Papers › Neural Twins Talk & Alternative Calculations
Neural Twins Talk & Alternative Calculations
Zanyar Zohourianshahzadi, Jugal K. Kalita
Inspired by how the human brain employs a higher number of neural pathways when describing a highly focused subject, we show that deep attentive models used for the main vision-language task of image captioning, could be extended to achieve better performance. Image captioning bridges a gap between computer vision and natural language processing. Automated image captioning is used as a tool to eliminate the need for human agent for creating descriptive captions for unseen images.Automated image captioning is challenging and yet interesting. One reason is that AI based systems capable of generating sentences that describe an input image could be used in a wide variety of tasks beyond generating captions for unseen images found on web or uploaded to social media. For example, in biology and medical sciences, these systems could provide researchers and physicians with a brief linguistic description of relevant images, potentially expediting their work.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections