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Multimodal

The term "multimodal" refers to concepts or systems involving multiple modes or modalities across various fields. In statistics, a multimodal distribution is a probability distribution with two or more modes, representing peaks in the data density. In transportation, multimodal transport involves the use of two or more modes of transportation (e.g., rail and road) to move goods or passengers from origin to destination. In linguistics and communication, multimodality describes the interplay of multiple semiotic modes, such as language, gesture, and visuals, in meaning-making and discourse. In human-computer interaction, multimodal interaction allows users to engage with systems using multiple input methods, like speech, touch, and gesture, for more natural interfaces. In artificial intelligence, multimodal learning processes and integrates information from diverse data types, such as text, images, and audio, to enhance model performance on complex tasks.^[1]^[2]

Statistics

Multimodal Distribution

A multimodal distribution is a probability distribution that exhibits two or more distinct modes, defined as local maxima in its probability density function (for continuous variables) or probability mass function (for discrete variables). This contrasts with a unimodal distribution, which features a single mode, and a bimodal distribution, which has precisely two modes. Such distributions often arise from mixtures of underlying subpopulations, leading to multiple peaks that reflect heterogeneity in the data.^[3]^[4] Mathematically, for a continuous multimodal distribution, modes occur at points x where the first derivative of the probability density function satisfies f'(x) = 0 and the second derivative f''(x) < 0, confirming a local maximum. For a discrete multimodal distribution, a mode is a value x such that the probability mass p(x) > p(x-1) and p(x) > p(x+1), indicating it exceeds the probabilities of its immediate neighbors. These conditions allow identification of multiple local peaks, distinguishing multimodal cases from simpler forms.^[5]^[6] Representative examples illustrate these concepts. The combined height distribution of adult males and females in a population is typically bimodal, with one peak around the average male height (approximately 175 cm) and another around the average female height (approximately 162 cm), due to sexual dimorphism. Income distributions in heterogeneous populations, such as those spanning multiple socioeconomic classes or urban-rural divides, can be multimodal with three or more peaks, each corresponding to distinct income clusters like low-wage workers, middle-class professionals, and high earners.^[7] The notion of multimodal distributions traces back to early statistical work on mixture models, notably Karl Pearson's 1895 analysis of asymmetric variations in homogeneous materials, where he modeled bimodal patterns in crab carapace measurements as mixtures of normal distributions. The term "multimodal" gained formal usage in the 20th century, coinciding with advancements in kernel density estimation, which enabled robust detection of multiple modes in empirical data.^[5]

Properties and Analysis

Detection of multimodal distributions often relies on kernel density estimation (KDE), a non-parametric method that smooths data points to approximate the underlying probability density function using a kernel function, typically Gaussian.^[8] Bandwidth selection is critical in KDE, as it controls the smoothness; Silverman's rule of thumb provides a common heuristic for the optimal bandwidth under the assumption of a Gaussian kernel, given by h = 1.06 \sigma n^{-1/5}, where \sigma is the standard deviation of the sample and n is the sample size. This rule helps identify modes by revealing peaks in the density estimate when the bandwidth is appropriately chosen, though it assumes near-normality and may oversmooth multimodal data.^[8] Another key technique is the dip test for unimodality, proposed by Hartigan and Hartigan, which rejects the null hypothesis of a unimodal distribution by measuring the maximum deviation between the empirical cumulative distribution function (ECDF) F_n(x) and the closest unimodal CDF F(x).^[9] The dip statistic is defined as D_n = \sup |F_n(x) - F(x)|, where the supremum is taken over all possible partitions of the support into unimodal segments.^[9] This nonparametric test is robust to the shape of the underlying distribution and provides a p-value via simulation or asymptotic approximation, making it suitable for detecting multimodality without assuming a specific parametric form.^[9] Multimodal distributions exhibit properties such as increased overall variance compared to unimodal distributions with similar means, arising from the dispersion of probability mass across separated modes rather than concentration around a single peak.^[10] Parameter estimation for multimodal data is often modeled as finite mixtures, but faces challenges like identifiability issues and sensitivity to initialization due to the likelihood's multimodality.^[11] The expectation-maximization (EM) algorithm addresses these by iteratively performing an E-step to compute posterior probabilities of component membership for each observation and an M-step to update mixture parameters (weights, means, variances) by maximizing the expected complete-data log-likelihood.^[12] Assuming unimodality when data is multimodal introduces bias in statistical procedures, such as inflated Type I or II errors in hypothesis testing for means or normality, as standard tests like the t-test fail to account for heterogeneous subpopulations.^[13] Multimodality frequently signals the presence of distinct subpopulations within the data, as illustrated in Galton's 1889 quincunx demonstration, which modeled bivariate normal distributions in heredity to show how correlated traits can reveal clustered inheritance patterns. In applications like clustering, multimodal distributions inform algorithms such as Gaussian mixture models to partition data into natural groups, though analysis remains confined to probabilistic inference rather than causal modeling.^[14]

Transportation

Multimodal Transport

Multimodal transport refers to the carriage of goods using two or more different modes of transportation, such as truck, rail, and ship, under a single contract that covers the entire journey from origin to destination.^[15] This distinguishes it from intermodal transport, where goods are moved across multiple modes but under separate contracts for each segment, with liability shifting between carriers at mode changes.^[16] In multimodal arrangements, a single multimodal transport operator (MTO) coordinates the process and assumes overall responsibility, simplifying logistics for shippers.^[17] The concept emerged in the post-World War II era, driven by the need for more efficient global trade. A pivotal innovation was containerization, pioneered by American entrepreneur Malcom McLean, who in 1956 launched the first container ship voyage from Newark, New Jersey, to Houston, Texas, using 58 standardized containers to reduce loading times and costs.^[18] This facilitated seamless transfers between modes without unpacking cargo, laying the groundwork for multimodal systems. In 1992, international standardization advanced with the development of the UNCTAD/ICC Rules for Multimodal Transport Documents, which provided a framework for uniform documentation and liability in combined transport operations.^[19] Key components of multimodal transport include the through bill of lading, a single document issued by the MTO that serves as the contract of carriage and receipt for the goods across all modes.^[20] The MTO bears liability for loss, damage, or delay throughout the journey, applying a uniform or network system based on the governing rules, which ensures accountability without gaps between modes.^[17] For instance, electronics manufacturers often use multimodal routes from Asia to Europe, combining sea freight from Shanghai to Dubai, air transport to Frankfurt, and rail to final inland destinations, achieving significant cost efficiencies compared to single-mode alternatives.^[21]^[22]

Legal and Operational Frameworks

The UNCTAD/ICC Rules for Multimodal Transport Documents, developed in 1992 by the United Nations Conference on Trade and Development (UNCTAD) and the International Chamber of Commerce (ICC), provide a voluntary framework for standardizing contracts in multimodal transport.^[19] These rules outline the obligations of multimodal transport operators (MTOs), who assume responsibility for goods from the point of receipt until delivery, including liability for loss, damage, or delay unless the MTO proves the absence of fault or neglect.^[19] Liability under these rules is limited to 666.67 special drawing rights (SDR) per package or unit, or 2 SDR per kilogram of gross weight, whichever is higher, with higher limits applying if no sea carriage is involved.^[19] The Rotterdam Rules, adopted by the UN General Assembly in 2008, represent an attempt to modernize international transport law by extending coverage to multimodal shipments that include a sea leg, though the convention has not entered into force due to insufficient ratifications.^[23] Under these rules, carrier liability for loss or damage is capped at 875 SDR per package or shipping unit, or 3 SDR per kilogram of gross weight, whichever is greater, promoting a unified regime across transport modes while allowing for declared higher values.^[23] Operational challenges in multimodal transport often arise during mode handoffs, such as customs delays at ports, which can disrupt schedules and increase costs due to regulatory inspections and documentation mismatches.^[24] To address visibility issues, technology integration like electronic data interchange (EDI) enables real-time tracking and automated data sharing among carriers, reducing errors and handoff delays in intermodal shipments.^[25] Sustainability efforts incorporate metrics for CO2 reduction through mode optimization models, which balance cost, time, and emissions by prioritizing lower-carbon routes like rail over road, potentially cutting emissions by integrating rail and water transport in path planning.^[26] Multimodal transport typically operates under a single contract with one operator coordinating all modes, contrasting with multiple contracts in intermodal arrangements where each carrier handles a separate segment.^[27] Insurance requirements for individual segments may invoke regime-specific rules, such as the Hague-Visby Rules for sea carriage (limiting liability to 666.67 SDR per package or 2 SDR per kg) or the Hamburg Rules for broader application (2.5 SDR per kg or 835 SDR per package), ensuring coverage aligns with the loss stage while the MTO maintains overall responsibility.^[28] A prominent case study is A.P. Moller-Maersk, which has expanded end-to-end multimodal services since the 2010s as part of its integrated logistics strategy, combining ocean, rail, and road transport to streamline global supply chains.^[29] By 2020, Maersk handled approximately 14.6% of global container traffic through its fleet of over 700 vessels and extensive inland networks, demonstrating how unified operations mitigate fragmentation in international trade.^[30]

