paper-with-me

Papers

Audio-Visual-Olfactory Resource Allocation for Tri-modal Virtual Environments

2020-02-07 · Efstratios Doukakis, Kurt Debattista, Thomas Bashford-Rogers, Amar Dhokia, Ali Asadipour, Alan Chalmers, Carlo Harvey

Virtual Environments (VEs) provide the opportunity to simulate a wide range of applications, from training to entertainment, in a safe and controlled manner. For applications which require realistic representations of real world environments, the VEs need to provide multiple, physically accurate sensory stimuli. However, simulating all the senses that comprise the human sensory system (HSS) is a task that requires significant computational resources. Since it is intractable to deliver all senses at the highest quality, we propose a resource distribution scheme in order to achieve an optimal perceptual experience within the given computational budgets. This paper investigates resource balancing for multi-modal scenarios composed of aural, visual and olfactory stimuli. Three experimental studies were conducted. The first experiment identified perceptual boundaries for olfactory computation. In the second experiment, participants (N=25) were asked, across a fixed number of budgets (M=5), to identify what they perceived to be the best visual, acoustic and olfactory stimulus quality for a given computational budget. Results demonstrate that participants tend to prioritise visual quality compared to other sensory stimuli. However, as the budget size is increased, users prefer a balanced distribution of resources with an increased preference for having smell impulses in the VE. Based on the collected data, a quality prediction model is proposed and its accuracy is validated against previously unused budgets and an untested scenario in a third and final experiment.

📄 PDF Abstract BibTeX arXiv:2002.02671

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

See & Sniff: Learning Visuo-Olfactory Representations

2026-06-25 · Seongyu Kim, Seungwoo Lee, Hyeonggon Ryu, Joon Son Chung 외 arxiv

While modern multimodal models integrate vision with language, audio, or touch, olfaction remains largely unexplored due to the lack of paired visuo-olfactory data. We introduce SmellNet-V, a scalable visuo-olfactory dat…

Cross-Modal Retrieval

New York Smells: A Large Multimodal Dataset for Olfaction

2025-11-25 · Ege Ozguroglu, Junbang Liang, Ruoshi Liu, Mia Chiquier 외 arxiv

While olfaction is central to how animals perceive the world, this rich chemical sensory modality remains largely inaccessible to machines. One key bottleneck is the lack of diverse, multimodal olfactory training data co…

Representation LearningImage Retrieval

What Images Cannot Say: Language-Guided Olfactory Representation Learning

2026-07-07 · Eleftherios Tsonis, Xi Wang, Vicky Kalogeiton arxiv

Images tell us what a scene looks like, but rarely what it would feel like to be there. While recent datasets pair visual scenes with electronic-nose measurements, aligning smell signals with images remains challenging b…

Representation LearningText Retrieval

MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech Tokens

2025-03-14 · Jeong Hun Yeo, Hyeongseop Rha, Se Jin Park, Yong Man Ro

Audio-Visual Speech Recognition (AVSR) achieves robust speech recognition in noisy environments by combining auditory and visual information. However, recent Large Language Model (LLM) based AVSR systems incur high compu…

Audio-Visual Speech RecognitionComputational EfficiencyLanguage ModelingLanguage Modelling+5

Human-Machine Cooperative Multimodal Learning Method for Cross-subject Olfactory Preference Recognition

2023-11-24 · Xiuxin Xia, Yuchen Guo, Yanwei Wang, Yuchao Yang 외

Odor sensory evaluation has a broad application in food, clothing, cosmetics, and other fields. Traditional artificial sensory evaluation has poor repeatability, and the machine olfaction represented by the electronic no…

EEGElectroencephalogram (EEG)