Abstract:
Remote sensing cross-modal text-image retrieval (RSCTIR) is a flexible and human-centered approach to retrieving rich information from different modalities, which has attracted plenty of attention in recent years. It remains challenging because the current methods usually ignore the varying difficulty levels of different sample pairs, stemming from the large image distribution difference and the high text similarity in the remote sensing (RS) field. Therefore, in this paper, we propose an innovative hypersphere-based visual semantic alignment (HVSA) network via curriculum learning. Specifically, we first design an adaptive alignment strategy based on curriculum learning, that aligns RS image-text pairs from easy to hard. Sample pairs with different levels of difficulty are treated unequally, and we obtain a better embedding representation when projecting the features onto the unit hypersphere. Then, to measure the robustness of cross-modal feature alignment on the unit hypersphere, we introduce the feature uniformity strategy. It reduces the occurrence of mismatching cases and improves generalization performance. Finally, we design the key-entity attention (KEA) mechanism to alleviate the problem of information imbalance among different modalities. KEA has the ability to extract information about the key entity which is aligned with textual information. Despite its conciseness, our framework achieves state-of-the-art performance on classical datasets of RSCTIR tasks while enjoying faster inference. The summed recall of HVSA on the RISCD and RSITMD is 120.97 and 198.94, 2.50 and 10.49 points ahead of the current best methods, respectively. Extensive experiments demonstrate the competitiveness of our method. The code has been released at https://github.com/ZhangWeihang99/HVSA.
| Specialty | Vegetarian |
| Ingredient Type | Vegetarian |
| Brand | Lavian Exotique |
| Form | Bar Brownie |
| Package Information | Box |
| Manufacturer | Prayagh Nutri Product Pvt Ltd |
| Item part number | LE 014 - 20pcs Crème Bakes (Pack of 2) |
| Net Quantity | 40.00 count |
Honestly, I regret buying this item. The quality is subpar, and it feels like a waste of money. I wouldn't recommend it to anyone.
I am extremely satisfied with this purchase. The item arrived promptly, and the quality is exceptional. It's evident that the makers paid attention to detail. Overall, a fantastic buy!
I am genuinely delighted with this item. It's a total winner! The quality is superb, and it has added so much convenience to my daily routine. Highly satisfied customer!
Very impressed with this purchase. The item is of excellent quality, and it has exceeded my expectations.
Tanto Cyber: A platform sharing free eBooks & tech insights, focusing on cybersecurity and knowledge.
29 Sep 2023 06:40:PM
Avoid this product. The quality is terrible, and it started falling apart almost immediately. I wish I had read more reviews before buying. Lesson learned.