Konstantinos Bacharidis

Manuel Benavent-Lledo, Konstantinos Bacharidis, Konstantinos Papoutsakis et al.

Human action anticipation is commonly treated as a video understanding problem, implicitly assuming that dense temporal information is required to reason about future actions. In this work, we challenge this assumption by investigating what can be achieved when action anticipation is constrained to a single visual observation. We ask a fundamental question: how much information about the future is already encoded in a single frame, and how can it be effectively exploited? Building on our prior work on Action Anticipation at a Glimpse (AAG), we conduct a systematic investigation of single-frame action anticipation enriched with complementary sources of information. We analyze the contribution of RGB appearance, depth-based geometric cues, and semantic representations of past actions, and investigate how different multimodal fusion strategies, keyframe selection policies and past-action history sources influence anticipation performance. Guided by these findings, we consolidate the most effective design choices into AAG+, a refined single-frame anticipation framework. Despite operating on a single frame, AAG+ consistently improves upon the original AAG and achieves performance comparable to, or exceeding, that of state-of-the-art video-based methods on challenging anticipation benchmarks including IKEA-ASM, Meccano and Assembly101. Our results offer new insights into the limits and potential of single-frame action anticipation, and clarify when dense temporal modeling is necessary and when a carefully selected glimpse is sufficient.

Manuel Benavent-Lledo, Konstantinos Bacharidis, Victoria Manousaki et al.

Anticipating actions before they occur is a core challenge in action understanding research. While conventional methods rely on extracting and aggregating temporal information from videos, as humans we can often predict upcoming actions by observing a single moment from a scene, when given sufficient context. Can a model achieve this competence? The short answer is yes, although its effectiveness depends on the complexity of the task. In this work, we investigate to what extent video aggregation can be replaced with alternative modalities. To this end, based on recent advances in visual feature extraction and language-based reasoning, we introduce AAG, a method for Action Anticipation at a Glimpse. AAG combines RGB features with depth cues from a single frame for enhanced spatial reasoning, and incorporates prior action information to provide long-term context. This context is obtained either through textual summaries from Vision-Language Models, or from predictions generated by a single-frame action recognizer. Our results demonstrate that multimodal single-frame action anticipation using AAG can perform competitively compared to both temporally aggregated video baselines and state-of-the-art methods across three instructional activity datasets: IKEA-ASM, Meccano, and Assembly101.

Georgios Simantiris, Konstantinos Bacharidis, Apostolos Papanikolaou et al.

Accurate flood detection from visual data is a critical step toward improving disaster response and risk assessment, yet datasets for flood segmentation remain scarce due to the challenges of collecting and annotating large-scale imagery. Existing resources are often limited in geographic scope and annotation detail, hindering the development of robust, generalized computer vision methods. To bridge this gap, we introduce AIFloodSense, a comprehensive, publicly available aerial imagery dataset comprising 470 high-resolution images from 230 distinct flood events across 64 countries and six continents. Unlike prior benchmarks, AIFloodSense ensures global diversity and temporal relevance (2022-2024), supporting three complementary tasks: (i) Image Classification with novel sub-tasks for environment type, camera angle, and continent recognition; (ii) Semantic Segmentation providing precise pixel-level masks for flood, sky, and buildings; and (iii) Visual Question Answering (VQA) to enable natural language reasoning for disaster assessment. We establish baseline benchmarks for all tasks using state-of-the-art architectures, demonstrating the dataset's complexity and its value in advancing domain-generalized AI tools for climate resilience.

Konstantinos Bacharidis, Antonis A. Argyros

Mistake analysis in procedural activities is a critical area of research with applications spanning industrial automation, physical rehabilitation, education and human-robot collaboration. This paper reviews vision-based methods for detecting and predicting mistakes in structured tasks, focusing on procedural and executional errors. By leveraging advancements in computer vision, including action recognition, anticipation and activity understanding, vision-based systems can identify deviations in task execution, such as incorrect sequencing, use of improper techniques, or timing errors. We explore the challenges posed by intra-class variability, viewpoint differences and compositional activity structures, which complicate mistake detection. Additionally, we provide a comprehensive overview of existing datasets, evaluation metrics and state-of-the-art methods, categorizing approaches based on their use of procedural structure, supervision levels and learning strategies. Open challenges, such as distinguishing permissible variations from true mistakes and modeling error propagation are discussed alongside future directions, including neuro-symbolic reasoning and counterfactual state modeling. This work aims to establish a unified perspective on vision-based mistake analysis in procedural activities, highlighting its potential to enhance safety, efficiency and task performance across diverse domains.

Victoria Manousaki, Konstantinos Bacharidis, Filippos Gouidis et al.

In this work, we introduce (a) the new problem of anticipating object state changes in images and videos during procedural activities, (b) new curated annotation data for object state change classification based on the Ego4D dataset, and (c) the first method for addressing this challenging problem. Solutions to this new task have important implications in vision-based scene understanding, automated monitoring systems, and action planning. The proposed novel framework predicts object state changes that will occur in the near future due to yet unseen human actions by integrating learned visual features that represent recent visual information with natural language (NLP) features that represent past object state changes and actions. Leveraging the extensive and challenging Ego4D dataset which provides a large-scale collection of first-person perspective videos across numerous interaction scenarios, we introduce an extension noted Ego4D-OSCA that provides new curated annotation data for the object state change anticipation task (OSCA). An extensive experimental evaluation is presented demonstrating the proposed method's efficacy in predicting object state changes in dynamic scenarios. The performance of the proposed approach also underscores the potential of integrating video and linguistic cues to enhance the predictive performance of video understanding systems and lays the groundwork for future research on the new task of object state change anticipation. The source code and the new annotation data (Ego4D-OSCA) will be made publicly available.