Image Captioning On Nocaps In Domain

CIDEr

METEOR

ROUGE-L

SPICE

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	B1	B2	B3	B4	CIDEr	METEOR	ROUGE-L	SPICE	Paper Title	Repository
GIT2, Single Model	88.86	75.86	59.94	41.1	124.18	33.83	63.82	16.36	GIT: A Generative Image-to-text Transformer for Vision and Language
MD	84.03	69.12	51.16	33.15	100.03	30.06	59.67	14.08	-	-
CoCa - Google Brain	87.27	74.29	58.01	39.24	117.9	33.01	63.12	15.49	-	-
7_10-7_40000_predict_test.json	75.31	56.79	37.85	21.91	73.73	26.02	52.44	12.04	-	-
IEDA-LAB	84.4	69.8	51.89	32.86	102.64	30.43	60.07	14.47	-	-
PaLI	-	-	-	-	149.1	-	-	-	PaLI: A Jointly-Scaled Multilingual Language-Image Model
Xinyi	81.61	63.74	43.22	24.82	84.79	27.27	55.03	12.3	-	-
evertyhing	79.58	63.09	43.92	26.07	87.86	27.97	55.88	12.6	-	-
CS395T	72.24	51.88	29.57	14.54	58.93	22.04	49.05	8.91	-	-
cxy_nocaps_training	81.64	63.79	43.43	25.15	85.81	27.25	55.06	12.35	-	-
FudanWYZ	82.91	68.02	50.75	33.59	104.25	31.33	59.67	14.85	-	-
GIT, Single Model	88.55	76.1	60.53	41.65	122.4	33.41	64.02	16.18	GIT: A Generative Image-to-text Transformer for Vision and Language
YX	76.48	58.76	39.28	21.96	69.59	25.08	53.22	10.94	-	-
UpDown	77.68	60.34	41.5	24.57	74.27	26.04	54.42	11.47	-	-
ViTCAP-CIDEr-136.7-ENC-DEC-ViTbfocal10-test-CBS	82.9	68.09	49.73	31.24	96.63	29.37	58.62	13.61	-	-
Single Model	84.64	70.0	52.96	34.66	108.98	31.97	61.01	14.6	SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
coco_all_19	72.76	53.52	34.13	19.45	64.37	23.47	50.53	10.11	-	-
MQ-UpDown-C	78.73	61.63	42.35	25.94	80.19	27.25	55.25	12.38	-	-
Human	76.89	57.3	37.78	21.49	80.61	28.53	53.47	14.99	-	-
GRIT (zero-shot, no VL pretraining, no CBS)	-	-	-	-	105.9	-	-	13.6	GRIT: Faster and Better Image captioning Transformer Using Dual Visual Features

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