Modified Versions of Diving48: Shape and Texture
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'We modify the Diving48 dataset ('RESOUND: Towards Action Recognition without Representation Bias', Li et al., ECCV 2020) into three new domains: two based on shape and one based on texture (following Geirhos et al., ICLR 2019).Note that the Statistical Visual Computing Lab in San Diego (http://www.svcl.ucsd.edu) has the copyright to the Diving48 dataset.Please cite the RESOUND paper, if you are using any data related to the Diving48 dataset, including our modified versions here 'RESOUND: Towards Action Recognition without Representation Bias', Li et al., ECCV 2020.In the shape domains, we blur the background and only maintain the segmented diver(s) (S1), or their bounding boxes (S2). In the texture domain (T), we conversely mask out bounding boxes where the diver(s) are, and only keep the background. The masked boxes are filled wit' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["diving48","texture bias","shape bias","cross-domain robustness","domain shift","action recognition","fine-grained action recognition"]} <|eot_id|>
 Here is the Description of a dataset. Description:'We modify the Diving48 dataset ('RESOUND: Towards Action Recognition without Representation Bias', Li et al., ECCV 2020) into three new domains: two based on shape and one based on texture (following Geirhos et al., ICLR 2019).Note that the Statistical Visual Computing Lab in San Diego (http://www.svcl.ucsd.edu) has the copyright to the Diving48 dataset.Please cite the RESOUND paper, if you are using any data related to the Diving48 dataset, including our modified versions here 'RESOUND: Towards Action Recognition without Representation Bias', Li et al., ECCV 2020.In the shape domains, we blur the background and only maintain the segmented diver(s) (S1), or their bounding boxes (S2). In the texture domain (T), we conversely mask out bounding boxes where the diver(s) are, and only keep the background. The masked boxes are filled wit' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
We modify the Diving48 dataset ('RESOUND: Towards Action Recognition without Representation Bias', Li et al., ECCV 2020) into three new domains: two based on shape and one based on texture (following Geirhos et al., ICLR 2019).Note that the Statistical Visual Computing Lab in San Diego (http://www.svcl.ucsd.edu) has the copyright to the Diving48 dataset.Please cite the RESOUND paper, if you are using any data related to the Diving48 dataset, including our modified versions here 'RESOUND: Towards Action Recognition without Representation Bias', Li et al., ECCV 2020.In the shape domains, we blur the background and only maintain the segmented diver(s) (S1), or their bounding boxes (S2). In the texture domain (T), we conversely mask out bounding boxes where the diver(s) are, and only keep the background. The masked boxes are filled wit
diving48; texture bias; shape bias; cross-domain robustness; domain shift; action recognition; fine-grained action recognition