Linguistics and Communication

Multimodality

Multimodality in linguistics refers to the integration of multiple semiotic modes—such as language, gesture, and image—to construct and convey meaning within communicative acts. This approach, rooted in social semiotics, posits that meaning emerges not from isolated modes but from their orchestrated interplay, where each mode contributes distinct yet complementary resources. Gunther Kress and Theo van Leeuwen introduced this framework in their seminal work, emphasizing how visual and linguistic elements together form a "grammar" for representation in contemporary texts. Key theorists have shaped this perspective. Gunther Kress further developed the concept by describing multimodal texts as "orchestrated ensembles," where sign makers deliberately select and arrange modes to realize social and cultural purposes, drawing on the affordances of each to achieve communicative efficacy. Building on Michael Halliday's systemic functional linguistics, Kress and van Leeuwen extended Halliday's three metafunctions—ideational (representing experience), interpersonal (enacting social relations), and textual (organizing the message)—to visual and other non-linguistic modes, arguing that all semiotic resources fulfill these functions in parallel. Modes in multimodality are classified based on their semiotic potential, including verbal (encompassing speech and writing, which excel in precision and sequential argumentation), visual (incorporating images and gestures, suited for spatial relations and embodiment), and aural (involving sound, effective for rhythm and emotional resonance). Each mode carries unique affordances: for instance, color in visual modes can evoke emotional responses, while verbal text provides explicit logical structure, allowing communicators to exploit these differences for nuanced meaning-making. The theoretical foundations of multimodality trace back to Charles Sanders Peirce's late 19th-century semiotics, which categorized signs into icons, indices, and symbols, laying groundwork for understanding diverse representational forms. Its prominence in linguistics surged in the 1990s, propelled by the proliferation of digital media that demanded analysis of hybrid texts combining text, image, and sound. This historical shift paralleled developments in artificial intelligence, where systems process diverse inputs akin to human multimodal integration.

Applications in Discourse Analysis

In discourse analysis, systemic functional linguistics (SFL) provides a foundational framework for examining multimodal texts by extending Halliday's metafunctional approach to encompass visual, gestural, and spatial modes alongside language, enabling researchers to unpack how these elements construct ideational, interpersonal, and textual meanings. This method, often termed systemic functional-multimodal discourse analysis (SF-MDA), treats modes as social semiotic resources that interact to produce coherent discourse, as demonstrated in analyses of printed and digital artifacts where visual imagery reinforces linguistic propositions.^[31] Complementing SFL, transcription tools like ELAN facilitate the synchronization of multiple modes in video data, allowing analysts to annotate timelines for speech, gestures, gaze, and other nonverbal cues with high precision, which is essential for empirical studies of interactive discourse.^[32] ELAN's tiered annotation structure supports layered analysis, revealing temporal alignments that reveal how modes co-construct meaning in real-time communication.^[33] Applications of these methods appear prominently in case studies of advertising, such as multimodal analyses of Nike campaigns, where visual-verbal synergy—through dynamic imagery of athletic performance paired with empowering slogans—constructs ideologies of resilience and inclusivity, particularly for women.^[34] In political discourse, examinations of Barack Obama's 2008 speeches, including his Democratic National Convention address, highlight how gestures and rhetorical pauses integrate with verbal appeals to foster synthetic personalization, enhancing audience engagement and ideological alignment in staged contexts.^[35] These analyses underscore how multimodal orchestration amplifies persuasive effects, with gestures often mirroring linguistic rhythms to reinforce themes of unity and change. In digital contexts, social media memes serve as key multimodal artifacts, combining images, text, and emojis to convey satirical or ideological messages, as seen in analyses of election-related memes where visual irony critiques political rhetoric through layered semiotic choices.^[36] However, interpreting these artifacts cross-culturally poses challenges, particularly with emoji variations; for instance, the thumbs-up emoji may signify approval in Western contexts but offense in parts of the Middle East, complicating global discourse analysis and requiring culturally sensitive frameworks to avoid misattribution of intent.^[37] Such variations highlight the need for comparative studies to map modal equivalences across cultures. Empirical research using eye-tracking in the 2010s demonstrates that multimodal texts enhance comprehension over unimodal ones by directing attention to integrated elements, with systematic reviews showing consistent improvements in learning outcomes—often linked to better visual-text integration.^[38] For example, eye movements reveal faster processing and higher retention when visuals signal key textual information, supporting broader applications in discourse analysis for understanding cognitive impacts. As of 2025, multimodal discourse analysis has expanded with applications in social media platforms like TikTok and integration with artificial intelligence for analyzing digital artifacts, as evidenced by bibliometric surveys highlighting trends in sustainable tourism imaginaries and pandemic-era communications.^[39]^[40]

