SlowGuess/NeurIPS_2023_OCR huggingface dataset:
We also note that there are some limitations of our work. First, this work directly utilizes stable diffusion models trained for inpainting on "laion-aesthetics v2 5+". Though the zero-shot generation performance is good, further improvement might be observed if further trained on room images. Second, we investigate one specific
Original Environment
Figure 5: Qualitative analysis of panorama-trajectory-instruction pairs.
PanoGen Environment
task Vision-and-Language Navigation in this paper, but the proposed method can be potentially used in other embodied tasks like concept learning and grounding in panoramic environments. We will explore other useful and interesting tasks in the future.
H Licenses
We provide the licenses of the existing assets we use in this paper in Table 10.
Table 10: A list of the licenses of the existing assets used in this paper.
Asset	License
Pytorch	BSD-style
Huggingface Transformers	Apache License 2.0
Torchvision	BSD 3-Clause “New” or “Revised” License
Room-to-Room	MIT
Room-for-Room	Apache License 2.0
Cooperative Vision-and-Dialog Navigation	MIT
BLIP-2	BSD 3-Clause “New” or “Revised” License
mPLUG	Apache License 2.0
DUET	N/A
Stable Diffusion	CreativeML Open RAIL-M