifts towards intelligent and mechanized methods, artificial intelligence recognition technology has become a crucial tool, showing great potential in recognizing large-scale tea picking operations and various picking behaviors. Constructing a comprehensive database is essential for these advancements. The newly developed Tea Garden Harvest Dataset offers several advantages that have a positive impact on tea garden management: 1) Enhanced image diversity: through advanced data augmentation techniques such as rotation, cropping, enhancement, and flipping, our dataset provides a rich variety of images. This diversity improves the model’s ability to accurately recognize tea picking behaviors under different environments and conditions. 2) Precise annotations: every image in our dataset is meticulously annotated with boundary box coordinates, object categories, and sizes. This detailed annotation helps to better understand the target features, enhancing the model’s learning process and overall performance. 3) Multi-Scale training capability: our dataset supports multi-scale training, allowing the model to adapt to targets of different sizes. This capability ensures versatility and accuracy in real-world applications, where objects may appear at varying distances and scales. This tea garden picking dataset not only fills the existing gap in the data related to tea picking in China but also makes a significant contribution to advancing intelligent tea picking practices. By leveraging its unique advantages, this dataset becomes a powerful resource for tea garden management, promoting increased efficiency, accuracy, and productivity in tea production.

Research Advanced in Image Recognition under Autonomous Driving Scene Based on Deep Learning

Image recognition has always been a fundamental research task in the computer vision community, aimed at identifying the categories of objects in images and has been widely used in many fields, especially in autonomous driving. Early image recognition technologies were mostly based on machine learning, and their recognition speed and accuracy could not meet the application requirements of complex autonomous driving scenarios. With the great success of convolutional neural networks, image recognition technology based on deep learning has attracted increasing research interest. Taking the autonomous driving scenario as an example, this article introduces the latest research progress of image recognition technology, including representative methods and their basic pipelines. In addition, this paper also introduces the commonly used dataset in image recognition and discusses the existing problems of image recognition in autonomous driving tasks. Finally, this paper looks forward to the future development directions of this field, hoping bring some new insight to advance the further development of image recognition under autonomous driving scene.

A human single-neuron dataset for object recognition

Object recognition is fundamental to how we interact with and interpret the world around us. The human amygdala and hippocampus play a key role in object recognition, contributing to both the encoding and retrieval of visual information. Here, we recorded single-neuron activity from the human amygdala and hippocampus when neurosurgical epilepsy patients performed a one-back task using naturalistic object stimuli. We employed two sets of naturalistic object images from leading datasets extensively used in primate neural recordings and computer vision models: we recorded 1204 neurons using the ImageNet stimuli, which included broader object categories (10 different images per category for 50 categories), and we recorded 512 neurons using the Microsoft COCO stimuli, which featured a higher number of images per category (50 different images per category for 10 categories). Together, our extensive dataset, offering the highest spatial and temporal resolution currently available in humans, will not only facilitate a comprehensive analysis of the neural correlates of object recognition but also provide valuable opportunities for training and validating computational models.

Advances and Challenges in Small Object Detection: A Comparative Analysis of State-of-the-Art Models and Future Directions

Object detection involves the precise and efficient identification and localization of multiple predefined object categories within images. With the advent of deep learning, both the accuracy and efficiency of object detection have significantly improved. Nevertheless, challenges remain in optimizing the performance of mainstream detection algorithms, improving the accuracy of small object detection, enabling multi-class detection, and developing lightweight models. In response to these challenges, this paper provides a comprehensive literature review, analyzing approaches to enhance mainstream object detection by exploring advancements in backbone networks, expanding the visual receptive field, feature fusion, and various training strategies. We also evaluate the performance of leading detection models across established datasets, identifying current limitations and proposing future research directions. These directions include enhancing small object representation in datasets, enriching semantic information, and improving model interpretability. Small object detection remains a critical focus in computer vision, and we anticipate the continued development of algorithms with higher accuracy and efficiency

Flexibility in choosing decision policies in gathering discrete evidence over time.

The brain can remarkably adapt its decision-making process to suit the dynamic environment and diverse aims and demands. The brain's flexibility can be classified into three categories: flexibility in choosing solutions, decision policies, and actions. We employ two experiments to explore flexibility in decision policy: a visual object categorization task and an auditory object categorization task. Both tasks required participants to accumulate discrete evidence over time, with the only difference being the sensory state of the stimuli. We aim to investigate how the brain demonstrates flexibility in selecting decision policies in different sensory contexts when the solution and action remain the same. Our results indicate that the decision policy of the brain in integrating information is independent of inter-pulse interval across these two tasks. However, the decision policy based on how the brain ranks the first and second pulse of evidence changes flexibly. We show that the sequence of pulses does not affect the choice accuracy in the auditory mode. However, in the visual mode, the first pulse had the larger leverage on decisions. Our research underscores the importance of incorporating diverse contexts to improve our understanding of the brain's flexibility in real-world decision-making.