Human-Computer Interaction

Multimodal Interaction

Multimodal interaction in human-computer interaction (HCI) encompasses user-system communication through multiple input and output modalities, enabling simultaneous processing of natural inputs such as speech, gestures, touch, eye gaze, and body movements, alongside outputs delivered via audio, visual displays, and haptic feedback. This paradigm aims to mimic human multimodal communication, fostering more natural, robust, and error-resilient interfaces by leveraging diverse sensory channels.^[41]^[42] The roots of multimodal interaction draw briefly from linguistic multimodality, which informs HCI designs by emphasizing how humans integrate verbal and nonverbal cues in everyday discourse. Historically, the field emerged with Richard Bolt's seminal 1980 demonstration of the "Put-that-there" system at MIT, where users manipulated graphical objects on a large display by combining spoken commands with pointing gestures, demonstrating the potential for concerted voice-gesture synergy.^[43] This innovation marked a shift from unimodal interfaces, paving the way for broader adoption. The proliferation accelerated in the mobile era, particularly with Apple's introduction of Siri in 2011 on the iPhone 4S, which integrated voice-based querying with touch-screen interactions to handle tasks like scheduling and navigation, making multimodal capabilities accessible to mainstream users.^[44] At its core, multimodal interaction relies on principles such as complementarity, where modalities fulfill specialized roles—for instance, speech articulates abstract commands while gestures provide spatial references, enhancing precision and reducing ambiguity as illustrated in Bolt's system. Fusion strategies underpin this integration: early fusion combines raw signals from modalities at the feature level for low-latency processing, whereas late fusion merges higher-level semantic interpretations post-recognition to accommodate asynchronous inputs and improve decision accuracy.^[43] Practical examples abound in modern applications. In virtual reality environments, Oculus Quest devices since 2019 have incorporated hand-tracking for gesture-based manipulation alongside voice commands, allowing users to interact with virtual objects through natural pointing and verbal instructions without physical controllers. Similarly, in automotive infotainment, BMW's iDrive system debuted gesture controls in 2015 with the 7 Series, enabling drivers to perform actions like volume adjustment or call acceptance via hand waves detected by a 3D camera, often complemented by voice inputs to maintain focus on driving.^[45]

Design Principles and Challenges

Design principles for multimodal interfaces in human-computer interaction emphasize user-centered fusion to ensure seamless integration of multiple input and output modalities. A foundational framework is the CARE properties, proposed by Coutaz, Nigay, and colleagues, which characterize multimodal systems through Complementarity (modalities providing distinct information), Assignment (allocation of tasks to appropriate modalities), Representation (consistent mapping between modalities and referents), and Equivalence (equivalent functionality across modalities).^[46] These properties guide designers in creating interfaces that leverage natural human communication while minimizing redundancy or conflict, as demonstrated in early systems combining speech and gesture for map navigation. Accessibility standards further inform design, with WCAG 2.1 introducing guidelines for input modalities to support diverse users, such as ensuring pointer gestures have non-pointer alternatives and character key shortcuts for keyboard avoidance.^[47] Challenges in multimodal design include robust error handling for input conflicts, where ambiguous signals from different modalities—such as overlapping speech and gesture commands—require disambiguation through context models or mutual error correction. For instance, in speech-gesture interfaces, systems like those developed by Oviatt employ probabilistic fusion to resolve ambiguities, reducing recognition errors by up to 50% compared to unimodal approaches.^[48] Privacy concerns arise particularly with biometric modalities like gaze tracking, which collect sensitive data on user attention and intent; under the GDPR, such biometrics qualify as special category personal data, necessitating explicit consent and data minimization to mitigate risks of unauthorized profiling.^[49] Evaluation of multimodal interfaces often relies on usability metrics from controlled tests, where integration of modalities has shown task completion times reduced by approximately 10% in visual-spatial tasks relative to unimodal controls.^[48] However, a key issue is mode overload, where excessive simultaneous modalities strain cognitive resources, as explained by Wickens' multiple resource theory, which posits that demands on shared pools like visual or verbal channels lead to performance decrements and increased mental workload.^[50] Recent advancements in AI-assisted multimodality address these principles and challenges in wearable devices, exemplified by the Apple Vision Pro's integration of eye tracking, hand gestures, and voice commands in its visionOS platform, enabling natural spatial interactions while incorporating accessibility features like Switch Control for combined modality use.^[51]

Artificial Intelligence

Multimodal Learning

Multimodal learning in artificial intelligence refers to the process of training models on diverse input modalities—such as text, images, audio, and video—to capture and exploit cross-modal relationships that enhance understanding and performance, distinguishing it from unimodal deep learning, which relies on a single data type. This integration allows models to learn shared representations that reflect real-world interactions among modalities, enabling more comprehensive inference in complex scenarios. Seminal work, such as Ngiam et al.'s exploration of audiovisual deep networks, laid the foundation by demonstrating how joint training can improve feature extraction across senses like vision and sound.^[52] The field's early milestones emerged in the 2010s with initial efforts on modality fusion, including Ngiam et al.'s 2011 models that achieved state-of-the-art results in audiovisual speech recognition by learning shared representations, outperforming prior methods by leveraging deep architectures for cross-modal tasks.^[52] A pivotal shift occurred post-2017, following Vaswani et al.'s introduction of the Transformer architecture, which relied solely on attention mechanisms and facilitated scalable processing of sequential data, sparking widespread adoption in multimodal settings by enabling efficient alignment of disparate inputs like images and text.^[53] This transformer-driven surge has since dominated, powering advancements in tasks requiring synchronized multi-input reasoning. Key architectures in multimodal learning encompass early fusion, which concatenates raw inputs from multiple modalities at the outset to form a unified representation processed by a single network; late fusion, where independent unimodal models generate decisions that are subsequently aggregated, often via averaging or meta-learning; and hybrid fusion, which combines these by first deriving modality-specific features before joint integration to balance specialization and interaction.^[54] Attention-based mechanisms, particularly cross-attention, have become central for aligning modalities, as exemplified in Radford et al.'s CLIP model, which uses contrastive learning to bridge vision and language through shared embedding spaces.^[55] The primary benefits of multimodal learning include greater robustness and generalization, with empirical gains of 10-30% in accuracy on vision-language benchmarks; for instance, CLIP demonstrates a 26% improvement over unimodal visual baselines on the aYahoo dataset by incorporating textual supervision.^[55] In practical applications like video captioning, fusing RGB visual frames, audio signals, and textual cues yields substantial enhancements, such as a 47% relative increase in METEOR scores (from 6.98 to 10.24) compared to visual-only approaches on the ActivityNet Captions dataset.^[56] Drawing brief inspiration from human cognition, where sensory integration enables nuanced perception, these methods mimic biological processes to achieve more reliable AI outcomes.

Representation Learning Techniques

Representation learning in multimodal AI focuses on deriving compact, joint embeddings that capture shared and modality-specific information from diverse data sources such as text, images, and audio. Contrastive learning techniques align representations across modalities by maximizing similarity between paired samples while minimizing it for unpaired ones. A seminal approach is the Contrastive Language-Image Pretraining (CLIP) model, which employs an InfoNCE loss to train image and text encoders on large-scale paired data. The loss for image-to-text retrieval is defined as:

L_{i2t} = -\frac{1}{N} \sum_{i=1}^{N} \log \frac{\exp(\text{sim}(z_i^v, z_i^t)/\tau)}{\sum_{j=1}^{N} \exp(\text{sim}(z_i^v, z_j^t)/\tau)},

