Visual Object Research Articles

Sensorimotor mechanisms selective to numerosity derived from individual differences.

We have previously shown that after few seconds of adaptation by finger-tapping, the perceived numerosity of spatial arrays and temporal sequences of visual objects displayed near the tapping region is increased or decreased, implying the existence of a sensorimotor numerosity system (Anobile et al., 2016). To date, this mechanism has been evidenced only by adaptation. Here, we extend our finding by leveraging on a well-established covariance technique, used to unveil and characterize 'channels' for basic visual features such as colour, motion, contrast, and spatial frequency. Participants were required to press rapidly a key a specific number of times, without counting. We then correlated the precision of reproduction for various target number presses between participants. The results showed high positive correlations for nearby target numbers, scaling down with numerical distance, implying tuning selectivity. Factor analysis identified two factors, one for low and the other for higher numbers. Principal component analysis revealed two bell-shaped covariance channels, peaking at different numerical values. Two control experiments ruled out the role of non-numerical strategies based on tapping frequency and response duration. These results reinforce our previous reports based on adaptation, and further suggest the existence of at least two sensorimotor number channels responsible for translating symbolic numbers into action sequences.

Sensorimotor mechanisms selective to numerosity derived from individual differences

We have previously shown that after few seconds of adaptation by finger-tapping, the perceived numerosity of spatial arrays and temporal sequences of visual objects displayed near the tapping region is increased or decreased, implying the existence of a sensorimotor numerosity system (Anobile et al., 2016). To date, this mechanism has been evidenced only by adaptation. Here, we extend our finding by leveraging on a well-established covariance technique, used to unveil and characterize ‘channels’ for basic visual features such as colour, motion, contrast, and spatial frequency. Participants were required to press rapidly a key a specific number of times, without counting. We then correlated the precision of reproduction for various target number presses between participants. The results showed high positive correlations for nearby target numbers, scaling down with numerical distance, implying tuning selectivity. Factor analysis identified two factors, one for low and the other for higher numbers. Principal component analysis revealed two bell-shaped covariance channels, peaking at different numerical values. Two control experiments ruled out the role of non-numerical strategies based on tapping frequency and response duration. These results reinforce our previous reports based on adaptation, and further suggest the existence of at least two sensorimotor number channels responsible for translating symbolic numbers into action sequences.

Visual Quality Assessment After FS-LASIK Using Customized Aspheric Ablation Profile for Age-Related Accommodation Deficiency Compensation.

To evaluate clinical outcomes and visual quality 12 months after femtosecond laser-assisted laser in situ keratomileusis (FS-LASIK) performed with the Custom-Q algorithm for correction of myopia with or without astigmatism and compensate for age-related accommodation deficiency. Patients who had Custom-Q FS-LASIK for myopia and myopic astigmatism with age-related accommodation deficiency were included in this retrospective study. Distance, intermediate, and near visual acuities, objective and subjective refractions, Q-factor, corneal higher order aberrations (HOAs), accommodation function, defocus curve, contrast sensitivity, and a subjective questionnaire assessing visual quality were evaluated 12 months postoperatively. Clinical data of 43 cases were analyzed. The mean age was 42.02 ± 1.85 years (range: 40 to 48 years). At the 12-month follow-up visit, there were 43 (100%), 42 (97%), and 33 (77%) patients who achieved a binocular uncorrected distance, intermediate, and near visual acuity, respectively, better than 20/20 separately. Defocus curves revealed better distance vision in the dominant eyes, and the nondominant eyes performed better at intermediate and near vergence (P < .001). The Q-value and corneal spherical aberration coefficient were more positive in the dominant eyes than those in the nondominant eyes (P < .001). The accommodative amplitude and relative accommodation improved binocularly (P < .001). The questionnaire demonstrated high patient satisfaction with near vision, and no one reported having severe visual disturbance. For myopic patients with age-related accommodation deficiency, the Custom-Q algorithm proved to be an effective way to achieve acceptable near vision without compromising distance vision. [J Refract Surg. 2024;40(4):e245-e252.].

