Learning With Privileged Multimodal Knowledge for Unimodal Segmentation.

Multimodal deep learning systems which employ multiple modalities like text, image, audio, video, etc., are showing better performance in comparison with individual modalities (i.e., unimodal) systems. ... Our final goal is to discuss challenges and perspectives along with the important ideas and directions for future work that we hope to be beneficial for the entire research community focusing on this exciting ... Knowledge distillation uses teacherstudent network and privileged information learning [46] to have a multimodal distillation network for video action recognition using RGB and depth modality. ...

arXiv:2107.13782v2 fatcat:s4spofwxjndb7leqbcqnwbifq4

Open Access Multiple Versions

In current literature, multimodal biometric approach is addressed at length for purpose of improving secured access into personal devices. ... In this paper, a multilayered multimodal biometric approach using three biometric methods (such as finger print, face and voice) is proposed for smartphones. ... In this paper, a multi-step/multi-layered multimodal biometric for smartphone devices is presented to investigate authentication with full or partial privileges depending on work environments. ...

doi:10.3991/ijim.v14i15.15825 fatcat:avy573ktqvd4rayqrb7k2hru34

DOAJ OJS Szczepanski

The full dataset is publicly available for use at https://github. com/soujanyaporia/MUStARD. * Equal contribution. 1 MUStARD is an abbreviation for MUltimodal SARcasm Dataset. ... MUStARD consists of audiovisual utterances annotated with sarcasm labels. ... Acknowledgements We are grateful to Gautam Naik for his help in curating part of the dataset from online resources. ...

doi:10.18653/v1/p19-1455 dblp:conf/acl/CastroHPZMP19 fatcat:q7j7t5y43rf3ljevh5avfmgpjm

Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. ... MultiBench introduces impactful challenges for future research, including scalability to large-scale multimodal datasets and robustness to realistic imperfections. ... Multimodal models outperform unimodal models when it comes to robustness (and initial performance). This is especially true for imperfections in the image modality. ...

arXiv:2107.07502v2 fatcat:ls47dr7lpfhkbfry4r6dtqjtua

Multiple Versions

Our paper aims to fill this gap by proposing MultiViz, a method for analyzing the behavior of multimodal models by scaffolding the problem of interpretability into 4 stages: (1) unimodal importance: how ... The promise of multimodal models for real-world applications has inspired research in visualizing and understanding their internal mechanics with the end goal of empowering stakeholders to visualize model ... PPL is partially supported by a Facebook PhD Fellowship and a Carnegie Mellon University's Center for Machine Learning and Health Fellowship. ...

arXiv:2207.00056v1 fatcat:vxg2lcvm6jgghldw74b7onwjje

We introduce a novel framework employing deep learning techniques to coordinate the channels of textual and im-age processing. ... This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook. ... For binary classification experiments, we first observe that unimodal BiL is highly competitive with the multimodal FCM, SCM, and TKM baselines. ...

arXiv:2105.01129v1 fatcat:lakkm66thrfy3kidfc734cfjbe

Open Access

Multimodal recognition is therefore acknowledged as a vital component of the next generation of spoken language systems. ... Speech recognition and speaker recognition by machine are crucial ingredients for many important applications such as natural and flexible human-machine interfaces. ... Also, to combat data variability, the symbiotic combination of sensor fusion with mature techniques developed for robust unimodal recognition, is also a worthwhile research avenue. ...

doi:10.1109/6046.985551 fatcat:6fezo5zovbdtti3lzxh24ksaii

A third reason the debate is important is that a view that makes multimodal perception a flexible, learned process (see, for instance, Connolly, 2014) fits more naturally with the emerging view of perception ... Say, for instance, that the target set is a set of two horizontal and one vertical line segments. ...

doi:10.3389/fpsyg.2014.01095 pmid:25309498 pmcid:PMC4176039 fatcat:i2ywyzadcbahvgvikfvp5mr6ja

