Q6 How many instances are there in total (of each type, if appropriate)?

Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst being consistent throughout the frames.

Despite their demonstrated utility for NLP, multimodal counterfactual examples have been relatively unexplored due to the difficulty of creating paired image-text data with minimal counterfactual changes. To address this challenge, we introduce a scalable framework for automatic generation of counterfactual examples using text-to-image diffusion models.

Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data.

"Rule-of-Thumb" generation, show that our method consistently exceeds previous

The data is collected in Peking University and uses the same data format as SemanticKITTI. To ensure all tasks are well-defined, we formalize consistent and compatible semantic class vocabulary across the above datasets, ensuring there is a one-to-one mapping between all semantic classes. As for ASFDA and ADA settings, we have an additional warm-up stage, i.e., the network is Both source and target data have a batch size of 16. Both training loss and validation loss consistently decrease over time, indicating effective model training. We report mIoU results across existing AL approaches in Table A3.