Scene Context Research Articles

"The Butcher on the Bus" is Less Familiar to Older Adults than She is to Younger Adults.

The "butcher on the bus" is a term describing recognizing someone as familiar but failing to recollect how we know them. Previous studies probing this phenomenon have not used paradigms that mimic real life, and age-related differences have not been adequately addressed. In two studies, younger and older adults studied faces in scene contexts a variable number of times. In the test phases, studied faces were presented identical to the study phase, new faces were presented with new scenes, or studied faces were presented in a different context. In Study 1, this different context was the face presented with new features (pose, hairstyle, and clothing) and in a new scene. In Study 2, this different context was the same scene, but with new features. Participants made recollect, familiar, or new judgments, as well as source memory judgments for each face. In both studies, younger adults had better recognition discrimination and recollection. The critical finding of both studies was that when faces were presented with new features, in either the same scene or a different scene, older adults did not find these faces as familiar as did younger adults. Age-related declines in face recognition when features change may impact social interactions and everyday recognition tasks, highlighting the need for further research and supportive strategies to assist older adults in recognizing people.

Smart Caption: Intelligent Image Description Using Transformer Decoder

The need for automated image description systems has grown significantly with the rise of multimedia content across diverse domains such as social media, digital libraries, and accessibility technologies. Smart Caption presents a novel approach for generating intelligent and context-aware image descriptions by utilizing a combination of Vision Transformers (ViTs) and Natural Language Processing (NLP) Transformer decoders. Unlike traditional convolution-based methods, Vision Transformers treat images as sequences of patches, enabling them to capture global image features more effectively. These extracted visual features are then processed by a Transformer-based decoder to produce coherent and contextually appropriate captions, bridging the gap between image recognition and natural language generation. Our approach focuses on leveraging the attention mechanisms inherent in both Vision and NLP Transformers to enhance the quality of image descriptions. The ViT architecture is designed to focus on relevant regions within an image, while the NLP Transformer decoder is adept at generating fluent and detailed descriptions by attending to the most significant visual features. This two-stage process improves the precision of object detection, relationship understanding, and scene context in generated captions. Experimental results demonstrate that Smart Caption out performs traditional methods in terms of accuracy, contextual relevance, and fluency, marking a significant step forward in the field of automated image captioning. Through this research, we aim to provide a scalable, efficient, and intelligent solution for image description, with potential applications in areas such as accessibility tools for visually impaired users, digital content indexing, and automated content generation. Our findings highlight the strengths of transformer-based models in bridging vision and language tasks, offering promising directions for future research in multimodal AI..

Exploring challenges in audiovisual translation: A comparative analysis of human- and AI-generated Arabic subtitles in Birdman.

Movies often use allusions to add depth, create connections, and enrich the storytelling. However, translators may face challenges when subtitling movie allusions, as they must render both meaning and culture accurately despite existing language and cultural barriers. These challenges could be further complicated by the use of available AI tools attempting to subtitle movie allusions, while probably unaware of existing cultural complexities. This research investigates these challenges using qualitative and descriptive quantitative approaches by analyzing the movie Birdman or (The Unexpected Virtue of Ignorance), comprising13.014 words, to identify the types of allusions used and compare the human- vs. AI (ChatGPT)-generated Arabic subtitles in terms of the subtitling strategies, their frequency, and quality. The results revealed that the movie used 52 Noun Phrase (NP) allusions, where the writer intertextually employed a proper name to convey meaning, and 8 Key-Phrase (KP) allusions, where the writer used phrases that convey implicit meaning easily perceived by members of the source culture (by referring to religious, literary, or entertainment texts). For NP allusions, both the human translator and AI opted for retentive strategies; however, the human translator's preference to add guidance/parentheses to mark NP allusions was distinct. Additionally, it was observed that AI used neologism to render technology-related allusions, which could be a suggested strategy for NP subtitling into Arabic. For KP allusions, while the human translator seemed to be cognizant of the idea that KP allusions typically require a change in wording, AI fell short. Specifically, the human translator employed reduction in 5 out of 8 KPs, opting for minimum change/literal translation only three times. Conversely, AI utilized literal translation in all 8 examples, despite its awareness of the allusion and its intricate meaning/reference. As for the FAR assessment, for NP allusions, it revealed minor semantic errors in AI's subtitles that did not affect the plot. Regarding KP allusions, AI's subtitles were penalized in 5 out of its 8 Arabic renditions, in contrast to the human translator. Most of the errors were serious semantic errors that likely disrupted the flow of reading the subtitles due to conveying irrelevant meanings in the movie's/scene's context. Despite its functionality, this study suggests adding an extra parameter to the FAR model: consistency, as it plays a role in enhancing audience involvement and understanding. Its absence, as observed in some AI instances, can be misleading.

TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching.

This study tackles image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these approaches have high computational costs and may not capture sufficient high-level contextual information, such as spatial structures or semantic shapes. To overcome these limitations, we propose a novel image-matching method that leverages a topic-modeling strategy to capture high-level contexts in images. Our method represents each image as a multinomial distribution over topics, where each topic represents semantic structures. By incorporating these topics, we can effectively capture comprehensive context information and obtain discriminative and high-quality features. Notably, our coarse-level matching network enhances efficiency by employing attention layers only to fixed-sized topics and small-sized features. Finally, we design a dynamic feature refinement network for precise results at a finer matching stage. Through extensive experiments, we have demonstrated the superiority of our method in challenging scenarios. Specifically, our method ranks in the top 9% in the Image Matching Challenge 2023 without using ensemble techniques. Additionally, we achieve an approximately 50% reduction in computational costs compared to other Transformer-based methods. Code is available at https://github.com/TruongKhang/TopicFM.

Attentional guidance through object associations in visual cortex

Efficient behavior requires the rapid attentional selection of task-relevant objects. Preparatory activity of target-selective neurons in visual cortex is thought to support attentional selection, guiding spatial attention and favoring processing of target-matching input. However, naturalistic searches are often guided by nontargets, including target-associated “anchor” objects. For instance, when looking for a pen, we may direct our attention to the office desk on which we expect to find it. Here, using fMRI and eye tracking in a context-guided search task, we tested whether preparatory activity in visual cortex reflected the target, the guiding anchor object, or both. Participants learned associations between targets and anchors, reversing across two scene contexts, before searching for these targets. Participants’ first fixations were reliably guided by the associated anchor. Preparatory activity in lateral occipital cortex (LOC) and right intraparietal sulcus (IPS) represented the target-associated anchor rather than the target. These results shed light on the neural basis of context-guided search in structured environments.

Classification and extraction method of hidden dangers along railway lines based on semantic segmentation network

ABSTRACT Foreign objects invading high-speed railway lines can cause danger. One existing solution is to use remote sensing images to analyse the dangerous areas along the railway line, thereby providing a certain amount of investigation time. Considering the spatial and temporal resolution characteristics of existing remote sensing technologies in identifying floating objects and the reality of rapid land use changes, this paper identifies areas on the ground where floating objects may be generated by using semantic segmentation techniques oriented to remotely sensed imagery and provides early warnings to staff along the route. However, these regions that need to be analysed have different semantics and scales. To address these challenges, this paper proposes a Dual-branch Parallel Fusion Network (DPFNet) based on Transformer, aimed at enhancing multi-class semantic segmentation in remote sensing images. To leverage global contextual information, we introduce a Swin Transformer-based backbone network, which employs self-attention to capture a comprehensive scene context, facilitating better segmentation by considering the entire scene’s context. For multi-scale semantic features, we propose one approach that involves independent branching feature expression and a Multi-scale Feature Space Fusion Module (MFSFM). The former enriches multi-scale information, while the latter fuses features across different levels to capture diverse semantic features. Experimental results demonstrate that DPFNet can effectively identify the hidden danger area, and the fusion of multi-scale features makes the network more accurately identify and segment the risk area of different sizes, improving the segmentation accuracy and robustness, and is of great significance to the formation of the ‘prevention’ as the core of the railway safety operation.

Multimodal Hong Kong: a multisensory study of environmental perception of places through sight, sound, and smell

Despite the importance of soundscape and smellscape in intangible cultural heritage, little is known about multisensory interactions. To explore the impact of multiple senses on environmental perception, the present study focused on six stalls at Shek Kip Mei wet market in Hong Kong. In the first part of the study, an online survey collected perceptual ratings of whole, auditory, and visual environmental quality, as well as free-form responses of imaginations of smell. Participants (N = 36) were presented with 12 audiovisual captures where audio was either matched with video or mismatched. ANOVA was used to examine the influence of three independent factors - scenes, modes, and a priori valence groups - on the ratings. The imagined smells were analysed through frequency counts of keywords. The results demonstrate participants' capacity to notice changes in audio modes and to perceive the environment through the recognition of scene context and multisensory interactions. In the second part of the study, not discussed in the present paper, we will extend this experimental method to include smells in a laboratory setting. We discuss the importance of understanding multisensory interactions between sight, sound, and smell for environmental perceptions and Hong Kong's intangible cultural heritage and urban studies.

Pose-oriented scene-adaptive matching for abnormal event detection

For intelligent surveillance systems, abnormal event detection automatically analyses surveillance video sequences and detects abnormal objects or unusual human actions at the frame level. Due to the lack of labelled data, most approaches are semi-supervised based on reconstruction or prediction methods. However, these methods may not generalize well to unseen scene contexts. To address this issue, we present a novel self and mutual scene-adaptive matching method for abnormal event detection. In the framework, we propose synergistic pose estimation and object detection, which effectively integrates human pose and object detection information to improve pose estimation accuracy. Then, the poses are resized to reduce the spatial distance between the source and target domains. The improved pose sequences are further fed into a spatio-temporal graph convolutional network to extract the geometric features. Finally, the features are embedded in a clustering layer to classify action types and compute normality scores. The training data is taken from the training part of common video anomaly detection datasets: UCSD PED1 & PED2, CHUK Avenue, and ShanghaiTech Campus. The proposed framework is evaluated on video sequences with unseen scene contexts in the UCSD PED2 and ShanghaiTech Campus datasets. The detection accuracy and efficiency are also evaluated in detail, and the proposed method for abnormal event detection achieves the highest AUC performance, 84.6%, on the ShanghaiTech Campus dataset and relatively high AUC performance, 96.9% and 74.8%, on UCSD PED2 & PED1 datasets. Compared with other state-of-the-art works, the performance analysis and results confirm the robustness and effectiveness of our proposed framework for cross-scene abnormal event detection.

Visual Communication Method of Multi frame Film and Television Special Effects Images Based on Deep Learning

For the dynamic film and television special effects industry, creating visually stunning and valuable graphics requires the integration of excellent deep-learning algorithms. This paper presents an enhanced version of the 3-D Convolutional Neural Network (3-D CNN), specifically tailored to meet the demanding needs of multi-frame television and film special effects. The version’s key feature is its efficient data handling through coupled precision training, significantly increasing computational efficiency while reducing memory needs. This method, which combines floating-issue operations of 16 and 32 bits, is ideal for efficiently processing large amounts of high-resolution video data. The proposed 3-D CNN structure excels in extracting and analysing complex spatiotemporal capabilities from video sequences, capturing the spatial and temporal nuances crucial in film imagery. This capability is important for accomplishing excessive fidelity in visual results, ensuring seamless integration with live motion pics. Incorporating a cutting-edge attention mechanism inspired by the aid of transformer-based architectures, the model specialises inside the maximum pertinent components of video frames to enhance the quality and realism of outcomes. Furthermore, the model boasts an excessive-resolution processing characteristic, permitting simultaneous capture of first-rate records and broader scene context. This guarantees consistency and realism in outcomes, from complicated textures to the overarching visual narrative. Advanced regularisation strategies are hired to prevent overfitting, permitting the model to generalise efficiently through numerous film manufacturing conditions. The state-of-the-art 3-D information augmentation techniques make the model robust and prepare it to deal with an intensive variety of challenging situations involving computer special effects. Real-time processing competencies make the model a game-changer for on-set visible outcomes and adjustments. Designed for seamless integration with the famous CGI software program software utility, it allows a harmonious aggregate of AI and ingenious creativity. Acknowledging the environmental impact of high-powered computing, the model consists of power-efficient computational techniques that align with sustainable computing practices, lower operational costs, and are related to processing complex video statistics. model boasts an excessive-resolution processing characteristic, permitting simultaneous capture of first-rate records and broader scene context. This guarantees consistency and realism in outcomes, from complicated textures to the overarching visual narrative. Advanced regularisation strategies are hired to prevent overfitting, permitting the model to generalize efficiently through numerous film manufacturing conditions. The state-of-the-art 3-D information augmentation techniques, in addition, make the model robust, and prepare it to deal with an intensive variety of computer special effects challenging situations.

Context and semantic object properties interact to support recognition memory.

We have a great capacity to remember a large number of items, yet memory is selective. While multiple factors dictate why we remember some things and not others, it is increasingly acknowledged that some objects are more memorable than others. Recent studies show semantically distinctive objects are better remembered, as are objects located in expected scene contexts. However, we know little about how object semantics and context interact to facilitate memory. Here we test the intriguing hypothesis that these factors have complementary benefits for memory. Participants rated the congruency of object-scene pairs, followed by a surprise memory test. We show that object memory is best predicted by semantic familiarity when an object-scene pairing was congruent, but when object-scene pairings were incongruent, semantic statistics have an especially prominent impact. This demonstrates both the item and its schematic relationship to the environment interact to shape what we will and will not remember.

GRNet: a graph reasoning network for enhanced multi-modal learning in scene text recognition

Abstract Recent advancements in scene text recognition have predominantly focused on leveraging textual semantics. However, an over-reliance on linguistic priors can impede a model’s ability to handle irregular text scenes, including non-standard word usage, occlusions, severe distortions, or stretching. The key challenges lie in effectively localizing occlusions, perceiving multi-scale text, and inferring text based on scene context. To address these challenges and enhance visual capabilities, we introduce the Graph Reasoning Model (GRM). The GRM employs a novel feature fusion method to align spatial context information across different scales, beginning with a feature aggregation stage that extracts rich spatial contextual information from various feature maps. Visual reasoning representations are then obtained through graph convolution. We integrate the GRM module with a language model to form a two-stream architecture called GRNet. This architecture combines pure visual predictions with joint visual-linguistic predictions to produce the final recognition results. Additionally, we propose a dynamic iteration refinement for the language model to prevent over-correction of prediction results, ensuring a balanced contribution from both visual and linguistic cues. Extensive experiments demonstrate that GRNet achieves state-of-the-art average recognition accuracy across six mainstream benchmarks. These results highlight the efficacy of our multi-modal approach in scene text recognition, particularly in challenging scenarios where visual reasoning plays a crucial role.

Exploring the synergy between textual identity and visual signals in human-object interaction

Human-Object Interaction (HOI) detection task aims to recognize and understand interactions between humans and objects depicted in images. Unlike instance recognition tasks, which focus on isolated objects, HOI detection requires considering various explanatory factors, such as instance identity, spatial relationships, and scene context. However, previous HOI detection methods have primarily relied on local visual cues, often overlooking the vital role of instance identity and thus limiting the performance of models. In this paper, we introduce textual features to expand the definition of HOI representations, incorporating instance identity into the HOI reasoning process. Drawing inspiration from the human activity perception process, we explore the synergy between textual identity and visual signals to leverage various explanatory factors more effectively and enhance HOI detection performance. Specifically, our method extracts HOI explanatory factors using both modal representations. Visual features capture interactive cues, while textual features explicitly denote instance identities within human-object pairs, delineating relevant interaction categories. Additionally, we utilize Contrastive Language-Image Pre-training (CLIP) to enhance the semantic alignment between visual and textual features and design a cross-modal learning module for integrating HOI multimodal information. Extensive experiments on several benchmarks demonstrate that our proposed framework surpasses most existing methods, achieving outstanding performance with a mean average precision (mAP) of 33.95 on the HICO-DET dataset and 63.2 mAP on the V-COCO dataset.

HyperSOR: Context-Aware Graph Hypernetwork for Salient Object Ranking.

Salient object ranking (SOR) aims to segment salient objects in an image and simultaneously predict their saliency rankings, according to the shifted human attention over different objects. The existing SOR approaches mainly focus on object-based attention, e.g., the semantic and appearance of object. However, we find that the scene context plays a vital role in SOR, in which the saliency ranking of the same object varies a lot at different scenes. In this paper, we thus make the first attempt towards explicitly learning scene context for SOR. Specifically, we establish a large-scale SOR dataset of 24,373 images with rich context annotations, i.e., scene graphs, segmentation, and saliency rankings. Inspired by the data analysis on our dataset, we propose a novel graph hypernetwork, named HyperSOR, for context-aware SOR. In HyperSOR, an initial graph module is developed to segment objects and construct an initial graph by considering both geometry and semantic information. Then, a scene graph generation module with multi-path graph attention mechanism is designed to learn semantic relationships among objects based on the initial graph. Finally, a saliency ranking prediction module dynamically adopts the learned scene context through a novel graph hypernetwork, for inferring the saliency rankings. Experimental results show that our HyperSOR can significantly improve the performance of SOR.

Depth Matters: Spatial Proximity-based Gaze Cone Generation for Gaze Following in Wild

Gaze following aims to predict where a person is looking in a scene. Existing methods tend to prioritize traditional 2D RGB visual cues or require burdensome prior knowledge and extra expensive datasets annotated in 3D coordinate systems to train specialized modules to enhance scene modeling. In this work, we introduce a novel framework deployed on a simple ResNet backbone, which exclusively uses image and depth maps to mimic human visual preferences and realize 3D-like depth perception. We first leverage depth maps to formulate spatial-based proximity information regarding the objects with the target person. This process sharpens the focus of the gaze cone on the specific region of interest pertaining to the target while diminishing the impact of surrounding distractions. To capture the diverse dependence of scene context on the saliency gaze cone, we then introduce a learnable grid-level regularized attention that anticipates coarse-grained regions of interest, thereby refining the mapping of the saliency feature to pixel-level heatmaps. This allows our model to better account for individual differences when predicting others’ gaze locations. Finally, we employ the KL-divergence loss to super the grid-level regularized attention, which combines the gaze direction, heatmap regression, and in/out classification losses, providing comprehensive supervision for model optimization. Experimental results on two publicly available datasets demonstrate the comparable performance of our model with less help of modal information. Quantitative visualization results further validate the interpretability of our method. The source code will be available at https://github.com/VUT-HFUT/DepthMatters .

Enhancing Place Emotion Analysis with Multi-View Emotion Recognition from Geo-Tagged Photos: A Global Tourist Attraction Perspective

User−generated geo−tagged photos (UGPs) have emerged as a valuable tool for analyzing large−scale tourist place emotions with unprecedented detail. This process involves extracting and analyzing human emotions associated with specific locations. However, previous studies have been limited to analyzing individual faces in the UGPs. This approach falls short of representing the contextual scene characteristics, such as environmental elements and overall scene context, which may contain implicit emotional knowledge. To address this issue, we propose an innovative computational framework for global tourist place emotion analysis leveraging UGPs. Specifically, we first introduce a Multi−view Graph Fusion Network (M−GFN) to effectively recognize multi−view emotions from UGPs, considering crowd emotions and scene implicit sentiment. After that, we designed an attraction−specific emotion index (AEI) to quantitatively measure place emotions based on the identified multi−view emotions at various tourist attractions with place types. Complementing the AEI, we employ the emotion intensity index (EII) and Pearson correlation coefficient (PCC) to deepen the exploration of the association between attraction types and place emotions. The synergy of AEI, EII, and PCC allows comprehensive attraction−specific place emotion extraction, enhancing the overall quality of tourist place emotion analysis. Extensive experiments demonstrate that our framework enhances existing place emotion analysis methods, and the M−GFN outperforms state−of−the−art emotion recognition methods. Our framework can be adapted for various geo−emotion analysis tasks, like recognizing and regulating workplace emotions, underscoring the intrinsic link between emotions and geographic contexts.

Exploring global context and position-aware representation for group activity recognition

This paper explores the context and position information in the scene for group activity understanding. Firstly, previous group activity recognition methods strive to reason on individual features without considering the information in the scene. Besides correlations among actors, we argue that integrating the scene context simultaneously can afford us more useful and supplementary cues. Therefore, we propose a new network, termed Contextual Transformer Network (CTN), to incorporate global contextual information into individual representations. In addition, the position of individuals also plays a vital role in group activity understanding. Unlike previous methods that explore correlations among individuals semantically, we propose Clustered Position Embedding (CPE) to integrate the spatial structure of actors and produce position-aware representations. Experimental results on two widely used datasets for sports video and social activity (i.e., Volleyball and Collective Activity datasets) show that the proposed method outperforms state-of-the-art approaches. Especially, when using ResNet-18 as the backbone, our method achieves 93.6/93.9% MCA/MPCA on the Volleyball dataset and 95.4/96.3% MCA/MPCA on the Collective Activity dataset.

Face detection in contextual scenes.

