![]() Blue is a referenced result that originates from a different paper. What do the colors mean? Green means the result is approved and shown on the website. A result consists of a metric value, model name, dataset name and task name. What are the colored boxes on the right hand side? These show results extracted from the paper and linked to tables on the left hand side. It shows extracted results on the right hand side that match the taxonomy on Papers With Code. What is this page? This page shows tables extracted from arXiv papers on the left-hand side. ![]() The improvement in performance is particularly large when the number of shots is very small. ![]() Experiments also show that our model can effectively adjust its focus on the two modalities. Through a series of experiments, we show that by this adaptive combination of the two modalities, our model outperforms current uni-modality few-shot learning methods and modality-alignment methods by a large margin on all benchmarks and few-shot scenarios tested. ![]() Based on these two intuitions, we propose a mechanism that can adaptively combine information from both modalities according to new image categories to be learned. Moreover, when the support from visual information is limited in image classification, semantic representations (learned from unsupervised text corpora) can provide strong prior knowledge and context to help learning. While for others, the inverse might be true. For certain concepts, visual features might be richer and more discriminative than text ones. Visual and semantic feature spaces have different structures by definition. In this paper, we propose to leverage cross-modal information to enhance metric-based few-shot learning methods. You can even control it with URL schemes.Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. You can shrink the break window if you want to spend it at your computer. You can set custom work and break durations any time you want. You can control it entirely from your keyboard. You can set a hotkey to invoke Tadam from anywhere. It’s *designed* to be annoying enough that you’ll take the break like you know you should. The window covers most of your screen, and if you move it, it will snap back after 30 seconds. You can’t ignore it - you can either take a break or choose to work a little more. When it’s time for a break, a big window pops up, reminding you to take a break. To stay focused, take regular breaks and let your mind recharge. Your brain is like a muscle - it gets tired with use. (Or click the notification to give yourself more time.) Wrap up your work for the cycle and prepare for a break. When work time is almost up, you’ll get a gentle reminder to hurry up. Out of the way: A quick glance gives you a sense of how much time you have left. Only what you need for productivity and nothing more.Īt your fingertips: Tadam lives in the menu bar, so it’s always just a click away. Simple: Few buttons, few features, few options. Tadam helps you stay focused at work by reminding you to take regular breaks from work.Ĥ. Was macht Tadam tun? Simple Pomodoro timer that stays out of your way so you can get your work done.
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