Heterogeneity in category recognition across the visual field.

Visual information emerging from the extrafoveal locations is important for visual search, saccadic eye movement control, and spatial attention allocation. Our everyday sensory experience with visual object categories varies across different parts of the visual field which may result in location-contingent variations in visual object recognition. We used a body, animal body, and chair two-forced choice object category recognition task to investigate this possibility. Animal body and chair images with various levels of visual ambiguity were presented at the fovea and different extrafoveal locations across the vertical and horizontal meridians. We found heterogeneous body and chair category recognition across the visual field. Specifically, while the recognition performance of the body and chair presented at the fovea were similar, it varied across different extrafoveal locations. The largest difference was observed when the body and chair images were presented at the lower-left and upper-right visual fields, respectively. The lower/upper visual field bias of the body/chair recognition was particularly observed in low/high stimulus visual signals. Finally, when subjects' performances were adjusted for a potential location-contingent decision bias in category recognition by subtracting the category detection in full noise condition, location-dependent category recognition was observed only for the body category. These results suggest heterogeneous body recognition bias across the visual field potentially due to more frequent exposure of the lower visual field to body stimuli.Significance Statement Our study reveals that visual object recognition exhibits notable variations across different visual field regions, with a pronounced bias in recognizing body images in the lower visual field. This heterogeneity in recognition performance suggests that the frequent exposure of certain visual field areas to specific object categories, such as bodies, influences our visual processing abilities. These findings highlight the importance of considering spatial attention and saccadic eye movements in understanding visual object recognition and have potential implications for designing more effective visual information displays and interfaces.

Face pareidolia minimally engages macaque face selective neurons.

The macaque cerebral cortex contains concentrations of neurons that prefer faces over inanimate objects. Although these so-called face patches are thought to be specialized for the analysis of facial signals, their exact tuning properties remain unclear. For example, what happens when an object by chance resembles a face? Everyday objects can sometimes, through the accidental positioning of their internal components, appear as faces. This phenomenon is known as face pareidolia. Behavioral experiments have suggested that macaques, like humans, perceive illusory faces in such objects. However, it is an open question whether such stimuli would naturally stimulate neurons residing in cortical face patches. To address this question, we recorded single unit activity from four fMRI-defined face-selective regions: the anterior medial (AM), anterior fundus (AF), prefrontal orbital (PO), and perirhinal cortex (PRh) face patches. We compared neural responses elicited by images of real macaque faces, pareidolia-evoking objects, and matched control objects. Contrary to expectations, we found no evidence of a general preference for pareidolia-evoking objects over control objects. Although a subset of neurons exhibited stronger responses to pareidolia-evoking objects, the population responses to both categories of objects were similar, and collectively much less than to real macaque faces. These results suggest that neural responses in the four regions we tested are principally concerned with the analysis of realistic facial characteristics, whereas the special attention afforded to face-like pareidolia stimuli is supported by activity elsewhere in the brain.

The cognitive critical brain: modulation of criticality in perception-related cortical regions

The constantly evolving world necessitates a brain that can swiftly adapt and respond to rapid changes. The brain, conceptualized as a system performing cognitive functions through collective neural activity, has been shown to maintain a resting state characterized by near-critical neural dynamics, positioning it to effectively respond to external stimuli. However, how near-criticality is dynamically modulated during task performance remains insufficiently understood. In this study, we utilized the prototypical Ising Hamiltonian model to investigate the modulation of near-criticality in neural activity at the cortical subsystem level during perceptual tasks. Specifically, we simulated 2D-Ising models in silico using structural MRI data and empirically estimated the system's state in vivo using functional MRI data. We first replicated previous findings that the resting state is typically near-critical as captured by the Ising model. Importantly, we observed heterogeneous changes in criticality across cortical subsystems during a naturalistic movie-watching task, with visual and auditory regions fine-tuned closer to criticality. A more fine-grained analysis of the ventral temporal cortex during an object recognition task further revealed that only regions selectively responsive to a specific object category were tuned closer to criticality when processing that object category. In conclusion, our study provides empirical evidence from the domain of perception supporting the cognitive critical brain hypothesis that modulating the criticality of subsystems within the brain's hierarchical and modular organization may be a fundamental mechanism for achieving diverse cognitive functions.

EFFECTIVENESS OF DUAL-LANGUAGE COGNITIVE INTERVENTION IN COGNITIVE HEALTHY AND DEMENTIA BILINGUAL OLDER ADULTS

Abstract There is an increasing interest in using touch-screen devices to conduct cognitive training and collect measurements of cognitive performance. Although research with technology-based intervention has shown benefits in improving cognitive performance with older adults, overall results are mixed (Ge et al, 2018). This study aims to investigate whether our elder-friendly, multi-modal touch-screen, computerized dual-language intervention program would help to improve cognitive skills in cognitive healthy and dementia bilingual older adults in a multilingual society. Fifty-eight older adults, 26 with dementia (Mage 82.77 ± 7.45) and 32 healthy older adults (Mage 72.44 ± 8.42), completed 24 sessions of gameplay over 8-12 weeks. Each session took about 30-45 minutes to complete, and consisted of two cognitive tasks: Object Categorization (OC), and Utility of things (UoT). Participants’ performance was assessed by reaction time (RT) and number of prompts used to reduce task difficulty. Using a two-way ANOVA, we compared the performance of the first 4 and last 4 sessions of the two groups for both tasks. By the last 4 sessions, there were significant improvements in performance of the dementia group (i.e., faster RT and fewer prompts for OC, and fewer prompts for UoT; post-hoc ps<.002). Performance in the cognitively-healthy group remained stable (ps>.11), and they consistenly outperformed the dementia group across the intervention (ps<.001). These improvements suggest that our cognitive intervention program could be effective for older adults with dementia. In sum, bilingual elder-friendly touch-screen platforms can be a novel yet effective method to study cognitive interventions in populations of older multilinguals.

Unsupervised Domain Adaptation Semantic Segmentation of Remote Sensing Imagery with Scene Covariance Alignment

Remote sensing imagery (RSI) segmentation plays a crucial role in environmental monitoring and geospatial analysis. However, in real-world practical applications, the domain shift problem between the source domain and target domain often leads to severe degradation of model performance. Most existing unsupervised domain adaptation methods focus on aligning global-local domain features or category features, neglecting the variations of ground object categories within local scenes. To capture these variations, we propose the scene covariance alignment (SCA) approach to guide the learning of scene-level features in the domain. Specifically, we propose a scene covariance alignment model to address the domain adaptation challenge in RSI segmentation. Unlike traditional global feature alignment methods, SCA incorporates a scene feature pooling (SFP) module and a covariance regularization (CR) mechanism to extract and align scene-level features effectively and focuses on aligning local regions with different scene characteristics between source and target domains. Experiments on both the LoveDA and Yanqing land cover datasets demonstrate that SCA exhibits excellent performance in cross-domain RSI segmentation tasks, particularly outperforming state-of-the-art baselines across various scenarios, including different noise levels, spatial resolutions, and environmental conditions.

The Evolution of Construction 5.0: Challenges and Opportunities for the Construction Industry

Progress and development in the overall industrial sector have been revolutionized by the advent of advanced digital tools and technologies, transitioning towards Industry 5.0 (I5.0), and the term Construction 5.0 (C5.0) is derived from the emergence of I5.0 in Europe. Given its currency, C5.0 remains a nascent and under-explored research area. This review article presents a bibliometric analysis of studies on C5.0, identifying and analyzing challenges and opportunities in its adoption and exploring the strategies to overcome such challenges and ways to maximize productivity from potential opportunities. Fifty-seven relevant documents were studied to accomplish the aim of this study, which were identified from the Scopus database, evaluated, and included based on the PRISMA framework. The findings showed a relatively small but growing body of literature on C5.0 research, which is disseminated globally and grouped into four specific objective categories. This pattern suggests a growing recognition of C5.0’s potential in the construction field, reflecting its expanding influence in the scientific community. Furthermore, the study examined seven critical challenges, including insufficient technological maturity, the absence of standardization, privacy concerns, ethical considerations, and more. It also explores four potential opportunities associated with the adoption of the C5.0 vision, emphasizing its alignment with societal objectives, sustainability initiatives, personalization, and profitability.

