1.0Debiasing vision datasetshttps://mscvprojects.ri.cmu.edu/2025team12-1Overview - Debiasing vision datasetsrich600338<blockquote class="wp-embedded-content" data-secret="zjTiRquEh0"><a href="https://mscvprojects.ri.cmu.edu/2025team12-1/">Overview</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2025team12-1/embed/#?secret=zjTiRquEh0" width="600" height="338" title="“Overview” — Debiasing vision datasets" data-secret="zjTiRquEh0" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script>
/*! This file is auto-generated */
!function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document);
//# sourceURL=https://mscvprojects.ri.cmu.edu/2025team12-1/wp-includes/js/wp-embed.min.js
</script>
Motivation Harmful bias is an issue that has persisted with the development of computer vision. Among the potential causes, a major bias contributor is the training dataset. Biases in training datasets often manifest as spurious correlations, which are unintended associations with nuisance attributes that undermine both model effectiveness and fairness. Gender biases are evident in … Continue reading ""https://mscvprojects.ri.cmu.edu/2025team12-1/wp-content/uploads/sites/128/2025/05/Captions.png715425