SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Setting	In-the-wild	Illumination	Pose	All
mAP↑	top-1↑	ARI↑	mAP↑	top-1↑	ARI↑	mAP↑	top-1↑	ARI↑	mAP↑	top-1↑	ARI↑
LPIPS	9.0	0.0	-	11.5	0.0	-	47.1	57.5	-	39.9	52.0	-
SSIM	14.8	0.1	-	11.7	0.0	-	57.6	72.0	-	40.3	60.2	-
CLIPScore	84.0	82.5	-0.3	85.0	85.9	-0.9	87.1	97.4	8.6	84.9	96.9	3.5
CLIPScore + Crop	86.0	86.6	2.8	86.5	89.9	2.8	86.2	94.3	5.43	84.9	94.3	3.3
DreamSim	84.2	81.7	-0.9	84.2	85.3	-2.1	87.6	96.0	6.4	84.3	95.5	-0.1
DreamSim + Crop	86.4	86.2	5.0	86.7	90.7	0.7	86.6	95.3	5.6	84.6	95.4	2.1
DinoV2	85.5	84.5	2.4	88.4	91.8	5.4	87.5	97.5	9.0	85.8	97.1	6.5
DinoV2 + Crop	88.3	90.3	8.2	88.0	93.0	3.9	87.6	96.4	7.0	86.4	96.5	5.3
DinoV1	83.1	80.4	-1.0	84.2	86.6	-0.1	87.6	97.1	6.1	84.3	96.8	2.9
DinoV1 + Crop	86.1	85.0	5.0	86.3	90.5	0.3	86.7	95.9	6.1	84.8	95.7	4.0
Unicom	85.0	84.4	2.3	84.9	86.0	-0.6	87.8	96.3	7.5	66.6	94.8	9.7
Unicom + Crop	86.5	88.0	4.2	86.6	89.7	3.9	86.1	93.4	5.9	84.7	93.6	2.5
FFA Crop-Img (ours)	87.1	88.5	3.3	88.1	92.1	5.4	86.8	95.5	7.6	85.6	95.6	6.6
FFA Crop-Feat (ours)	86.6	86.7	3.0	89.0	93.1	5.6	87.9	96.9	7.6	86.7	96.9	7.4
Table 7: Performance of image similarity metrics on differentiating object instances from our extended 10 instance object dataset. We evaluate them by image retrieval, measured by top-1 accuracy  $(\%)$  and mean average precision (mAP,  $\%$ ). For embedding-based approach including ours and CLIPScore, we also evaluate it by clustering measured by the adjusted Rand index (ARI) which is multiplied by 100 in the table with 100 indicating perfect clustering, 0 indicating a random clustering and -50 indicating worst-case clustering.