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+imagen_museum
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diff --git a/index.html b/index.html
index 7a14206..8854e07 100644
--- a/index.html
+++ b/index.html
@@ -209,7 +209,7 @@
How effective is VIEScore with current state-of-the-art M
But how well can Multimodal large language models access different tasks of conditional Image generation? We reported that the best model GPT4v’s performance is significantly better than the open-source models. Most open-source MLLMs failed to adapt to our VieScore except LLaVA.
-

+
Table 1: Correlations across all tasks with different backbone models. We highlight the highest correlation numbers in green. Visit our paper for insights and challenges in VIEScore.
@@ -256,10 +256,22 @@
How is Traditional Metrics correlating with human compare
-0.0114 |
-0.0881 |
+
+ VIEScore(GPT-4o0shot) |
+ 0.4989 |
+ 0.2495 |
+ 0.3928 |
+
+
+ VIEScore(GPT-4o1shot) |
+ 0.5124 |
+ 0.0336 |
+ 0.4042 |
+
VIEScore(GPT-4v0shot) |
- 0.4885 |
- 0.2379 |
+ 0.4885 |
+ 0.2379 |
0.4614 |
@@ -296,10 +308,22 @@ How is Traditional Metrics correlating with human compare
-0.0694 |
- VIEScore(GPT-4v0shot) |
- 0.4508 |
- 0.2859 |
- 0.4069 |
+ VIEScore(GPT-4o0shot) |
+ 0.5421 |
+ 0.3469 |
+ 0.4769 |
+
+
+ VIEScore(GPT-4o1shot) |
+ 0.5246 |
+ 0.1272 |
+ 0.4432 |
+
+
+ VIEScore(GPT-4v0shot) |
+ 0.4508 |
+ 0.2859 |
+ 0.4069 |
VIEScore(GPT-4v1shot) |
@@ -335,10 +359,22 @@ How is Traditional Metrics correlating with human compare
0.1142 |
- VIEScore(GPT-4v0shot) |
- 0.2610 |
- 0.4274 |
- 0.2456 |
+ VIEScore(GPT-4o0shot) |
+ 0.4062 |
+ 0.4863 |
+ 0.3821 |
+
+
+ VIEScore(GPT-4o1shot) |
+ 0.3684 |
+ 0.1939 |
+ 0.3438 |
+
+
+ VIEScore(GPT-4v0shot) |
+ 0.2610 |
+ 0.4274 |
+ 0.2456 |
VIEScore(GPT-4v1shot) |
@@ -368,10 +404,10 @@ How is Traditional Metrics correlating with human compare
0.4653 |
- DINO |
- 0.4160 |
+ DINO |
+ 0.4160 |
0.1206 |
- 0.4246 |
+ 0.4246 |
CLIP-I |
@@ -379,6 +415,18 @@ How is Traditional Metrics correlating with human compare
0.1694 |
0.3058 |
+
+ VIEScore(GPT-4o0shot) |
+ 0.4806 |
+ 0.2576 |
+ 0.4637 |
+
+
+ VIEScore(GPT-4o1shot) |
+ 0.4685 |
+ -0.0171 |
+ 0.4292 |
+
VIEScore(GPT-4v0shot) |
0.3979 |
@@ -388,7 +436,7 @@ How is Traditional Metrics correlating with human compare
VIEScore(GPT-4v1shot) |
0.2757 |
- 0.2261 |
+ 0.2261 |
0.2753 |
@@ -413,10 +461,10 @@ How is Traditional Metrics correlating with human compare
0.4747 |
- DINO |
+ DINO |
0.3022 |
-0.0381 |
- 0.3005 |
+ 0.3005 |
CLIP-I |
@@ -424,10 +472,22 @@ How is Traditional Metrics correlating with human compare
0.1248 |
0.2813 |
+
+ VIEScore(GPT-4o0shot) |
+ 0.4800 |
+ 0.3734 |
+ 0.3268 |
+
+
+ VIEScore(GPT-4o1shot) |
+ 0.3862 |
+ 0.1273 |
+ 0.3268 |
+
VIEScore(GPT-4v0shot) |
- 0.3274 |
- 0.2960 |
+ 0.3274 |
+ 0.2960 |
0.1507 |
@@ -470,10 +530,22 @@ How is Traditional Metrics correlating with human compare
0.1498 |
- VIEScore(GPT-4v0shot) |
- 0.3209 |
+ VIEScore(GPT-4o0shot) |
+ 0.4516 |
+ 0.2751 |
+ 0.4136 |
+
+
+ VIEScore(GPT-4o1shot) |
+ 0.4120 |
+ -0.0141 |
+ 0.3523 |
+
+
+ VIEScore(GPT-4v0shot) |
+ 0.3209 |
0.3025 |
- 0.3346 |
+ 0.3346 |
VIEScore(GPT-4v1shot) |
@@ -508,17 +580,29 @@ How is Traditional Metrics correlating with human compare
0.4204 |
0.4133 |
+
+ VIEScore(GPT-4o0shot) |
+ 0.4972 |
+ 0.4892 |
+ 0.5439 |
+
+
+ VIEScore(GPT-4o1shot) |
+ 0.5544 |
+ 0.3699 |
+ 0.5238 |
+
VIEScore(GPT-4v0shot) |
- 0.4360 |
- 0.4975 |
+ 0.4360 |
+ 0.4975 |
0.3999 |
- VIEScore(GPT-4v1shot) |
+ VIEScore(GPT-4v1shot) |
0.3892 |
0.4132 |
- 0.4237 |
+ 0.4237 |
VIEScore(LLaVA0shot) |
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