Search Results for author: Digbalay Bose

Found 12 papers, 4 papers with code

Can Text-to-image Model Assist Multi-modal Learning for Visual Recognition with Visual Modality Missing?

no code implementations14 Feb 2024 Tiantian Feng, Daniel Yang, Digbalay Bose, Shrikanth Narayanan

Specifically, we propose a simple but effective multi-modal learning framework GTI-MM to enhance the data efficiency and model robustness against missing visual modality by imputing the missing data with generative transformers.

MM-AU:Towards Multimodal Understanding of Advertisement Videos

no code implementations27 Aug 2023 Digbalay Bose, Rajat Hebbar, Tiantian Feng, Krishna Somandepalli, Anfeng Xu, Shrikanth Narayanan

Advertisement videos (ads) play an integral part in the domain of Internet e-commerce as they amplify the reach of particular products to a broad audience or can serve as a medium to raise awareness about specific issues through concise narrative structures.

FedMultimodal: A Benchmark For Multimodal Federated Learning

no code implementations15 Jun 2023 Tiantian Feng, Digbalay Bose, Tuo Zhang, Rajat Hebbar, Anil Ramakrishna, Rahul Gupta, Mi Zhang, Salman Avestimehr, Shrikanth Narayanan

In order to facilitate the research in multimodal FL, we introduce FedMultimodal, the first FL benchmark for multimodal learning covering five representative multimodal applications from ten commonly used datasets with a total of eight unique modalities.

Emotion Recognition Federated Learning +1

Signal Processing Grand Challenge 2023 -- e-Prevention: Sleep Behavior as an Indicator of Relapses in Psychotic Patients

no code implementations17 Apr 2023 Kleanthis Avramidis, Kranti Adsul, Digbalay Bose, Shrikanth Narayanan

This paper presents the approach and results of USC SAIL's submission to the Signal Processing Grand Challenge 2023 - e-Prevention (Task 2), on detecting relapses in psychotic patients.

Task 2

Contextually-rich human affect perception using multimodal scene information

1 code implementation13 Mar 2023 Digbalay Bose, Rajat Hebbar, Krishna Somandepalli, Shrikanth Narayanan

The process of human affect understanding involves the ability to infer person specific emotional states from various sources including images, speech, and language.

Multimodal Estimation of Change Points of Physiological Arousal in Drivers

1 code implementation28 Oct 2022 Kleanthis Avramidis, Tiantian Feng, Digbalay Bose, Shrikanth Narayanan

Detecting unsafe driving states, such as stress, drowsiness, and fatigue, is an important component of ensuring driving safety and an essential prerequisite for automatic intervention systems in vehicles.

Time Series Time Series Analysis

MovieCLIP: Visual Scene Recognition in Movies

1 code implementation20 Oct 2022 Digbalay Bose, Rajat Hebbar, Krishna Somandepalli, Haoyang Zhang, Yin Cui, Kree Cole-McLaughlin, Huisheng Wang, Shrikanth Narayanan

Longform media such as movies have complex narrative structures, with events spanning a rich variety of ambient visual scenes.

Genre classification Scene Recognition

Understanding of Emotion Perception from Art

no code implementations13 Oct 2021 Digbalay Bose, Krishna Somandepalli, Souvik Kundu, Rimita Lahiri, Jonathan Gratch, Shrikanth Narayanan

Computational modeling of the emotions evoked by art in humans is a challenging problem because of the subjective and nuanced nature of art and affective signals.

Cross Domain Emotion Recognition using Few Shot Knowledge Transfer

no code implementations11 Oct 2021 Justin Olah, Sabyasachee Baruah, Digbalay Bose, Shrikanth Narayanan

Emotion recognition from text is a challenging task due to diverse emotion taxonomies, lack of reliable labeled data in different domains, and highly subjective annotation standards.

Emotion Recognition Transfer Learning

Clustering using Vector Membership: An Extension of the Fuzzy C-Means Algorithm

no code implementations14 Dec 2013 Srinjoy Ganguly, Digbalay Bose, Amit Konar

We also examine the efficacy of the proposed scheme by analyzing its performance on image segmentation examples and comparing it with the classical Fuzzy C-means clustering algorithm.

Clustering Image Segmentation +1

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