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Human Vision toLanguage Processing
%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=Human+Vision+to+Language+Processing&simid=608035110968444796&FORM=IRPRST&ck=61CB6240C5A588FE79CFB9899EF4DB90&selectedIndex=21&itb=0)
%252C+enabling+multimodal+processing+of+visual+and+linguistic+data.+However%252C+their+performance+has+been+typically+assessed+on+general+scene+understanding+-+recognizing+objects%252C+attributes%252C+and+actions+-+rather+than+cultural+comprehension.+This+study+introduces+CulturalVQA%252C+a+visual+question-answering+benchmark+aimed+at+assessing+VLM%27s+geo-diverse+cultural+understanding.+We+curate+a+collection+of+2%252C378+image-question+pairs+with+1-5+answers+per+question+representing+cultures+from+11+countries+across+5+continents.+The+questions+probe+understanding+of+various+facets+of+culture+such+as+clothing%252C+food%252C+drinks%252C+rituals%252C+and+traditions.+Benchmarking+VLMs+on+CulturalVQA%252C+including+GPT-4V+and+Gemini%252C+reveals+disparity+in+their+level+of+cultural+understanding+across+regions%252C+with+strong+cultural+understanding+capabilities+for+North+America+while+significantly+lower+performance+for+Africa.+We+observe+disparity+in+their+performance+across+cultural+facets+too%252C+with+clothing%252C+rituals%252C+and+traditions+seeing+higher+performances+than+food+and+drink.+These+disparities+help+us+identify+areas+where+VLMs+lack+cultural+understanding+and+demonstrate+the+potential+of+CulturalVQA+as+a+comprehensive+evaluation+set+for+gauging+VLM+progress+in+understanding+diverse+cultures.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.7b802d928122f13cdd0d5f832ed45270%3frik%3dEAlaR1%252ftT6FcAQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Human+Vision+to+Language+Processing&simid=607990095461888870&FORM=IRPRST&ck=533CA0F8E4A1B29DBA45E4548A515B1B&selectedIndex=26&itb=0)
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