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This is an implementation for vision and language multimodal research developed on top of Pythia

Pythia

Model Implementation

We have proposed few major improvements over Pythia, which is an implementation for the LoRRA model. The dynamic answering space is expanded by adding an Instance segmentation module. We have also replaced the existing OCR with spell correcting OCR and add the spatial features of the OCR. We have also implemented n-gram to the modified OCR.

Pythia

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For testing run our notebook

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A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

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var __re = new RegExp('^' + "github\\.com" + '
GitHub - anandroid/pythia: A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) · GitHub
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This is an implementation for vision and language multimodal research developed on top of Pythia

Pythia

Model Implementation

We have proposed few major improvements over Pythia, which is an implementation for the LoRRA model. The dynamic answering space is expanded by adding an Instance segmentation module. We have also replaced the existing OCR with spell correcting OCR and add the spatial features of the OCR. We have also implemented n-gram to the modified OCR.

Pythia

Test

For testing run our notebook

About

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

Resources

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Force GitHub README to respect dark mode (function() { var style = document.createElement('style'); style.textContent = ' .markdown-body { color-scheme: dark light; } .markdown-body pre { background: #161b22 !important; } .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; } .markdown-body table th, .markdown-body table td { border-color: #30363d !important; } .markdown-body img { background: #0d1117; } .markdown-body blockquote { border-left-color: #8b949e; } .markdown-body hr { border-color: #30363d; } '; document.head.appendChild(style); })(); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - anandroid/pythia: A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) · GitHub
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This is an implementation for vision and language multimodal research developed on top of Pythia

Pythia

Model Implementation

We have proposed few major improvements over Pythia, which is an implementation for the LoRRA model. The dynamic answering space is expanded by adding an Instance segmentation module. We have also replaced the existing OCR with spell correcting OCR and add the spatial features of the OCR. We have also implemented n-gram to the modified OCR.

Pythia

Test

For testing run our notebook

About

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

Resources

Code of conduct

Contributing

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Highlight search terms from Google/DuckDuckGo/Bing referrer (function() { var ref = document.referrer; var terms = []; if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) { var url = new URL(ref); var q = url.searchParams.get('q') || url.searchParams.get('p'); if (q) { terms = q.split(/\s+/).filter(function(t) { return t.length > 2; }); } } if (terms.length === 0) return; var style = document.createElement('style'); style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }'; document.head.appendChild(style); function highlight(node) { if (node.nodeType === 3) { // text node var text = node.textContent; var found = false; terms.forEach(function(term) { var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - anandroid/pythia: A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) · GitHub
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This is an implementation for vision and language multimodal research developed on top of Pythia

Pythia

Model Implementation

We have proposed few major improvements over Pythia, which is an implementation for the LoRRA model. The dynamic answering space is expanded by adding an Instance segmentation module. We have also replaced the existing OCR with spell correcting OCR and add the spatial features of the OCR. We have also implemented n-gram to the modified OCR.

Pythia

Test

For testing run our notebook

About

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

Resources

Code of conduct

Contributing

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ' GitHub - anandroid/pythia: A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) · GitHub
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This is an implementation for vision and language multimodal research developed on top of Pythia

Pythia

Model Implementation

We have proposed few major improvements over Pythia, which is an implementation for the LoRRA model. The dynamic answering space is expanded by adding an Instance segmentation module. We have also replaced the existing OCR with spell correcting OCR and add the spatial features of the OCR. We have also implemented n-gram to the modified OCR.

Pythia

Test

For testing run our notebook

About

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

Resources

Code of conduct

Contributing

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - anandroid/pythia: A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) · GitHub
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Repository files navigation

This is an implementation for vision and language multimodal research developed on top of Pythia

Pythia

Model Implementation

We have proposed few major improvements over Pythia, which is an implementation for the LoRRA model. The dynamic answering space is expanded by adding an Instance segmentation module. We have also replaced the existing OCR with spell correcting OCR and add the spatial features of the OCR. We have also implemented n-gram to the modified OCR.

Pythia

Test

For testing run our notebook

About

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

Resources

Code of conduct

Contributing

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - anandroid/pythia: A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) · GitHub
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This is an implementation for vision and language multimodal research developed on top of Pythia

Pythia

Model Implementation

We have proposed few major improvements over Pythia, which is an implementation for the LoRRA model. The dynamic answering space is expanded by adding an Instance segmentation module. We have also replaced the existing OCR with spell correcting OCR and add the spatial features of the OCR. We have also implemented n-gram to the modified OCR.

Pythia

Test

For testing run our notebook

About

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

Resources

Code of conduct

Contributing

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); GitHub - anandroid/pythia: A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) · GitHub
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This is an implementation for vision and language multimodal research developed on top of Pythia

Pythia

Model Implementation

We have proposed few major improvements over Pythia, which is an implementation for the LoRRA model. The dynamic answering space is expanded by adding an Instance segmentation module. We have also replaced the existing OCR with spell correcting OCR and add the spatial features of the OCR. We have also implemented n-gram to the modified OCR.

Pythia

Test

For testing run our notebook

About

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

Resources

Code of conduct

Contributing

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages