Region Captioning using Multimodal Deep Learning скачать в хорошем качестве

Region Captioning using Multimodal Deep Learning

𝐒𝐮𝐦𝐦𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝟐𝟎𝟐𝟓 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐍𝐚𝐦𝐞: 𝐑𝐞𝐠𝐢𝐨𝐧 𝐂𝐚𝐩𝐭𝐢𝐨𝐧𝐢𝐧𝐠 𝐮𝐬𝐢𝐧𝐠 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 In this project, we designed and implemented a 𝐑𝐞𝐠𝐢𝐨𝐧 𝐂𝐚𝐩𝐭𝐢𝐨𝐧𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦 that bridges 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠. Unlike traditional image captioning models that generate a single global sentence, our system produces 𝐟𝐢𝐧𝐞-𝐠𝐫𝐚𝐢𝐧𝐞𝐝 𝐜𝐚𝐩𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐫𝐞𝐠𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡𝐢𝐧 𝐚𝐧 𝐢𝐦𝐚𝐠𝐞, enabling deeper scene understanding. 𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐚𝐭𝐡 & 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 • Neural Networks → CNNs → RNNs / GRUs • Vision–Language Models • Multimodal Representation Learning 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 & 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 • Dataset: Flickr30k Entities • Region annotations with bounding boxes • Caption tokenization using NLTK • Vocabulary creation with special tokens • Dynamic batching using a custom collate_fn 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 • Region proposal: Faster R-CNN • Visual encoder: VGG-19 CNN for region-level features • Language encoder: GRU-based text embeddings • Shared embedding space aligning image regions and text • Alignment model: Max-Margin Ranking Loss • Generative model: CNN–RNN for region-wise caption generation 𝐊𝐞𝐲 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 Learning a 𝐣𝐨𝐢𝐧𝐭 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐬𝐩𝐚𝐜𝐞 where semantically related image regions and textual descriptions lie close together, enabling accurate region-level caption generation. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 • Autonomous perception systems • Assistive technologies for visually impaired users • Surveillance and activity understanding • Medical image interpretation • Image–text retrieval systems This project reflects a strong transition from 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 𝐭𝐨 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. 𝐓𝐞𝐚𝐦 𝐌𝐞𝐦𝐛𝐞𝐫𝐬: Aman Karki, Ritika Varshney, Nidhi Soni 𝐌𝐞𝐧𝐭𝐨𝐫𝐬: Shaan Vora, Rajas Daryapurkar, Roushni Sareen, Devansh Palan, Sameh Nadeem, Siddharth Hoonka