If the benchmark doesn’t exist, a “new” icon will appear signifying a new leaderboard.If a benchmark already exists for a dataset/task pair you enter, you’ll see a link appear.Note that you can use parentheses to highlight details, for example: BERT Large (12 layers), FoveaBox (ResNeXt-101), EfficientNet-B7 (NoisyStudent). What are the model naming conventions? Model name should be straightforward, as presented in the paper. ImageNet on Image Classification already exists with metrics Top 1 Accuracy and Top 5 Accuracy. You should check if a benchmark already exists to prevent duplication if it doesn’t exist you can create a new dataset.
#Paradigm shift images code
Then choose a task, dataset and metric name from the Papers With Code taxonomy. You can manually edit the incorrect or missing fields. How do I add a new result from a table? Click on a cell in a table on the left hand side where the result comes from. Help! Don’t worry! If you make mistakes we can revert them: everything is versioned! So just tell us on the Slack channel if you’ve accidentally deleted something (and so on) - it’s not a problem at all, so just go for it! I’m editing for the first time and scared of making mistakes. Where do referenced results come from? If we find referenced results in a table to other papers, we show a parsed reference box that editors can use to annotate to get these extra results from other papers. Where do suggested results come from? We have a machine learning model running in the background that makes suggestions on papers. 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. TheĪnalysis is concluded by arguing that if images are to be used effectively toĭetect and identify human rights violations by rights advocates, greaterĪttention to gathering task-specific visual concepts from large-scale web Monitoring efforts when combined with novel computer vision approaches. The capacity to contribute complementary data to operational human rights This study demonstrates that real-world images have Potential of images in human rights context including the opportunities andĬhallenges they present. Media networks images and videos of specific events. Ordinary citizens, victims of human rights abuse, and participants in armedĬonflicts, protests, and disaster situations to capture and share via social Phones and tablets, combined with ever improving internet networks have enabled
The growing presence of devices carrying digital cameras, such as mobile A Paradigm Shift: Detecting Human Rights Violations Through Web Images