Attention modulates human visual responses to objects by tuning sharpening

Visual stimuli compete with each other for cortical processing and attention biases this competition in favor of the attended stimulus. How does the relationship between the stimuli affect the strength of this attentional bias? Here, we used functional MRI to explore the effect of target-distractor similarity in neural representation on attentional modulation in the human visual cortex using univariate and multivariate pattern analyses. Using stimuli from four object categories (human bodies, cats, cars, and houses), we investigated attentional effects in the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA. We demonstrated that the strength of the attentional bias toward the target is not fixed but decreases with increasing target-distractor similarity. Simulations provided evidence that this result pattern is explained by tuning sharpening rather than an increase in gain. Our findings provide a mechanistic explanation for the behavioral effects of target-distractor similarity on attentional biases and suggest tuning sharpening as the underlying mechanism in object-based attention.

Attention modulates human visual responses to objects by tuning sharpening.

Visual stimuli compete with each other for cortical processing and attention biases this competition in favor of the attended stimulus. How does the relationship between the stimuli affect the strength of this attentional bias? Here, we used functional MRI to explore the effect of target-distractor similarity in neural representation on attentional modulation in the human visual cortex using univariate and multivariate pattern analyses. Using stimuli from four object categories (human bodies, cats, cars, and houses), we investigated attentional effects in the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA. We demonstrated that the strength of the attentional bias toward the target is not fixed but decreases with increasing target-distractor similarity. Simulations provided evidence that this result pattern is explained by tuning sharpening rather than an increase in gain. Our findings provide a mechanistic explanation for the behavioral effects of target-distractor similarity on attentional biases and suggest tuning sharpening as the underlying mechanism in object-based attention.

Resting‐State Network Plasticity Following Category Learning Depends on Sensory Modality

ABSTRACTLearning new categories is fundamental to cognition, occurring in daily life through various sensory modalities. However, it is not well known how acquiring new categories can modulate the brain networks. Resting‐state functional connectivity is an effective method for detecting short‐term brain alterations induced by various modality‐based learning experiences. Using fMRI, our study investigated the intricate link between novel category learning and brain network reorganization. Eighty‐four adults participated in an object categorization experiment utilizing visual (n = 41, with 20 females and a mean age of 23.91 ± 3.11 years) or tactile (n = 43, with 21 females and a mean age of 24.57 ± 2.58 years) modalities. Resting‐state networks (RSNs) were identified using independent component analysis across the group of participants, and their correlation with individual differences in object category learning across modalities was examined using dual regression. Our results reveal an increased functional connectivity of the frontoparietal network with the left superior frontal gyrus in visual category learning task and with the right superior occipital gyrus and the left middle temporal gyrus after tactile category learning. Moreover, the somatomotor network demonstrated an increased functional connectivity with the left parahippocampus exclusively after tactile category learning. These findings illuminate the neural mechanisms of novel category learning, emphasizing distinct brain networks' roles in diverse modalities. The dynamic nature of RSNs emphasizes the ongoing adaptability of the brain, which is essential for efficient novel object category learning. This research provides valuable insights into the dynamic interplay between sensory learning, brain plasticity, and network reorganization, advancing our understanding of cognitive processes across different modalities.

Cognitive load of three levels of object categories in reading tasks: evidence from eye movements

ABSTRACT To evaluate the cognitive load of Chinese basic-level categories, eye movement measures of naturalistic reading and proofreading tasks were used. 36 participants completed two reading tasks, each involving 21 groups of sentences containing category terms of varying levels. The results indicate that the difficulty of reading tasks does not influence initial category processing, but only affects late overall processing. The cognitive economy of basic-level categories appears as the difficulty and complexity of reading tasks increase. Specifically, in the proofreading task, the cognitive load of basic-level categories is lower than that of the subordinate categories. The processing difficulty of basic-level categories in naturalistic reading is equivalent to that of the superordinate category in the overall processing. Furthermore, generalisation plays the same role as gestalt in the judgment of category cognitive economy. It results in smaller differences in processing difficulty and cognitive load between the superordinate and basic-level categories in local processing.

Becoming articulate: women writing on the visual arts in Renaissance Italy

Despite continuing interest in the contribution made by women to the material culture of Renaissance Italy, little attention has been devoted to their writings on the subject, although there is much material, both informal and intended for publication. This paper will attempt a preliminary charting of the area, by selecting letters and poems from c1450-1580 by a range of women which speak of actual or fictional artifacts. Although these are predominantly from those categories of objects which especially appealed to women in the period, namely small devotional works, textiles and portraits, the primary aim will not be to argue for a specifically feminine taste influenced by social and cultural factors. Rather, the paper will try to place women’s writing within the context of a developing critical language for discussing the art of which at least some women could be aware. Three broad phases will be identified within this evolving discourse.