Moving stimuli enhance beat timing and sensorimotor coupling in vision.

Vision has long been known for its inefficiency in beat perception and synchronization. However, this has been challenged by the finding that moving stimuli (bouncing ball or moving bar) can significantly improve visual beat synchronization. The present study examined two possible mechanisms for this phenomenon: visual motion facilitates temporal processing or promotes sensorimotor coupling. Instead of a single visual object (such as a ball or bar), random-dot kinematograms (RDKs) were used to construct visual motion sequences to avoid confounding factors, such as changes in trajectory and velocity. Experiment 1 showed that RDKs improved beat-timing discrimination compared with visual flashes, but auditory tones were still superior to RDKs. In Experiment 2, synchronized movements improved auditory-tone beat timing but impaired visual-flash beat timing, with no effect on RDK beat timing. Experiment 3 indicated that the regression slope of the phase correction response in RDKs was higher than that in visual flashes but still lower than that in auditory tones. The results showed that moving stimuli enhances both temporal processing (Experiment 1) and sensorimotor coupling (Experiments 2 and 3) in vision, but to a lesser degree, with audition retaining an advantage. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Smooth pursuit inhibition reveals audiovisual enhancement of fast movement control.

The sudden onset of a visual object or event elicits an inhibition of eye movements at latencies approaching the minimum delay of visuomotor conductance in the brain. Typically, information presented via multiple sensory modalities, such as sound and vision, evokes stronger and more robust responses than unisensory information. Whether and how multisensory information affects ultra-short latency oculomotor inhibition is unknown. In two experiments, we investigate smooth pursuit and saccadic inhibition in response to multisensory distractors. Observers tracked a horizontallymoving dot and were interrupted by an unpredictable visual, auditory, or audiovisual distractor. Distractors elicited a transient inhibition of pursuit eye velocity and catch-up saccade rate within ∼100 ms of their onset. Audiovisual distractors evoked stronger oculomotor inhibition than visual- or auditory-only distractors, indicating multisensory response enhancement. Multisensory response enhancement magnitudes were equal to the linear sum of responses to component stimuli. These results demonstrate that multisensory information affects eye movements even at ultra-short latencies, establishing a lower time boundary for multisensory-guided behavior. We conclude that oculomotor circuits must have privileged access to sensory information from multiple modalities, presumably via a fast, subcortical pathway.

Individual Alpha Frequency Contributes to the Precision of Human Visual Processing.

Brain oscillatory activity within the alpha band has been associated with a wide range of processes encompassing perception, memory, decision-making, and overall cognitive functioning. Individual alpha frequency (IAF) is a specific parameter accounting for the mean velocity of the alpha cycling activity, conventionally ranging between ∼7 and ∼13 Hz. One influential hypothesis has proposed a fundamental role of this cycling activity in the segmentation of sensory input and in the regulation of the speed of sensory processing, with faster alpha oscillations resulting in greater temporal resolution and more refined perceptual experience. However, although several recent theoretical and empirical studies would support this account, contradictory evidence suggests caution and more systematic approaches in the assessment and interpretation of this hypothesis. For example, it remains to be explored to what degree IAF shapes perceptual outcomes. In the present study, we investigated whether inter-individual differences in bias-free visual contrast detection threshold in a large sample of individuals in the general population (n = 122) could be explained by inter-individual differences in alpha pace. Our results show that the contrast needed to correctly identify target stimuli (individual perceptual threshold) is associated with alpha peak frequency (not amplitude). Specifically, individuals who require reduced contrast show higher IAF than individuals requiring higher contrasts. This suggests that inter-individual differences in alpha frequency contribute to performance variability in low-level perceptual tasks, supporting the hypothesis that IAF underlies a fundamental temporal sampling mechanism that shapes visual objective performance, with higher frequencies promoting enhanced sensory evidence per time unit.

I prefer what you can see: The role of visual perspective-taking on the gaze-liking effect

Individuals' gaze on an object usually leads others to prefer that object, which is called the gaze-liking effect. However, it is still unclear whether this effect is driven by social factors (i.e., visual perspective-taking) or the domain-general processing (i.e., attention cueing). This research explored the mechanism of the gaze-liking effect by manipulating the objects' visibility to an avatar in six online one-shot experiments. The results showed that participants' affective evaluation for the object was modulated by the avatar's visual perspective. Specifically, the visible object to the avatar received a higher rating of liking degree. However, when the avatar was replaced with a non-social stimulus, the experimental effect was absent. Furthermore, the gaze-liking effect was robust while controlling for confounding factors such as the distance between the object and the avatar or type of stimuli. These findings provided convincing evidence that the gaze-liking effect involves a process of the other's visual experience and is not merely a by-product of the gaze-cueing effect.

High-resolution vision in pelagic polychaetes

High-resolution object vision - the ability to separate, classify, and interact with specific objects in the environment against the visual background - has only been conclusively shown to have evolved in three of the thirty-five animal phyla: chordates, arthropods, and mollusks (cephalopods)1. However, alciopid polychaetes (Phyllodocidae, Alciopini), which possess a pair of bulbous camera-type eyes, have also been hypothesized to achieve high acuity. In this study, we examined three species of night-active pelagic alciopids from the Mediterranean Sea. Our optical, morphological, and electrophysiological investigations show that their eyes have high spatial acuity and temporal resolution, supporting the notion that they are capable of active, high-resolution object vision. These results encourage interesting hypotheses about the visual ecology of these enigmatic polychaetes.

Formation and Development of Mathematical Literacy in the Context of PISA-2021 Program Concep

This article presents the results of pedagogical experiments, which revealed educational problems that teachers face in the process of teaching mathematical literacy to schoolchildren: a low proportion of practice-oriented tasks in the total number of educational tasks in school mathematics intended for 15-year-old schoolchildren, a passive attitude of teachers to the formation of mathematical literacy among schoolchildren. The analysis of the experimental data made it possible to conclude that the knowledge and skills of teachers on the issues of reducing the standard task of the school mathematics course are insufficient to problem situations in the context of the PISA assessment tasks, compiling task systems for these tasks, containing an algorithm for solving the assigned problem, description of a visual object that allows the visualization of the task. To solve these problems, the paper proposes to apply a modular learning technology in the process of teaching schoolchildren mathematical literacy, an algorithmic method for reducing a standard task to assessment-learning tasks in the context of the PISA-2021 program concept. In the work, examining the task to the problem situation as an object of mental activity of students, the main components of the "Mathematical Reasoning" of the student are highlighted. Namely: recognizes the area of the task; singles out the main components of the tasks; to bring data under known concepts; establishes interdisciplinary links; applies induction, interdisciplinary knowledge in the preparation of a mathematical model; solves a mathematical problem; interprets, evaluates the solution in the context of the phenomenon under consideration. The analysis of algorithms for solving tasks made it possible to identify the following key structural components of mathematical literacy: understanding the context of tasks; the ability to establish correspondences between various objects, apply induction and knowledge, build mathematical models, find solutions to mathematical models of tasks, interpret and evaluate the solutions obtained; have mathematical reasoning skills. The final experimental study confirmed the effectiveness of the developed methodology for the formation and development of mathematical literacy at 15 year old schoolchildren.

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.

