From 5f89f0fc6bd6ff35f2ff973055dc8ab808e91719 Mon Sep 17 00:00:00 2001
From: David Chan <DavidMChan@users.noreply.github.com>
Date: Tue, 23 Jul 2024 08:30:58 -0700
Subject: [PATCH 1/2] Update index.html

Add description information to head
---
 index.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/index.html b/index.html
index 1e3ad23..9bd0a6f 100644
--- a/index.html
+++ b/index.html
@@ -7,7 +7,7 @@
     <!-- Replace the content tag with appropriate information -->
     <meta name="description" content="SVisual Haystacks: Answering Harder Questions About Sets of Images">
     <meta property="og:title" content="Visual Haystacks" />
-    <meta property="og:description" content="SOCIAL MEDIA DESCRIPTION TAG TAG" />
+    <meta property="og:description" content="Recent advancements in Large Multimodal Models (LMMs) have made significant progress in the field of single-image visual question answering. However, these models face substantial challenges when tasked with queries that span extensive collections of images, similar to real-world scenarios like searching through large photo albums, finding specific information across the internet, or monitoring environmental changes through satellite imagery. This paper explores the task of Multi-Image Visual Question Answering (MIQA): given a large set of images and a natural language query, the task is to generate a relevant and grounded response. We propose a new public benchmark, dubbed "Visual Haystacks (VHs)," specifically designed to evaluate LMMs' capabilities in visual retrieval and reasoning over sets of unrelated images, where we perform comprehensive evaluations demonstrating that even robust closed-source models struggle significantly. Towards addressing these shortcomings, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), a novel retrieval/QA framework tailored for LMMs that confronts the challenges of MIQA with marked efficiency and accuracy improvements over baseline methods. Our evaluation shows that MIRAGE surpasses closed-source GPT-4o models by up to 11% on the VHs benchmark and offers up to 3.4x improvements in efficiency over text-focused multi-stage approaches." />
     <meta property="og:url" content="http://visual-haystacks.github.io" />
     <!-- Path to banner image, should be in the path listed below. Optimal dimensions are 1200X630-->
     <meta property="og:image" content="/static/images/VHs_logo.png" />
@@ -16,7 +16,7 @@
 
 
     <meta name="twitter:title" content="Visual Haystacks: Answering Harder Questions About Sets of Images">
-    <meta name="twitter:description" content="TWITTER BANNER DESCRIPTION META TAG">
+    <meta name="twitter:description" content="Recent advancements in Large Multimodal Models (LMMs) have made significant progress in the field of single-image visual question answering. However, these models face substantial challenges when tasked with queries that span extensive collections of images, similar to real-world scenarios like searching through large photo albums, finding specific information across the internet, or monitoring environmental changes through satellite imagery. This paper explores the task of Multi-Image Visual Question Answering (MIQA): given a large set of images and a natural language query, the task is to generate a relevant and grounded response. We propose a new public benchmark, dubbed "Visual Haystacks (VHs)," specifically designed to evaluate LMMs' capabilities in visual retrieval and reasoning over sets of unrelated images, where we perform comprehensive evaluations demonstrating that even robust closed-source models struggle significantly. Towards addressing these shortcomings, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), a novel retrieval/QA framework tailored for LMMs that confronts the challenges of MIQA with marked efficiency and accuracy improvements over baseline methods. Our evaluation shows that MIRAGE surpasses closed-source GPT-4o models by up to 11% on the VHs benchmark and offers up to 3.4x improvements in efficiency over text-focused multi-stage approaches.">
     <!-- Path to banner image, should be in the path listed below. Optimal dimensions are 1200X600-->
     <meta name="twitter:image" content="static/images/VHs_logo.png">
     <meta name="twitter:card" content="Visual Haystack Project Logo: A cartoon character photo sitting on top of a haystack of images.">

From 9d8ec98eba7b8dc8debb380d42c69785d3d13696 Mon Sep 17 00:00:00 2001
From: David Chan <DavidMChan@users.noreply.github.com>
Date: Wed, 14 Aug 2024 12:24:22 -0700
Subject: [PATCH 2/2] Update index.html

