Visual Task Research Articles

Distinct visual processing networks for foveal and peripheral visual fields

Foveal and peripheral vision are two distinct modes of visual processing essential for navigating the world. However, it remains unclear if they engage different neural mechanisms and circuits within the visual attentional system. Here, we trained macaques to perform a free-gaze visual search task using natural face and object stimuli and recorded a large number of 14588 visually responsive units from a broadly distributed network of brain regions involved in visual attentional processing. Foveal and peripheral units had substantially different proportions across brain regions and exhibited systematic differences in encoding visual information and visual attention. The spike-local field potential (LFP) coherence of foveal units was more extensively modulated by both attention and visual selectivity, thus indicating differential engagement of the attention and visual coding network compared to peripheral units. Furthermore, we delineated the interaction and coordination between foveal and peripheral processing for spatial attention and saccade selection. Together, the systematic differences between foveal and peripheral processing provide valuable insights into how the brain processes and integrates visual information from different regions of the visual field.

The effects of multitalker babble noise on children's cognitive performance

Classroom noise, particularly irrelevant speech or background babble noise, can have detrimental effects on academic tasks and learning more generally. Researchers have proposed two alternative explanations of the interference of babble noise on non-auditory academic tasks, such as reading or math: i) Babble noise may generate interference on these tasks due to the automatic access of speech sounds (babbling) to phonological short-term memory; or ii) by capturing children's attention, thereby diverting general attentional resources from the task. To test these hypotheses, we asked 3rd to 5th graders to perform two visual attention and two verbal working memory tasks (digit span and reading span) in both quiet and noisy conditions. Multitalker babble noise was administered through headphones at 65dB. The results revealed significant noise effects only in the most complex verbal working memory task (reading span). Interestingly, a significant difference between conditions was also observed in one of the visual attention tasks, where higher scores were, however, achieved in the presence of noise. These preliminary findings will be discussed in light of the current literature.

Active noise-cancelling headphones do not improve cognitive performance in office noise environment

Our purpose was to determine the influence of different use settings of on-ear ANC (active noise-cancelling) headphones on performance, experience, and physiological stress in conditions resembling an office where task irrelevant speech is present. Laboratory experiment involved 54 participants, who were exposed to five conditions: 1. No headphones, 2. Headphones without ANC operation, 3. Headphones with ANC operation, 4. Headphones with sound masking playback, 5. Headphones with ANC operation and sound masking playback. In all conditions, speech was present in the room at 52 dB LAeq. Masking was 51 dB LAeq wideband noise played from the headphones. Participants' performance was measured with visual serial recall and 3-back tasks, and physiological stress with heart rate variability. The condition did not influence performances nor physiological stress. The only effects of sound condition were observed in subjective experiences. With masking (conditions 4 and 5), the speech was less annoying than without the headphones (condition 1). Sound environment was estimated to impair performance and disturb concentration less than without the headphones only in condition 5. Therefore, using headphones together with masking and ANC function can improve experience. Using headphones with ANC function alone did not improve performance nor reduce stress level.

Central tendency and serial dependence in vestibular path integration.

Path integration, the process of updating one's position using successive self-motion signals, has previously been studied using visual distance reproduction tasks in which optic flow patterns provide information about traveled distance. These studies have reported that reproduced distances show two types of systematic biases: central tendency and serial dependence. In the present study, we investigated whether these biases are also present in vestibular path integration. Participants were seated on a linear motion platform and performed a distance reproduction task in total darkness. The platform first passively moved the participant a pre-defined stimulus distance which they then actively reproduced by steering the platform back the same distance. Stimulus distances were sampled from short- and long-distance probability distributions and presented in either a randomized order or in separate blocks to study the effect of presentation context. Similar to the effects observed in visual path integration, we found that reproduced distances showed an overall positive central tendency effect as well as a positive, attractive serial dependence effect. Furthermore, reproduction behavior was affected by presentation context. These results were mostly consistent with predictions of a Bayesian Kalman-filter model, originally proposed for visual path integration.

Memory-related brain potentials for visual objects in early AD show impairment and compensatory mechanisms.

Impaired episodic memory is the primary feature of early Alzheimer's disease (AD), but not all memories are equally affected. Patients with AD and amnestic Mild Cognitive Impairment (aMCI) remember pictures better than words, to a greater extent than healthy elderly. We investigated neural mechanisms for visual object recognition in 30 patients (14AD, 16 aMCI) and 36 cognitively unimpaired healthy (19 in the "preclinical" stage of AD). Event-related brain potentials (ERPs) were recorded while participants performed a visual object recognition task. Hippocampal occupancy (integrity), amyloid (florbetapir) PET, and neuropsychological measures of verbal & visual memory, executive function were also collected. A right-frontal ERP recognition effect (500-700ms post-stimulus) was seen in cognitively unimpaired participants only, and significantly correlated with memory and executive function abilities. A later right-posterior negative ERP effect (700-900ms) correlated with visual memory abilities across participants with low verbal memory ability, and may reflect a compensatory mechanism. A correlation of this retrieval-related negativity with right hippocampal occupancy (r = 0.55), implicates the hippocampus in the engagement of compensatory perceptual retrieval mechanisms. Our results suggest that early AD patients are impaired in goal-directed retrieval processing, but may engage compensatory perceptual mechanisms which rely on hippocampal function.

Enhancing motor skill learning through multiple sessions of online high-definition transcranial direct current stimulation in healthy adults: insights from EEG power spectrum.

The purpose of this study was to evaluate the influence of high-definition transcranial direct current stimulation (HD-tDCS) on finger motor skill acquisition. Thirty-one healthy adult males were randomly assigned to one of three groups: online HD-tDCS (administered during motor skill learning), offline HD-tDCS (delivered before motor skill learning), and a sham group. Participants engaged in a visual isometric pinch task for three consecutive days. Overall motor skill learning and speed-accuracy tradeoff function were used to evaluate the efficacy of tDCS. Electroencephalography was recorded and power spectral density was calculated. Both online and offline HD-tDCS total motor skill acquisition was significantly higher than the sham group (P < 0.001 and P < 0.05, respectively). Motor skill acquisition in the online group was higher than offline (P = 0.132, Cohen's d =1.46). Speed-accuracy tradeoff function in the online group was higher than both offline and sham groups in the post-test. The online group exhibited significantly lower electroencephalography activity in the frontal, fronto-central, and centro-parietal alpha band regions compared to the sham (P < 0.05). The findings suggest that HD-tDCS application can boost finger motor skill acquisition, with online HD-tDCS displaying superior facilitation. Furthermore, online HD-tDCS reduces the power of alpha rhythms during motor skill execution, enhancing information processing and skill learning efficiency.

Optical Flow as Spatial-Temporal Attention Learners.

Optical flow is an indispensable building block for various important computer vision tasks, including motion estimation, object tracking, and disparity measurement. To date, the dominant methods are CNN-based, leaving plenty of room for improvement. Inthiswork, we propose TransFlow, a transformer architecture for optical flow estimation. Compared to dominant CNN-based methods, TransFlow demonstrates three advantages. First, it provides more accurate correlation and trustworthy matching in flow estimation by utilizing spatial self-attention and cross-attention mechanisms between adjacent frames to effectively capture global dependencies; Second, it recovers more compromised information (e.g. occlusion and motion blur) in flow estimation through long-range temporal association in dynamic scenes; Third, it introduces a concise self-learning paradigm, eliminating the need for complex and laborious multi-stage pre-training procedures. The versatility and superiority of TransFlow extend seamlessly to 3D scene motion, yielding competitive outcomes in 3D scene flow estimation. Our approach attains state-of-the-art results on benchmark datasets such as Sintel and KITTI-15, while also exhibiting exceptional performance on downstream tasks, including video object detection using the ImageNet VID dataset, video frame interpolation using the GoPro dataset, and video stabilization using the DeepStab dataset. We believe that the effectiveness of TransFlow positions it as a flexible baseline for both optical flow and scene flow estimation, offering promising avenues for future research and development.

Adaptive search for broad attention based vision transformers

Vision Transformer (ViT) has prevailed among computer vision tasks for its powerful capability of image representation recently. Frustratingly, the manual design of efficient architectures for ViTs can be laborious, often involving repetitive trial and error processes. Furthermore, the exploration of lightweight ViTs remains limited, resulting in inferior performance compared to convolutional neural networks. To tackle these challenges, we propose Adaptive Search for Broad attention based Vision Transformers, called ASB, which automates the design of efficient ViT architectures by utilizing the broad search space and an adaptive evolutionary algorithm. The broad search space facilitates the exploration of a novel connection paradigm, enabling more comprehensive integration of attention information to improve ViT performance. Additionally, an efficient adaptive evolutionary algorithm is developed to efficiently explore architectures by dynamically learning the probability distribution of candidate operators. Our experimental results demonstrate that the adaptive evolution in ASB efficiently learns excellent lightweight models, achieving a 55% improvement in convergence speed over traditional evolutionary algorithms. Moreover, the effectiveness of ASB is validated across several visual tasks. For instance, on ImageNet classification, the searched model attains a performance of 77.8% with 6.5M parameters and outperforms state-of-the-art models, including EfficientNet and EfficientViT networks. On mobile COCO panoptic segmentation, our approach delivers 43.7% PQ. On mobile ADE20K semantic segmentation, our method attains 40.9% mIoU. The code and pre-trained models will be available soon in ASB-Code.

Click to Correction: Interactive Bidirectional Dynamic Propagation Video Object Segmentation Network.

High-quality video object segmentation is a challenging visual computing task. Interactive segmentation can improve segmentation results. This paper proposes a multi-round interactive dynamic propagation instance-level video object segmentation network based on click interaction. The network consists of two parts: a user interaction segmentation module and a bidirectional dynamic propagation module. A prior segmentation network was designed in the user interaction segmentation module to better segment objects of different scales that users click on. The dynamic propagation network achieves high-precision video object segmentation through the bidirectional propagation and fusion of segmentation masks obtained from multiple rounds of interaction. Experiments on interactive segmentation datasets and video object segmentation datasets show that our method achieves state-of-the-art segmentation results with fewer click interactions.

Negative aftereffects of face trait impressions are modulated by emotional expressions

ABSTRACT Facial trustworthiness impressions critically shape our everyday social interactions. While previous research has predominantly considered trustworthiness impressions to be stable over time, preliminary evidence has shown that they are affected by visual adaptation, such that long exposure to (un)trustworthy-looking faces biases the perception of following faces in the opposite trustworthiness direction. Here, by employing a visual adaptation task across two experiments, we sought further evidence that trustworthiness impressions are shaped by the temporal context. In Experiment 1, we investigated whether visual adaptation affect trustworthiness judgements and found evidence of robust negative face aftereffect. In Experiment 2, we focused our investigation on whether emotional expressions, key cues involved in trait impressions, influence trustworthiness and dominance impressions. We found that adaptation to anti-expressions, which were expected to bias subsequent neutral faces to resemble the original expression (happiness, anger, and fear), significantly modulated subsequent evaluations of trustworthiness and dominance. This result confirms the critical role of emotion perception in trait evaluations. Importantly, using anti-expressions minimised semantic adaptation, thus highlighting the perceptual nature of this aftereffect. Taken together, our findings confirm that temporal context shapes trustworthiness impressions, by showing that visual adaptation affects trust judgements, and that past emotional expressions influence following impressions of trustworthiness and dominance.

Visual mental imagery of nonpredictive central social cues triggers automatic attentional orienting

Previous research has demonstrated that social cues (e.g., eye gaze, walking direction of biological motion) can automatically guide people's focus of attention, a well-known phenomenon called social attention. The current research shows that voluntarily generated social cues via visual mental imagery, without being physically presented, can produce robust attentional orienting similar to the classic social attentional orienting effect. Combining a visual imagery task with a dot-probe task, we found that imagining a non-predictive gaze cue could orient attention towards the gazed-at hemifield. Such attentional effect persisted even when the imagery gaze cue was counter-predictive of the target hemifield, and could be generalized to biological motion cue. Besides, this effect could not be simply attributed to low-level motion signal embedded in gaze cues. More importantly, an eye-tracking experiment carefully monitoring potential eye movements demonstrated the imagery-induced attentional orienting effect induced by social cues, but not by non-social cues (i.e., arrows), suggesting that such effect is specialized to visual imagery of social cues. These findings accentuate the demarcation between social and non-social attentional orienting, and may take a preliminary step in conceptualizing voluntary visual imagery as a form of internally directed attention.

Recurrency time entropy of brain wave rhythms as an indicator of performance on visual search tasks in schoolchildren

Recurrency time entropy of brain wave rhythms as an indicator of performance on visual search tasks in schoolchildren

Object Detection Using Capsule Neural Network: An Overview

Appropriate remedies for tasks like language translation, object identification, object segmentation, picture recognition, and natural language processing are needed for today's computer vision tasks. This paper explores the use of Capsule neural Networks (Caps Nets) in object identification and offers a thorough analysis of their developments and uses. It presents an analysis of the transformative impact of Caps Nets on object identification tasks. This distinctive the architecture of neural network is presented as an alternative to convolutional neural networks (CNNs). The paper highlights that how Caps Nets are unique in capturing spatial correlations and hierarchical patterns in visualization data by analyzing the fundamental ideas of the technology. Additionally, it demonstrates how Caps net out perform CNNs in terms of generalization, interpretability, and last in the resistance to spatial distortions, confirming their goodness at object detection. By integrating the Caps networks with the latest scientific findings and advances, the paper shows the current status and potential future paths for object detection methods that use this leading neural network architecture.

Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration.

The deep unfolding approach has attracted significant attention in computer vision tasks, which well connects conventional image processing modeling manners with more recent deep learning techniques. Specifically, by establishing a direct correspondence between algorithm operators at each implementation step and network modules within each layer, one can rationally construct an almost "white box" network architecture with high interpretability. In this architecture, only the predefined component of the proximal operator, known as a proximal network, needs manual configuration, enabling the network to automatically extract intrinsic image priors in a data-driven manner. In current deep unfolding methods, such a proximal network is generally designed as a CNN architecture, whose necessity has been proven by a recent theory. That is, CNN structure substantially delivers the translational symmetry image prior, which is the most universally possessed structural prior across various types of images. However, standard CNN-based proximal networks have essential limitations in capturing the rotation symmetry prior, another universal structural prior underlying general images. This leaves a large room for further performance improvement in deep unfolding approaches. To address this issue, this study makes efforts to suggest a high-accuracy rotation equivariant proximal network that effectively embeds rotation symmetry priors into the deep unfolding framework. Especially, we deduce, for the first time, the theoretical equivariant error for such a designed proximal network with arbitrary layers under arbitrary rotation degrees. This analysis should be the most refined theoretical conclusion for such error evaluation to date and is also indispensable for supporting the rationale behind such networks with intrinsic interpretability requirements. Through experimental validation on different vision tasks, including blind image super-resolution, medical image reconstruction, and image de-raining, the proposed method is validated to be capable of directly replacing the proximal network in current deep unfolding architecture and readily enhancing their state-of-the-art performance. This indicates its potential usability in general vision tasks.

Complex trade-offs in a dual-target visual search task are indexed by lateralised ERP components

In everyday tasks, the choices we make incorporate complex trade-offs between conflicting factors that affect how we will achieve our goals. Previous experimental research has used dual-target visual search to determine how people flexibly adjust their behaviour and make choices that optimise their decisions. In this experiment, we leveraged a visual search task that incorporates complex trade-offs, and electroencephalography (EEG), to understand how neural mechanisms of selective attention contribute to choice behaviour in these tasks. On each trial, participants could choose to respond to the gap location on either of two possible targets. Each target was colour coded such that colour indicated which of the two had the easier gap discrimination. Orthogonally, we manipulated the set size of coloured distractors to modulate how efficiently each target could be found. As a result, optimised task performance required participants to trade-off conflicts between the ease of finding a target given the current set size, and the ease of making its associated gap discrimination. Our results confirm that participants are able to flexibly adjust their behaviour, and trade-off these two factors to maintain their response speed and accuracy. Additionally, the N2pc and SPCN components elicited by search displays could reliably predict the choice that participants would ultimately make on a given trial. These results suggest that initial attentional processes may help to determine the choice participants make, highlighting the central role that attention may play in optimising performance on complex tasks.

HADT: Image super-resolution restoration Using Hybrid Attention-Dense Connected Transformer Networks

Image super-resolution (SR) plays a vital role in vision tasks, in which Transformer-based methods outperform conventional convolutional neural networks. Existing work usually uses residual linking to improve the performance, but this type of linking provides limited information transfer within the block. Also, existing work usually restricts the self-attention computation to a single window to improve feature extraction. This means transformer-based networks can only use feature information within a limited spatial range. To handle the challenge, this paper proposes a novel Hybrid Attention-Dense Connected Transformer Network (HADT) to utilize the potential feature information better. HADT is constructed by stacking an attentional transformer block (ATB), which contains an Effective Dense Transformer Block (EDTB) and a Hybrid Attention Block (HAB). EDTB combines dense connectivity and swin-transformer to enhance feature transfer and improve model representation, and meanwhile, HAB is used for cross-window information interaction and joint modeling of features for better visualization. Based on the experiments, our method is effective on SR tasks with magnification factors of 2, 3, and 4. For example, using the Urban100 dataset in an experiment with an amplification factor of 4 our method has a PSNR value that is 0.15 dB higher than the previous method and reconstructs a more detailed texture.

Visual-guided hierarchical iterative fusion for multi-modal video action recognition

Vision-Language models(VLMs) have shown promising improvements on various visual tasks. Most existing VLMs employ two separate transformer-based encoders, each dedicated to modeling visual and language features independently. Because the visual features and language features are unaligned in the feature space, it is challenging for the multi-modal encoder to learn vision-language interactions. In this paper, we propose a V,isual-guided Hierarchical Iterative Fusion (VgHIF) method for VLMs in video action recognition, which acquires more discriminative vision and language representation. VgHIF leverages visual features from different levels in visual encoder to interact with language representation. The interaction is processed by the attention mechanism to calculate the correlation between visual features and language representation. VgHIF learns grounded video-text representation and supports many different pre-trained VLMs in a flexible and efficient manner with a tiny computational cost. We conducted experiments on the Kinetics-400 Mini Kinetics 200 HMDB51, and UCF101 using VLMs: CLIP, X-CLIP, and ViFi-CLIP. The experiments were conducted under full supervision and few shot settings, and compared with the baseline multi-modal model without VgHIF, the Top-1 accuracy of the proposed method has been improved to varying degrees, and several groups of results have achieved comparable results with state-of-the-art performance, which strongly verified the effectiveness of the proposed method.

Quantifying neurovascular coupling through a concurrent assessment of arterial, capillary, and neuronal activation in humans: A multimodal EEG-fNIRS-TCD investigation.

This study explored a novel multimodal neuroimaging approach to assess neurovascular coupling (NVC) in humans using electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and transcranial Doppler ultrasound (TCD). Fifteen participants (nine females; age 19-32) completed concurrent EEG-fNIRS-TCD imaging during motor (finger tapping) and visual ("Where's Waldo?") tasks, with synchronized monitoring of blood pressure, capnography, and heart rate. fNIRS assessed microvascular oxygenation within the frontal, motor, parietal, and occipital cortices, while the middle and posterior cerebral arteries (MCA/PCA) were insonated using TCD. A 16-channel EEG set-up was placed according to the 10-20 system. Wilcoxon signed-rank tests were used to compare physiological responses between the active and resting phases of the tasks, while cross-correlations with zero legs compared cerebral and systemic hemodynamic responses across both tasks. Time-frequency analysis demonstrated a reduction in alpha and low beta band power in electrodes C3/C4 during finger tapping (p<0.045) and all electrodes during the Waldo task (all p<0.001). During Waldo, cross-correlation analysis demonstrated the change in oxygenated hemoglobin and cerebral blood velocity had a moderate-to-strong negative correlation with systemic physiological influences, highlighting the measured change resulted from neuronal input. Deoxygenated hemoglobin displayed the greatest negative cross-correlation with the MCA/PCA within the motor cortices and visual during the motor and visual tasks, respectively (range: -0.54, -0.82). This investigation demonstrated the feasibility of the proposed EEG-fNIRS-TCD response to comprehensively assess the NVC response within human, specifically quantifying the real-time temporal synchrony between neuronal activation (EEG), microvascular oxygenation changes (fNIRS), and conduit artery velocity alterations (TCD).

Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral image classification

Hyperspectral image (HSI) classification plays a crucial role in remote sensing (RS) applications, enabling the precise identification of materials and land cover based on spectral information. This supports tasks such as agricultural management and urban planning. While sequential neural models like Recurrent Neural Networks (RNNs) and Transformers have been adapted for this task, they present limitations: RNNs struggle with feature aggregation and are sensitive to noise from interfering pixels, whereas Transformers require extensive computational resources and tend to underperform when HSI datasets contain limited or unbalanced training samples. To address these challenges, Mamba architectures have emerged, offering a balance between RNNs and Transformers by leveraging lightweight, parallel scanning capabilities. Although models like Vision Mamba (ViM) and Visual Mamba (VMamba) have demonstrated improvements in visual tasks, their application to HSI classification remains underexplored, particularly in handling land-cover semantic tokens and multi-scale feature aggregation for patch-wise classifiers. In response, this study introduces the Mamba-in-Mamba (MiM) architecture for HSI classification, marking a pioneering effort in this domain. The MiM model features: (1) a novel centralized Mamba-Cross-Scan (MCS) mechanism for efficient image-to-sequence data transformation; (2) a Tokenized Mamba (T-Mamba) encoder that incorporates a Gaussian Decay Mask (GDM), Semantic Token Learner (STL), and Semantic Token Fuser (STF) for enhanced feature generation; and (3) a Weighted MCS Fusion (WMF) module with a Multi-Scale Loss Design for improved training efficiency. Experimental results on four public HSI datasets—Indian Pines, Pavia University, Houston2013, and WHU-Hi-Honghu—demonstrate that our method achieves an overall accuracy improvement of up to 3.3%, 2.7%, 1.5%, and 2.3% over state-of-the-art approaches (i.e., SSFTT, MAEST, etc.) under both fixed and disjoint training-testing settings.

Prospective Evaluation of Clinical and Patient-Reported Outcomes Following Contralateral Implantation of an Extended Depth of Focus (EDOF) and Hybrid EDOF-Multifocal IOL.

To evaluate clinical outcomes following contralateral implantation of a diffractive extended depth of focus (EDOF) and a hybrid EDOF-multifocal intraocular lens (IOL) with a violet filter in a U.S.-based population. In this prospective, non-comparative study, 60 eyes of 30 patients aged 40 years and older underwent bilateral cataract surgery and implantation of a DXR00V IOL (Tecnis Symfony OptiBlue) in the dominant eye and a DFR00V (Tecnis Synergy) IOL in the non-dominant eye. Outcome measures included uncorrected (UDVA) and corrected (CDVA) distance visual acuity, distance-corrected intermediate (DCIVA, 66 cm), near and very near visual acuity (DCNVA, 40 and 33 cm), and patient-reported outcomes. At 6 months postoperatively, binocular mean UDVA and CDVA were -0.10 ± 0.07 and -0.11 ± 0.05 logarithm of the minimum angle of resolution (logMAR), respectively. The mean DCIVA was 0.26 ± 0.08 logMAR in the DXR00V eyes and 0.23 ± 0.06 logMAR in the DFR00V eyes. The mean DCNVA at 40 and 33 cm were 0.28 ± 0.12 and 0.30 ± 0.15 logMAR, respectively, in the DXR00V eyes and 0.07 ± 0.08 and 0.07 ± 0.10 logMAR, respectively, in the DFR00V eyes. The mean binocular DCIVA and DCNVA at 33 and 40 cm were 0.18 ± 0.05, 0.06 ± 0.08, and 0.06 ± 0.07 logMAR, respectively. The percentages of patients achieving spectacle independence at far, intermediate, and near distances were 100%, 100%, and 81%, respectively. Contralateral implantation of the diffractive EDOF and a hybrid EDOF-multifocal IOL yielded excellent binocular visual acuity at all distances, as well as high patient satisfaction and functional performance on visual tasks. [J Refract Surg. 2024;40(10):e699-e705.].