DOAJ Szczepanski

imaging are unimodal, i.e., they only learn features from pixel-level information. ... To better assess these challenges, we present RadFusion, a multimodal, benchmark dataset of 1794 patients with corresponding EHR data and high-resolution computed tomography (CT) scans labeled for pulmonary ... in TPR (i.e., Sensitivity) for the privileged and under-privileged groups following the evaluation protocol in [19, 46, 37] . ...

arXiv:2111.11665v2 fatcat:jtbpdwnq6jb2znuw6qucfjf3r4

Multiple Versions

Experiments show that the reachability prior combined with multi-hypotheses learning improves multimodal prediction of the future location of tracked objects and, for the first time, the emergence of new ... In contrast to many previous works, we do not assume structural knowledge from maps. ... Hence, a multimodal method is favored over a unimodal one. Still, such significant improvement indicates the need for multimodality. ...

arXiv:2006.04700v1 fatcat:2arah4qiybdaro63tykxw24izy

For example, in a dyadic interaction, the speaker contextual cues such as gaze shifts or changes in prosody will influence listener backchannel feedback (e.g., head nod). ... Multimodal integration between context and visual observations is performed using a discriminative sequential model (Latent-Dynamic Conditional Random Fields) trained on previous interactions. ... In every case, no knowledge of the future is needed. ...

doi:10.1145/1452392.1452426 dblp:conf/icmi/MorencyKG08 fatcat:4pwhgrrslnez3emvdgyhhggppy

This would better align with the new trend in stroke rehabilitation which challenges the popular idea of the existence of an ultimate good-for-all intervention. ... Reduced hand dexterity is a common component of sensorimotor impairments for individuals after stroke. ... privileged over a unimodal approach. ...

doi:10.3389/fresc.2022.789479 fatcat:fb2noy4rlndzpapd46humlirsm

DOAJ

The unique role of these areas is to bind multiple unimodal and other transmodal areas into distributed but integrated multimodal representations. ... This process occurs along a core synaptic hierarchy which includes the primary sensory, upstream unimodal, downstream unimodal, heteromodal, paralimbic and limbic zones of the cerebral cortex. ... Nobre for detailed and insightful comments. This work was supported in part by NS 20285, NS 30863 and AG 13854. ...

doi:10.1093/brain/121.6.1013 pmid:9648540 fatcat:xv6jy7ff7nfpjb4ruzrhqwzfym

The Author Index contains the primary entry for each item, listed under the first author's name. ... Zuo, Y., +, TMM 2021 772-783 Hard Pixel Mining for Depth Privileged Semantic Segmentation. ... Liu, Z., +, TMM 2021 3414-3426 Data mining Hard Pixel Mining for Depth Privileged Semantic Segmentation. ...

doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Then we review several different deep learning architectures for multimodal semantic segmentation. ... us to improve performances with respect to the exploitation of a single source of information. ... Outline In this paper, we focus on analyzing and discussing deep learning based fusion methods in multimodal semantic segmentation. ...

doi:10.3390/technologies10040090 fatcat:zfalhwsz3ndrnog62l22ucqc7e

DOAJ

Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directions [article]

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Multi-Layered Multimodal Biometric Authentication for Smartphone Devices

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Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)

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MultiBench: Multiscale Benchmarks for Multimodal Representation Learning [article]

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MultiViz: An Analysis Benchmark for Visualizing and Understanding Multimodal Models [article]

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Towards A Multi-agent System for Online Hate Speech Detection [article]

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A review of speech-based bimodal recognition

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Multisensory perception as an associative learning process

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RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR [article]

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Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior [article]

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Context-based recognition during human interactions

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Somesthetic, Visual, and Auditory Feedback and Their Interactions Applied to Upper Limb Neurorehabilitation Technology: A Narrative Review to Facilitate Contextualization of Knowledge

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From sensation to cognition

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2021 Index IEEE Transactions on Multimedia Vol. 23

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Multimodal Semantic Segmentation in Autonomous Driving: A Review of Current Approaches and Future Perspectives

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