arXiv subjects

Sakib Reza

Publications and source records attributed to Sakib Reza.

8 recordsLinked to original sources

arXiv2026

DYAD: A Multimodal Dataset of Co-Located Human Assistance

An embodied assistant working beside a person must track task state, recognize help seeking, choose how to intervene, and produce an appropriate response. Existing procedural datasets richly describe individual execution, while interactive datasets capture remote verbal instruction or undifferentiated co-working. They do not jointly link a co-located helper's verbal and physical interventions to performer requests, task state, assistance triggers, and outcomes. We introduce DYAD (DYadic Assistance Dataset), a synchronized multimodal record of human-human assistance during gearbox assembly. Across 20 sessions, one trained helper follows a guidance-first policy while assisting HoloLens 2 wearers. DYAD links 528 task-step intervals and 611 performer requests with 851 valid assistance records spanning verbal and physical help. DYAD's annotations span the assistance process; three reference tasks evaluate selected components rather than an end-to-end system: causal step understanding, pre-onset mode anticipation, and instructor response generation. On 829 eligible mode events, the strongest four-seed RGB mean is 0.548 +/- 0.007 macro-F1; causal metadata reaches 0.624 and a privileged trigger mapping 0.915, revealing information not recovered from pre-onset RGB. DYAD's contribution is not scale, but a linked interaction structure spanning help seeking, intervention choice, execution, and outcome under egocentric and workspace sensing.

cs.RO↗

arXiv2026

OnPoint: Offline-to-Online Multi-Level Distillation for Point-Supervised Online Temporal Action Localization

Temporal Action Localization (TAL) typically relies on segment annotations or offline access to full videos, limiting scalability and online use. We introduce Point-Supervised Online TAL (POTAL), which localizes actions in streaming videos using only one temporal point per instance. To solve POTAL, we propose OnPoint, an offline-to-online multi-level distillation framework that transfers knowledge from a point-supervised offline teacher to an online student via (i) pseudo-segment instance distillation, (ii) class-activation sequence distillation, and (iii) anticipatory window-level distillation. We further improve robustness by incorporating the original point labels into student training and by refining anchor decoding with actionness-guided attention calibration. Experiments on five datasets show OnPoint consistently outperforms strong baselines, establishing a solid foundation for POTAL.

cs.CV↗

arXiv2025

psiUnity: A Platform for Multimodal Data-Driven XR

Extended reality (XR) research increasingly relies on the ability to stream and synchronize multimodal data between headsets and immersive applications for data-driven interaction and experimentation. However, developers face a critical gap: the Platform for Situated Intelligence (psi), which excels at deterministic temporal alignment and multimodal data management, has been largely inaccessible to the dominant Unity/MRTK ecosystem used for HoloLens development. We introduce psiUnity, an open-source C# integration that bridges psi's .NET libraries with Unity 2022.3 and MRTK3 for HoloLens 2. psiUnity enables bidirectional, real-time streaming of head pose, hand tracking, gaze, IMU, audio, and depth sensor data (AHAT and long-throw) with microsecond-level temporal precision, allowing Unity applications to both consume and produce synchronized multimodal data streams. By embedding psi's native serialization, logging, and temporal coordination directly within Unity's architecture, psiUnity extends psi beyond its previous StereoKit limitations and empowers the HRI, HCI, and embodied-AI communities to develop reproducible, data-driven XR interactions and experiments within the familiar Unity environment. The integration is available at https://github.com/sailgt/psiUnity.

cs.HC↗

arXiv2025

REEF: Relevance-Aware and Efficient LLM Adapter for Video Understanding

Integrating vision models into large language models (LLMs) has sparked significant interest in creating vision-language foundation models, especially for video understanding. Recent methods often utilize memory banks to handle untrimmed videos for video-level understanding. However, they typically compress visual memory using similarity-based greedy approaches, which can overlook the contextual importance of individual tokens. To address this, we introduce an efficient LLM adapter designed for video-level understanding of untrimmed videos that prioritizes the contextual relevance of spatio-temporal tokens. Our framework leverages scorer networks to selectively compress the visual memory bank and filter spatial tokens based on relevance, using a differentiable Top-K operator for end-to-end training. Across three key video-level understanding tasks$\unicode{x2013}$ untrimmed video classification, video question answering, and video captioning$\unicode{x2013}$our method achieves competitive or superior results on four large-scale datasets while reducing computational overhead by up to 34%. The code will be available soon on GitHub.