where z^v and z^t are the normalized embeddings from the vision and text encoders, \text{sim} denotes cosine similarity, \tau is a temperature parameter (typically 0.07), and N is the batch size; a symmetric text-to-image loss is averaged with this to form the total objective. Generative models also play a key role in learning discrete or continuous joint representations. The Vector Quantized Variational Autoencoder (VQ-VAE) extends traditional VAEs by quantizing continuous latent variables into a discrete codebook, enabling efficient modeling of multimodal data distributions without posterior collapse issues common in standard VAEs. In VQ-VAE, the encoder outputs are mapped to the nearest codebook vector via vector quantization, and the decoder reconstructs from these discrete latents, facilitating applications like multimodal generation where discrete codes bridge modalities such as speech and text.^[57] Alignment methods further enhance joint representations by discovering correlations between modalities. Canonical Correlation Analysis (CCA) classically identifies linear projections that maximize cross-modal correlations, but its deep extension, Deep Canonical Correlation Analysis (DCCA), uses nonlinear neural network transformations to capture complex dependencies. DCCA optimizes the trace norm of the correlation matrix between projected views, enabling better fusion in tasks like audio-visual learning. For zero-shot learning, modality-invariant embeddings—learned via methods like CLIP—allow transfer without task-specific fine-tuning by projecting all modalities into a shared space where semantic alignment enables generalization.^[58] Key challenges in these techniques include handling missing modalities and ensuring scalability. Modality missingness, where one or more data streams are absent, is addressed through imputation strategies using multimodal VAEs, which jointly model observed modalities and infer missing ones via variational inference in a shared latent space. For instance, joint VAEs reconstruct absent modalities by leveraging correlations learned during training, improving downstream performance on incomplete datasets. Scalability for large models is tackled via efficient architectures like gated cross-attention in the Flamingo visual language model, which inserts lightweight cross-attention layers into a pretrained 80B-parameter language model to condition on visual inputs without full retraining, achieving few-shot learning on vision-language tasks. As of 2025, state-of-the-art unified models like GPT-4o (released in 2024) exemplify advanced representation learning by natively handling text, vision, and audio in a single architecture, demonstrating strong performance on multimodal benchmarks such as Visual Question Answering (VQA) through end-to-end training on diverse data. These models build on prior techniques to enable seamless cross-modal reasoning.^[59]

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“Put-that-there”: Voice and gesture at the graphics interface
Voice and gesture inputs at the graphics interface can converge to provide a concerted, natural user modality.
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How Siri Works - Electronics | HowStuffWorks
While Siri is active, you can continue to touch the microphone icon to enter new voice commands. Before using Siri, you will want to go to "Settings ...
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https://www.itgovernance.eu/blog/en/does-the-gdpr-allow-you-to-track-biometric-data
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[PDF] THE CARE PROPERTIES - IIHM
In this paper, we propose the CARE properties as a simple way of characterising and assessing aspects of multimodal interaction: the Complementarity,.
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Web Content Accessibility Guidelines (WCAG) 2.1 - W3C
May 6, 2025 · WCAG 2.1 was initiated with the goal to improve accessibility guidance for three major groups: users with cognitive or learning disabilities, ...Understanding WCAG · User Agent Accessibility · WCAG21 history · Errata
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[PDF] Multimodal Interfaces - cs.wisc.edu
For example, in a multi- modal speech and pen-based gesture interface users will select the input mode that they judge to be less error prone for par- ticular ...
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Does the GDPR allow you to track biometric data? - IT Governance
Oct 22, 2018 · Under the EU GDPR, biometrics is considered a “special category of personal data” that requires both a special legal basis for processing.
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Multiple resources and mental workload - PubMed
The most important application of the multiple resource model is to recommend design changes when conditions of multitask resource overload exist.Missing: HCI load
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Apple Vision Pro
Built on the foundation of macOS, iOS, and iPadOS, visionOS enables powerful spatial experiences that you navigate naturally using your eyes, hands, and voice.Apple (AU) · Apple (CA) · Apple (SG) · Apple (UK)Missing: multimodal | Show results with:multimodal
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[PDF] Multimodal Deep Learning - People | MIT CSAIL
We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we ...
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[1706.03762] Attention Is All You Need - arXiv
Jun 12, 2017 · We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.
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Multimodal deep learning for biomedical data fusion: a review - PMC
Jan 28, 2022 · Largely, fusion strategies can be categorized according to the state of the input to the fusion layers into early, intermediate and late fusion ...
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Learning Transferable Visual Models From Natural Language ...
The paper proposes learning visual models by predicting image-caption pairs, then using natural language for zero-shot transfer to downstream tasks.
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Fusion of Multi-Modal Features to Enhance Dense Video Caption
Jun 14, 2023 · In this paper, we propose a fusion model that combines the Transformer framework to integrate both visual and audio features in the video for captioning.<|separator|>
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[1711.00937] Neural Discrete Representation Learning - arXiv
Nov 2, 2017 · In this paper, we propose a simple yet powerful generative model that learns such discrete representations.
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[PDF] Deep Canonical Correlation Analysis
We introduce Deep Canonical Correlation. Analysis (DCCA), a method to learn com- plex nonlinear transformations of two views.
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Hello GPT-4o - OpenAI
May 13, 2024 · As measured on traditional benchmarks, GPT‑4o achieves GPT‑4 Turbo-level performance on text, reasoning, and coding intelligence, while ...Missing: VQA | Show results with:VQA