Circular polarization sensitive opto-neuromorphic operation at plasmonic hot electron transistor using chiral gold nanoparticles

Opto-neuromorphic operation is critical for biological system to recognize the visual objects and mimicking such operation is important for artificial prosthesis as well as machine vision system for industrial applications. To sophisticatedly mimic biological system, regulation of learning and memorizing efficiency is needed, however engineered synthetic platform has been lack of controllability, which makes huge gap between biological system and synthetic platform. Here we demonstrated controllable learning and memorizing opto-neuromorphic operation at plasmonic hot electron transistor. Especially, circularly polarized light (CPL) sensitive synaptic characteristics and learning experience capability are enabled by incorporating chiral plasmonic nanoparticle. Furthermore, gate voltage gives rise to controllable neuromorphic operation due to hot electron injection and trapping effect, resulting in high remaining synaptic weight of ∼70% at negative gate voltage under CPL excitation. We believe that this discovery makes significant leap toward on-demand in-sensor computing as well as toward bio-realistic device.

Learning Regression Distribution: Information Diffusion from Template to Search for Visual Object Tracking

Article Learning Regression Distribution: Information Diffusion from Template to Search for Visual Object Tracking Shuo Hu *, Jinbo Lu, and Sien Zhou 1 School of Electrical Engineering, Yanshan University, Qinhuangdao 066000, China * Correspondence: hus@ysu.edu.cn Received: 5 July 2023 Accepted: 19 October 2023 Published: 26 March 2024 Abstract: The general paradigm of traditional Siamese networks involves using cross-correlations to fuse features from the backbone, and this paradigm is limited by the inductive bias of the convolution kernel, resulting in the lack of global information. In this paper, we propose the Siamese learning regression distribution (SiamLRD) to address the local limitations of traditional cross-correlation operations on feature fusion and weak self-connections between features within different branches. The SiamLRD uses the cross-attention mechanism to replace cross-correlations between the features of the target region of interest and the template so as to enhance flexibility. Firstly, the original transformer structure is improved to be suitable for convolutional Siamese networks. The improved transformer architecture is then used to replace cross-correlation operations, resulting in more comprehensive feature fusion between branches. Secondly, we introduce a new decoder structure into the novel fusion strategy to enhance the correlation between classification scores and regression accuracy during decoding. Multiple benchmarks are used to test the proposed SiamLRD approach, and it is verified that the proposed approach improves the baseline with 5.8% in terms of AO and 9.7% in terms of SR0.75 on the GOT-10K dataset.

Utilization of generative AI for the characterization and identification of visual unknowns

Current state-of-the-art artificial intelligence (AI) struggles with accurate interpretation of out-of-library objects. One method proposed remedy is analogical reasoning (AR), which utilizes abductive reasoning to draw inferences on an unfamiliar scenario given knowledge about a similar familiar scenario. Currently, applications of visual AR gravitate toward analogy-formatted image problems rather than real-world computer vision data sets. This paper proposes the Image Recognition Through Analogical Reasoning Algorithm (IRTARA) and its “generative AI” version called “GIRTARA” which describes and predicts out-of-library visual objects. IRTARA characterizes the out-of-library object through a list of words called the “term frequency list”. GIRTARA uses the term frequency list to predict what the out-of-library object is. To evaluate the quality of the results of IRTARA, both quantitative and qualitative assessments are used, including a baseline to compare the automated methods with human-generated results. The accuracy of GIRTARA’s predictions is calculated through a cosine similarity analysis. This study observed that IRTARA had consistent results in the term frequency list based on the three evaluation methods for the high-quality results and GIRTARA was able to obtain up to 65% match in terms of cosine similarity when compared to the out-of-library object’s true labels.

Relational Distant Supervision for Image Captioning without Image-Text Pairs

Unsupervised image captioning aims to generate descriptions of images without relying on any image-sentence pairs for training. Most existing works use detected visual objects or concepts as bridge to connect images and texts. Considering that the relationship between objects carries more information, we use the object relationship as a more accurate connection between images and texts. In this paper, we adapt the idea of distant supervision that extracts the knowledge about object relationships from an external corpus and imparts them to images to facilitate inferring visual object relationships, without introducing any extra pre-trained relationship detectors. Based on these learned informative relationships, we construct pseudo image-sentence pairs for captioning model training. Specifically, our method consists of three modules: (1) a relationship learning module that learns to infer relationships from images under the distant supervision; (2) a relationship-to-sentence module that transforms the inferred relationships into sentences to generate pseudo image-sentence pairs; (3) an image captioning module that is trained by using the generated image-sentence pairs. Promising results on three datasets show that our method outperforms the state-of-the-art methods of unsupervised image captioning.

