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Learning for VisionLanguageModel
%252C+several+attempts+have+been+made+to+extend+them+to+the+visual+domain.+From+having+a+visual+assistant+that+could+guide+us+through+unfamiliar+environments+to+generative+models+that+produce+images+using+only+a+high-level+text+description%252C+the+vision-language+model+(VLM)+applications+will+significantly+impact+our+relationship+with+technology.+However%252C+there+are+many+challenges+that+need+to+be+addressed+to+improve+the+reliability+of+those+models.+While+language+is+discrete%252C+vision+evolves+in+a+much+higher+dimensional+space+in+which+concepts+cannot+always+be+easily+discretized.+To+better+understand+the+mechanics+behind+mapping+vision+to+language%252C+we+present+this+introduction+to+VLMs+which+we+hope+will+help+anyone+who+would+like+to+enter+the+field.+First%252C+we+introduce+what+VLMs+are%252C+how+they+work%252C+and+how+to+train+them.+Then%252C+we+present+and+discuss+approaches+to+evaluate+VLMs.+Although+this+work+primarily+focuses+on+mapping+images+to+language%252C+we+also+discuss+extending+VLMs+to+videos.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.0a5e5fc1882482dedbdd326584424787%3frik%3dTuFcqck7yPAMUw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Graph+Learning+for+Vision+Language+Model&simid=608035110937127922&FORM=IRPRST&ck=61CB6240C5A588FE79CFB9899EF4DB90&selectedIndex=21&itb=0)
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