SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 11: Comparisons between per-category performance of Mask2Former-VIS and TMT-VIS. 'MDT' refers to 'Multiple Datasets Training', indicating whether the approach is trained on YTVIS, OVIS, and UVO. 'In Corresponding Dataset' is used to demonstrate whether the category is contained in corresponding dataset.
Categories	Methods	In Corresponding Dataset	MDT	Test Set	AP
YTVIS	OVIS	UVO
Person	Mask2Former-VIS	✓	YTVIS	57.2
TMT-VIS	✓	YTVIS	57.9 (↑ 0.7)
Mask2Former-VIS	✓	✓	✓	✓	YTVIS	59.3
TMT-VIS	✓	✓	✓	✓	YTVIS	60.7 (↑ 1.4)
Duck	Mask2Former-VIS	✓	YTVIS	41.6
TMT-VIS	✓	YTVIS	42.4 (↑ 0.8)
Mask2Former-VIS	✓	✓	YTVIS	38.3
TMT-VIS	✓	✓	YTVIS	43.9 (↑ 5.6)
Monkey	Mask2Former-VIS	✓	YTVIS	24.7
TMT-VIS	✓	YTVIS	26.4 (↑ 1.7)
Mask2Former-VIS	✓	✓	✓	YTVIS	25.6
TMT-VIS	✓	✓	✓	YTVIS	29.1 (↑ 3.5)
Snowboard	Mask2Former-VIS	✓	YTVIS	8.9
TMT-VIS	✓	YTVIS	11.8 (↑ 2.9)
Mask2Former-VIS	✓	✓	✓	YTVIS	10.0
TMT-VIS	✓	✓	✓	YTVIS	14.5 (↑ 4.5)
Table 12: Zero-shot Performance of TMT-VIS with ResNet-50 backbone. The results demonstrate the zero-shot ability of our proposed method. YouTube-VIS 2019 is abbreviated as 'YTVIS'.
Method	Train Set	Test Set	AP	\( AP_{50} \)	\( AP_{75} \)
YTVIS	OVIS	UVO
Mask2Former-VIS	✓	✓	YTVIS	7.1	11.4	8.3
TMT-VIS	✓	✓	YTVIS	11.6 (↑ 4.5)	17.2 (↑ 5.8)	15.0 (↑ 6.7)
Mask2Former-VIS	✓	✓	OVIS	3.7	9.8	5.2
TMT-VIS	✓	✓	OVIS	7.5 (↑ 3.8)	14.1 (↑ 4.3)	8.5 (↑ 3.3)
Figure 4: Visual comparison of our model with Mask2Former-VIS (abbreviated as 'M2F-VIS'). Our TMT-VIS shows better precision in segmenting and tracking small instances with the same taxonomy, such as the giraffes from the first two rows or the cyclists in the last two rows, and TMT-VIS shows better performance).
Figure 5: Visual comparison of our model with Mask2Former-VIS (abbreviated as 'M2F-VIS'). Our TMT-VIS shows better precision in segmenting and tracking instances with occlusions. In the top two rows, TMT-VIS could successfully segment the shark hidden behind the bubbles as well as the diver in the middle, while M2F-VIS fails to segment these instances. In the last two rows, our model successfully segments the person in different poses, while M2F-VIS fails to segment this person's arm in the first frame.