Object-Aware Adaptive-Positivity Learning for Audio-Visual Question Answering

This paper focuses on the Audio-Visual Question Answering (AVQA) task that aims to answer questions derived from untrimmed audible videos. To generate accurate answers, an AVQA model is expected to find the most informative audio-visual clues relevant to the given questions. In this paper, we propose to explicitly consider fine-grained visual objects in video frames (object-level clues) and explore the multi-modal relations (\textit{i.e.}, the object, audio, and question) in terms of feature interaction and model optimization. For the former, we present an end-to-end object-oriented network that adopts a question-conditioned clue discovery module to concentrate audio/visual modalities on respective keywords of the question and designs a modality-conditioned clue collection module to highlight closely associated audio segments or visual objects. For model optimization, we propose an object-aware adaptive-positivity learning strategy that selects the highly semantic-matched multi-modal pair as \textit{positivity}. Specifically, we design two object-aware contrastive loss functions to identify the highly relevant question-object pairs and audio-object pairs, respectively. These selected pairs are constrained to have larger similarity values than the mismatched pairs. The positivity-selecting process is adaptive as the positivity pairs selected in each video frame may be different. These two object-aware objectives help the model understand \textit{which objects are exactly relevant to the question} and \textit{which are making sounds}. Extensive experiments on the MUSIC-AVQA dataset demonstrate the proposed method is effective in finding favorable audio-visual clues and also achieves new state-of-the-art question-answering performance. The code is available at https://github.com/zhangbin-ai/APL.

Sequential Fusion Based Multi-Granularity Consistency for Space-Time Transformer Tracking

Regarded as a template-matching task for a long time, visual object tracking has witnessed significant progress in space-wise exploration. However, since tracking is performed on videos with substantial time-wise information, it is important to simultaneously mine the temporal contexts which have not yet been deeply explored. Previous supervised works mostly consider template reform as the breakthrough point, but they are often limited by additional computational burdens or the quality of chosen templates. To address this issue, we propose a Space-Time Consistent Transformer Tracker (STCFormer), which uses a sequential fusion framework with multi-granularity consistency constraints to learn spatiotemporal context information. We design a sequential fusion framework that recombines template and search images based on tracking results from chronological frames, fusing updated tracking states in training. To further overcome the over-reliance on the fixed template without increasing computational complexity, we design three space-time consistent constraints: Label Consistency Loss (LCL) for label-level consistency, Attention Consistency Loss (ACL) for patch-level ROI consistency, and Semantic Consistency Loss (SCL) for feature-level semantic consistency. Specifically, in ACL and SCL, the label information is used to constrain the attention and feature consistency of the target and the background, respectively, to avoid mutual interference. Extensive experiments have shown that our STCFormer outperforms many of the best-performing trackers on several popular benchmarks.

Augmented Commonsense Knowledge for Remote Object Grounding

The vision-and-language navigation (VLN) task necessitates an agent to perceive the surroundings, follow natural language instructions, and act in photo-realistic unseen environments. Most of the existing methods employ the entire image or object features to represent navigable viewpoints. However, these representations are insufficient for proper action prediction, especially for the REVERIE task, which uses concise high-level instructions, such as “Bring me the blue cushion in the master bedroom”. To address enhancing representation, we propose an augmented commonsense knowledge model (ACK) to leverage commonsense information as a spatio-temporal knowledge graph for improving agent navigation. Specifically, the proposed approach involves constructing a knowledge base by retrieving commonsense information from ConceptNet, followed by a refinement module to remove noisy and irrelevant knowledge. We further present ACK which consists of knowledge graph-aware cross-modal and concept aggregation modules to enhance visual representation and visual-textual data alignment by integrating visible objects, commonsense knowledge, and concept history, which includes object and knowledge temporal information. Moreover, we add a new pipeline for the commonsense-based decision-making process which leads to more accurate local action prediction. Experimental results demonstrate our proposed model noticeably outperforms the baseline and archives the state-of-the-art on the REVERIE benchmark. The source code is available at https://github.com/Bahram-Mohammadi/ACK.

Moving and Static Faces, Bodies, Objects, and Scenes Are Differentially Represented across the Three Visual Pathways.

Models of human cortex propose the existence of neuroanatomical pathways specialized for different behavioral functions. These pathways include a ventral pathway for object recognition, a dorsal pathway for performing visually guided physical actions, and a recently proposed third pathway for social perception. In the current study, we tested the hypothesis that different categories of moving stimuli are differentially processed across the dorsal and third pathways according to their behavioral implications. Human participants (n = 30) were scanned with fMRI while viewing moving and static stimuli from four categories (faces, bodies, scenes, and objects). A whole-brain group analysis showed that moving bodies and moving objects increased neural responses in the bilateral posterior parietal cortex, parts of the dorsal pathway. By contrast, moving faces and moving bodies increased neural responses, the superior temporal sulcus, part of the third pathway. This pattern of results was also supported by a separate ROI analysis showing that moving stimuli produced more robust neural responses for all visual object categories, particularly in lateral and dorsal brain areas. Our results suggest that dynamic naturalistic stimuli from different categories are routed in specific visual pathways that process dissociable behavioral functions.

Three Prenatal Developments in the Retina Allow for Cortico-Retinal Image Processing in Situ in the Eye

Image processing in the retina of the eye has thus far been mainly dealt with analogous to photographic ray optics i. e., imaging lens optics and photodiode arrays. However, it does not offer an answer to the questions that are crucial to human vision: WHAT a visible object invariantly represents conceptually (a house, a tree, etc.), WHERE it is located in relation to other objects in space or which RGB-colors and/or luminosities collaborate locally. For this purpose, ray optics needs to be supplemented by diffractive wave optics, which can be described as Fresnel near-field interference in cellular or spatial gratings. The fact that interference optics plays a decisive role in vision has already been proven by the fact that in binocular vision the image brightness is preserved when closing one eye. However, with the introduction of interference-wave-optics and especially with Fresnel Nearfield interference optics the cortico-retinal image processing now becomes possible in the eye, i.e. in the retina of the peripheral visual organ. Fresnel Nearfield interference optics especially allows multilayer proceeding and a better understanding of hierarchical imaging systems. It clearly becomes apparent in the di- and trichromatic proceeding and by the separation of color proceeding from invariant object form proceeding. Color - as an example - is not produced at the visual objects and also not in the cortex, but in the Fresnel space of the retina.

Efficient object tracking on edge devices with MobileTrack

Object tracking has made significant progress in recent years. However, the state-of-the-art trackers are becoming increasingly heavy and expensive, making their deployment challenging in resource-constrained applications. In this study, we introduce MobileTrack, a visual object tracker that strikes a perfect balance between tracking accuracy and inference speed. Utilizing a novel coordinated perception-aware fusion module and a lightweight prediction head, our proposed methodology outperforms most Siamese trackers on various academic benchmarks in terms of both accuracy and efficiency. When deployed on resource-constrained embedded devices such as NVIDIA Jetson TX2, MobileTrack ensures real-time performance at a speed exceeding 33FPS, while LightTrack only operates at 18FPS. Therefore, MobileTrack holds significant potential to unlock a wide range of practical applications across various industries. MobileTrack is released at here.