cs.CV↗

arXiv2025

MetaFAP: Meta-Learning for Frequency Agnostic Prediction of Metasurface Properties

Metasurfaces, and in particular reconfigurable intelligent surfaces (RIS), are revolutionizing wireless communications by dynamically controlling electromagnetic waves. Recent wireless communication advancements necessitate broadband and multi-band RIS, capable of supporting dynamic spectrum access and carrier aggregation from sub-6 GHz to mmWave and THz bands. The inherent frequency dependence of meta-atom resonances degrades performance as operating conditions change, making real-time, frequency-agnostic metasurface property prediction crucial for practical deployment. Yet, accurately predicting metasurface behavior across different frequencies remains challenging. Traditional simulations struggle with complexity, while standard deep learning models often overfit or generalize poorly. To address this, we introduce MetaFAP (Meta-Learning for Frequency-Agnostic Prediction), a novel framework built on the meta-learning paradigm for predicting metasurface properties. By training on diverse frequency tasks, MetaFAP learns broadly applicable patterns. This allows it to adapt quickly to new spectral conditions with minimal data, solving key limitations of existing methods. Experimental evaluations demonstrate that MetaFAP reduces prediction errors by an order of magnitude in MSE and MAE while maintaining high Pearson correlations. Remarkably, it achieves inference in less than a millisecond, bypassing the computational bottlenecks of traditional simulations, which take minutes per unit cell and scale poorly with array size. These improvements enable real-time RIS optimization in dynamic environments and support scalable, frequency-agnostic designs. MetaFAP thus bridges the gap between intelligent electromagnetic systems and practical deployment, offering a critical tool for next-generation wireless networks.

eess.SP↗

arXiv2024

HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization

Online video understanding often relies on individual frames, leading to frame-by-frame predictions. Recent advancements such as Online Temporal Action Localization (OnTAL), extend this approach to instance-level predictions. However, existing methods mainly focus on short-term context, neglecting historical information. To address this, we introduce the History-Augmented Anchor Transformer (HAT) Framework for OnTAL. By integrating historical context, our framework enhances the synergy between long-term and short-term information, improving the quality of anchor features crucial for classification and localization. We evaluate our model on both procedural egocentric (PREGO) datasets (EGTEA and EPIC) and standard non-PREGO OnTAL datasets (THUMOS and MUSES). Results show that our model outperforms state-of-the-art approaches significantly on PREGO datasets and achieves comparable or slightly superior performance on non-PREGO datasets, underscoring the importance of leveraging long-term history, especially in procedural and egocentric action scenarios. Code is available at: https://github.com/sakibreza/ECCV24-HAT/

cs.CV↗

arXiv2023

Enhancing Transformer Backbone for Egocentric Video Action Segmentation

Egocentric temporal action segmentation in videos is a crucial task in computer vision with applications in various fields such as mixed reality, human behavior analysis, and robotics. Although recent research has utilized advanced visual-language frameworks, transformers remain the backbone of action segmentation models. Therefore, it is necessary to improve transformers to enhance the robustness of action segmentation models. In this work, we propose two novel ideas to enhance the state-of-the-art transformer for action segmentation. First, we introduce a dual dilated attention mechanism to adaptively capture hierarchical representations in both local-to-global and global-to-local contexts. Second, we incorporate cross-connections between the encoder and decoder blocks to prevent the loss of local context by the decoder. We also utilize state-of-the-art visual-language representation learning techniques to extract richer and more compact features for our transformer. Our proposed approach outperforms other state-of-the-art methods on the Georgia Tech Egocentric Activities (GTEA) and HOI4D Office Tools datasets, and we validate our introduced components with ablation studies. The source code and supplementary materials are publicly available on https://www.sail-nu.com/dxformer.

cs.CV↗

arXiv2023

Augmenting Learning with Augmented Reality: Exploring the Affordances of AR in Supporting Mastery of Complex Psychomotor Tasks

This research seeks to explore how Augmented Reality (AR) can support learning psychomotor tasks that involve complex manipulation and reasoning processes. The AR prototype was created using Unity and used on HoloLens 2 headsets. Here, we explore the potential of AR as a training or assistive tool for spatial tasks and the need for intelligent mechanisms to enable adaptive and personalized interactions between learners and AR. The paper discusses how integrating AR with Artificial Intelligence (AI) can adaptably scaffold the learning of complex tasks to accelerate the development of expertise in psychomotor domains.

cs.HC↗