{"url":"/method/mhma","slug":"mhma","name":"MHMA","full_name":"Multi-Heads of Mixed Attention","full_name_withheld":false,"description_markdown":"The multi-head of mixed attention combines both self- and cross-attentions, encouraging high-level learning of interactions between entities captured in the various attention features. It is build with several attention heads, each of the head can implement either self or cross attention. A self attention is when the key and query features are the same or come from the same domain features. A cross attention is when the key and query features are generated from different features. Modeling MHMA allows a model to identity the relationship between features of different domains. This is very useful in tasks involving relationship modeling such as human-object interaction, tool-tissue interaction, man-machine interaction, human-computer interface, etc.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos","paper":"/paper/rendezvous-attention-mechanisms-for-the","first_author":"Chinedu Innocent Nwoye","n_authors":8,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/rendezvous-attention-mechanisms-for-the"},"source":{"url":"https://arxiv.org/abs/2109.03223v2","title":"Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/CAMMA-public/rendezvous","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Rendezvous","url":"/methods/category/rendezvous","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Vision Transformers","url":"/methods/category/vision-transformers","pwc_aliases":["vision-transformer"]},{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Transformers","url":"/methods/category/transformers","pwc_aliases":[]},{"area":"General","area_id":"general","collection":"Attention Mechanisms","url":"/methods/category/attention-mechanisms","pwc_aliases":["attention-mechanisms-1"]},{"area":"General","area_id":"general","collection":"Attention","url":"/methods/category/attention","pwc_aliases":[]},{"area":"General","area_id":"general","collection":"Attention Modules","url":"/methods/category/attention-modules","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/rendezvous-attention-mechanisms-for-the","title":"Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos","date":"2021-09-07","arxiv_id":"2109.03223","n_code_links":8,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/action-triplet-recognition","name":"Action Triplet Recognition","papers":1},{"task":null,"name":"Triplet","papers":1}],"tasks_shown":2,"n_tasks":2,"usage_by_year":[{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/mhma"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}