Curating computer heritage: a case study

Preserving legacy computer systems poses a complex challenge, given the inherent intricacies of these devices. While numerous organizations dedicated to safeguarding computing heritage have identified key challenges and produced reports and general guidelines for best preservation practices, the vast array of object categories, types, and numerous other factors may render such studies inadequate for the preservation task at hand. This paper is focused on specific challenges in the preservation of historical computer systems, using the MCM/70 preservation project conducted at the York University Computer Museum as a case study.

SS3DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion

Learning object shapes from a single image is challenging due to variations in scene content, geometric structures, and environmental factors, which create significant disparities between 2D image features and their corresponding 3D representations, hindering the effective training of deep learning models. Existing learning-based approaches can be divided into two-stage and single-stage methods, each with limitations. Two-stage methods often rely on generating intermediate proposals by searching for similar structures across the entire dataset, a process that is computationally expensive due to the large search space and high-dimensional feature-matching requirements, further limiting flexibility to predefined object categories. In contrast, single-stage methods directly reconstruct 3D shapes from images without intermediate steps, but they struggle to capture complex object geometries due to high feature loss between image features and 3D shapes and limit their ability to represent intricate details. To address these challenges, this paper introduces SS3DNet-AF, a single-stage, single-view 3D reconstruction network with an attention-based fusion (AF) mechanism to enhance focus on relevant image features, effectively capturing geometric details and generalizing across diverse object categories. The proposed method is quantitatively evaluated using the ShapeNet dataset, demonstrating its effectiveness in achieving accurate 3D reconstructions while overcoming the computational challenges associated with traditional approaches.

Innovative CNN approach for reliable chicken meat classification in the poultry industry

In response to the burgeoning need for advanced object recognition and classification, this research embarks on a journey harnessing the formidable capabilities of Convolutional Neural Networks (CNNs). The central aim of this study revolves around the precise identification and categorization of objects, with a specific focus on the critical task of distinguishing between fresh and spoiled chicken meat. This study's overarching objective is to craft a robust CNN-based classification model that excels in discriminating between objects. In the context of our research, we set out to create a model adept at distinguishing between fresh and rotten chicken meat. This endeavor holds immense potential in augmenting food safety and elevating quality control standards within the poultry industry. Our research methodology entails meticulous data collection, which includes acquiring high-resolution images of chicken meat. This meticulously curated dataset serves as the bedrock for both training and testing our CNN model. To optimize the model, we employ the 'adam' optimizer, while critical performance metrics, such as accuracy, precision, recall, and the F1-score, are methodically computed to evaluate the model's effectiveness. Our experimental findings unveil the remarkable success of our CNN model, with consistent accuracy, precision, and recall metrics all reaching an impressive pinnacle of 94%. These metrics underscore the model's excellence in the realm of object classification, with a particular emphasis on its proficiency in distinguishing between fresh and rotten chicken meat. In summation, our research concludes that the CNN model has exhibited exceptional prowess in the domains of object recognition and classification. The model's high accuracy signifies its precision in furnishing accurate predictions, while its elevated precision and recall values accentuate its effectiveness in differentiating between object classes. Consequently, the CNN model stands as a robust foundation for future strides in object classification technology. As we peer into the horizon of future research, myriad opportunities beckon. Our CNN model's applicability extends beyond chicken meat classification, inviting exploration across diverse domains. Furthermore, the model's refinement and adaptation for specific challenges represent an exciting avenue for future work, promising heightened performance across a broader spectrum of object recognition tasks.

Sculpting new visual categories into the human brain

Learning requires changing the brain. This typically occurs through experience, study, or instruction. We report an alternate route for humans to acquire visual knowledge, through the direct sculpting of activity patterns in the human brain that mirror those expected to arise through learning. We used neurofeedback from closed-loop real-time functional MRI to create new categories of visual objects in the brain, without the participants' explicit awareness. After neural sculpting, participants exhibited behavioral and neural biases for the learned, but not for the control categories. The ability to sculpt new perceptual distinctions into the human brain offers a noninvasive research paradigm for causal testing of the link between neural representations and behavior. As such, beyond its current application to perception, our work potentially has broad relevance for advancing understanding in other domains of cognition such as decision-making, memory, and motor control.