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Found 6 papers, 1 papers with code
Predicting population neural activity in the Algonauts challenge using end-to-end trained Siamese networks and group convolutions
The Algonauts challenge is about predicting the object representations in the form of Representational Dissimilarity Matrices (RDMS) derived from visual brain regions.
Recognition of Advertisement Emotions with Application to Computational Advertising
Advertisements (ads) often contain strong affective content to capture viewer attention and convey an effective message to the audience.
Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements
Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.
Human peripheral blur is optimal for object recognition
Our vision is sharpest at the center of our gaze and becomes progressively blurry into the periphery.
Can you tell where in India I am from? Comparing humans and computers on fine-grained race face classification
We have developed a novel face database of close to 1650 diverse Indian faces labeled for fine-grained race (South vs North India) as well as for age, weight, height and gender.
Deep neural networks can be improved using human-derived contextual expectations
To measure contextual expectations, we asked human subjects to indicate the scale, location and likelihood at which cars or people might occur in scenes without these objects.
