How image search works at Dropbox // By Thomas Berg • May 11, 2021
Photos are among the most common types of files in Dropbox, but searching for them by filename is even less productive than it is for text-based files. When you re looking for that photo from a picnic a few years ago, you surely don t remember that the filename set by your camera was
2017-07-04 12.37.54.jpg.
Instead, you look at individual photos, or thumbnails of them, and try to identify objects or aspects that match what you’re searching for whether that’s to recover a photo you’ve stored, or perhaps discover the perfect shot for a new campaign in your company’s archives. Wouldn’t it be great if Dropbox could pore through all those images for you instead, and call out those which best match a few descriptive words that you dictated? That’s pretty much what our image search does.
Qu est-ce que l IA ? Tout ce que vous devez savoir sur l intelligence artificielle zdnet.fr - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from zdnet.fr Daily Mail and Mail on Sunday newspapers.
Machine learning faces its toughest challenge yet: Discerning donuts from bagels
Machine learning may border on magic, and provide many of the biggest technical benefits we ve enjoyed in the last decade, but it has plenty of weak spots. One of Google s biggest concerns is that models are often trained using example data that s too easy to interpret, making them unprepared for the greater ambiguity of the real world. Case in point: Telling a donut from a bagel.
It s an easy mistake to make, they share so many characteristics: Both are round, they ve got a hole in them, and sometimes a visible texture on the top. It may even be something you or I could have difficulty recognizing the difference between in the right circumstance, but it doesn t actually end up being that much of an issue for us, often thanks to context.
Microsoft’s new research focuses on improving the image-encoding module. When combined with VL fusion modules such as OSCAR and VIVO, Microsoft’s newest VL system scored big on the most competitive artificial intelligence (AI) benchmarks, including visual question answering (VQA), Microsoft COCO Image Captioning, and novel object captioning (nocaps).
The tech giant also highlighted that VinVL significantly surpasses human performance on the nocaps leaderboard for consensus-based image description evaluation (CIDEr).
Microsoft trained its VinVL object-attribute detection model using a large object detection dataset containing 2.49 million images ascribed to 1,848 object classes and 524 attribute classes to achieve the results mentioned above. Microsoft formed the dataset by merging four public object detection datasets (COCO, Open Images, Objects365, and VG).
Presunto dolo dietro l incendio di un negozio quinewspisa.it - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from quinewspisa.it Daily Mail and Mail on Sunday newspapers.