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20172022
most citedPredicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses

8 citations · 12 across the 11 of their papers we have counts for

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cs.CV20228 cited

Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses

Phoebe Chua, Dimos Makris, Dorien Herremans +2

Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of…

cs.CV2022

FRIDA -- Generative Feature Replay for Incremental Domain Adaptation

Sayan Rakshit, Anwesh Mohanty, Ruchika Chavhan +3

We tackle the novel problem of incremental unsupervised domain adaptation (IDA) in this paper. We assume that a labeled source domain and different unlabeled target domains are inc…

cs.CV2021

The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motion

R. M. Cichy, K. Dwivedi, B. Lahner +8

The sciences of natural and artificial intelligence are fundamentally connected. Brain-inspired human-engineered AI are now the standard for predicting human brain responses during…

cs.CV2020

Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning

Kshitij Dwivedi, Jiahui Huang, Radoslaw Martin Cichy +1

In this paper, we tackle an open research question in transfer learning, which is selecting a model initialization to achieve high performance on a new task, given several pre-trai…

cs.CV2020

Using Human Psychophysics to Evaluate Generalization in Scene Text Recognition Models

Sahar Siddiqui, Elena Sizikova, Gemma Roig +2

Scene text recognition models have advanced greatly in recent years. Inspired by human reading we characterize two important scene text recognition models by measuring their domain…

cs.CV2019

LCD: Learned Cross-Domain Descriptors for 2D-3D Matching

Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua +3

In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network…