Object and scene perception are intertwined. When objects are expected to appear within a particular scene, they are detected and categorised with greater speed and accuracy. This study examined whether such context effects also moderate the perception of social objects such as faces. Female and male faces were embedded in scenes with a stereotypical female or male context. Semantic congruency of these scene contexts influenced the categorisation of faces (Experiment 1). These effects were bi-directional, such that face sex also affected scene categorisation (Experiment 2), suggesting concurrent automatic processing of both levels. In contrast, the more elementary task of face detection was not affected by semantic scene congruency (Experiment 3), even when scenes were previewed prior to face presentation (Experiment 4). This pattern of results indicates that semantic scene context can affect categorisation of faces. However, the earlier perceptual stage of detection appears to be encapsulated from the cognitive processes that give rise to this contextual interference.

Multisensory processing impacts memory for objects and their sources.

Multisensory object processing improves recognition memory for individual objects, but its impact on memory for neighboring visual objects and scene context remains largely unknown. It is therefore unclear how multisensory processing impacts episodic memory for information outside of the object itself. We conducted three experiments to test the prediction that the presence of audiovisual objects at encoding would improve memory for nearby visual objects, and improve memory for the environmental context in which they occurred. In Experiments 1a and 1b, participants viewed audiovisual-visual object pairs or visual-visual object pairs with a control sound during encoding and were subsequently tested on their memory for each object individually. In Experiment 2, objects were paired with semantically congruent or meaningless control sounds and appeared within four different scene environments. Memory for the environment was tested. Results from Experiments 1a and 1b showed that encoding a congruent audiovisual object did not significantly benefit memory for neighboring visual objects, but Experiment 2 showed that encoding a congruent audiovisual object did improve memory for the environments in which those objects were encoded. These findings suggest that multisensory processing can influence memory beyond the objects themselves and that it has a unique role in episodic memory formation. This is particularly important for understanding how memories and associations are formed in real-world situations, in which objects and their surroundings are often multimodal.

The Impact of Scene Context on Visual Object Recognition: Comparing Humans, Monkeys, and Computational Models.

During natural vision, we rarely see objects in isolation but rather embedded in rich and complex contexts. Understanding how the brain recognizes objects in natural scenes by integrating contextual information remains a key challenge. To elucidate neural mechanisms compatible with human visual processing, we need an animal model that behaves similarly to humans, so that inferred neural mechanisms can provide hypotheses relevant to the human brain. Here we assessed whether rhesus macaques could model human context-driven object recognition by quantifying visual object identification abilities across variations in the amount, quality, and congruency of contextual cues. Behavioral metrics revealed strikingly similar context-dependent patterns between humans and monkeys. However, neural responses in the inferior temporal (IT) cortex of monkeys that were never explicitly trained to discriminate objects in context, as well as current artificial neural network models, could only partially explain this cross-species correspondence. The shared behavioral variance unexplained by context-naive neural data or computational models highlights fundamental knowledge gaps. Our findings demonstrate an intriguing alignment of human and monkey visual object processing that defies full explanation by either brain activity in a key visual region or state-of-the-art models.

A framework for trust-related knowledge transfer in human–robot interaction

Trustworthy human–robot interaction (HRI) during activities of daily living (ADL) presents an interesting and challenging domain for assistive robots, particularly since methods for estimating the trust level of a human participant towards the assistive robot are still in their infancy. Trust is a multifaced concept which is affected by the interactions between the robot and the human, and depends, among other factors, on the history of the robot’s functionality, the task and the environmental state. In this paper, we are concerned with the challenge of trust transfer, i.e. whether experiences from interactions on a previous collaborative task can be taken into consideration in the trust level inference for a new collaborative task. This has the potential of avoiding re-computing trust levels from scratch for every new situation. The key challenge here is to automatically evaluate the similarity between the original and the novel situation, then adapt the robot’s behaviour to the novel situation using previous experience with various objects and tasks. To achieve this, we measure the semantic similarity between concepts in knowledge graphs (KGs) and adapt the robot’s actions towards a specific user based on personalised interaction histories. These actions are grounded and then verified before execution using a geometric motion planner to generate feasible trajectories in novel situations. This framework has been experimentally tested in human–robot handover tasks in different kitchen scene contexts. We conclude that trust-related knowledge positively influences and improves collaboration in both performance and time aspects.