---
 index.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/index.html b/index.html
index 9bd0a6f..55fe4a3 100644
--- a/index.html
+++ b/index.html
@@ -7,7 +7,7 @@
     <!-- Replace the content tag with appropriate information -->
     <meta name="description" content="SVisual Haystacks: Answering Harder Questions About Sets of Images">
     <meta property="og:title" content="Visual Haystacks" />
-    <meta property="og:description" content="Recent advancements in Large Multimodal Models (LMMs) have made significant progress in the field of single-image visual question answering. However, these models face substantial challenges when tasked with queries that span extensive collections of images, similar to real-world scenarios like searching through large photo albums, finding specific information across the internet, or monitoring environmental changes through satellite imagery. This paper explores the task of Multi-Image Visual Question Answering (MIQA): given a large set of images and a natural language query, the task is to generate a relevant and grounded response. We propose a new public benchmark, dubbed "Visual Haystacks (VHs)," specifically designed to evaluate LMMs' capabilities in visual retrieval and reasoning over sets of unrelated images, where we perform comprehensive evaluations demonstrating that even robust closed-source models struggle significantly. Towards addressing these shortcomings, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), a novel retrieval/QA framework tailored for LMMs that confronts the challenges of MIQA with marked efficiency and accuracy improvements over baseline methods. Our evaluation shows that MIRAGE surpasses closed-source GPT-4o models by up to 11% on the VHs benchmark and offers up to 3.4x improvements in efficiency over text-focused multi-stage approaches." />
+    <meta property="og:description" content="Recent advancements in Large Multimodal Models (LMMs) have made significant progress in the field of single-image visual question answering. However, these models face substantial challenges when tasked with queries that span extensive collections of images, similar to real-world scenarios like searching through large photo albums, finding specific information across the internet, or monitoring environmental changes through satellite imagery. This paper explores the task of Multi-Image Visual Question Answering (MIQA): given a large set of images and a natural language query, the task is to generate a relevant and grounded response. We propose a new public benchmark, dubbed Visual Haystacks (VHs), specifically designed to evaluate LMMs capabilities in visual retrieval and reasoning over sets of unrelated images, where we perform comprehensive evaluations demonstrating that even robust closed-source models struggle significantly. Towards addressing these shortcomings, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), a novel retrieval/QA framework tailored for LMMs that confronts the challenges of MIQA with marked efficiency and accuracy improvements over baseline methods. Our evaluation shows that MIRAGE surpasses closed-source GPT-4o models by up to 11% on the VHs benchmark and offers up to 3.4x improvements in efficiency over text-focused multi-stage approaches." />
     <meta property="og:url" content="http://visual-haystacks.github.io" />
     <!-- Path to banner image, should be in the path listed below. Optimal dimensions are 1200X630-->
     <meta property="og:image" content="/static/images/VHs_logo.png" />
@@ -16,7 +16,7 @@
 
 
     <meta name="twitter:title" content="Visual Haystacks: Answering Harder Questions About Sets of Images">
-    <meta name="twitter:description" content="Recent advancements in Large Multimodal Models (LMMs) have made significant progress in the field of single-image visual question answering. However, these models face substantial challenges when tasked with queries that span extensive collections of images, similar to real-world scenarios like searching through large photo albums, finding specific information across the internet, or monitoring environmental changes through satellite imagery. This paper explores the task of Multi-Image Visual Question Answering (MIQA): given a large set of images and a natural language query, the task is to generate a relevant and grounded response. We propose a new public benchmark, dubbed "Visual Haystacks (VHs)," specifically designed to evaluate LMMs' capabilities in visual retrieval and reasoning over sets of unrelated images, where we perform comprehensive evaluations demonstrating that even robust closed-source models struggle significantly. Towards addressing these shortcomings, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), a novel retrieval/QA framework tailored for LMMs that confronts the challenges of MIQA with marked efficiency and accuracy improvements over baseline methods. Our evaluation shows that MIRAGE surpasses closed-source GPT-4o models by up to 11% on the VHs benchmark and offers up to 3.4x improvements in efficiency over text-focused multi-stage approaches.">
+    <meta name="twitter:description" content="Recent advancements in Large Multimodal Models (LMMs) have made significant progress in the field of single-image visual question answering. However, these models face substantial challenges when tasked with queries that span extensive collections of images, similar to real-world scenarios like searching through large photo albums, finding specific information across the internet, or monitoring environmental changes through satellite imagery. This paper explores the task of Multi-Image Visual Question Answering (MIQA): given a large set of images and a natural language query, the task is to generate a relevant and grounded response. We propose a new public benchmark, dubbed Visual Haystacks (VHs), specifically designed to evaluate LMMs capabilities in visual retrieval and reasoning over sets of unrelated images, where we perform comprehensive evaluations demonstrating that even robust closed-source models struggle significantly. Towards addressing these shortcomings, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), a novel retrieval/QA framework tailored for LMMs that confronts the challenges of MIQA with marked efficiency and accuracy improvements over baseline methods. Our evaluation shows that MIRAGE surpasses closed-source GPT-4o models by up to 11% on the VHs benchmark and offers up to 3.4x improvements in efficiency over text-focused multi-stage approaches." />
     <!-- Path to banner image, should be in the path listed below. Optimal dimensions are 1200X600-->
     <meta name="twitter:image" content="static/images/VHs_logo.png">
     <meta name="twitter:card" content="Visual Haystack Project Logo: A cartoon character photo sitting on top of a haystack of images.">