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Current video generation models usually convert signals indicating appearance and motion received from inputs (e.g., image and text) or latent spaces (e.g., noise vectors) into consecutive frames, fulfilling a stochastic generation process for the uncertainty introduced by latent code sampling. However, this generation pattern lacks deterministic constraints for both appearance and motion, leading to uncontrollable and undesirable outcomes. To this end, we propose a new task called Text-driven Video Prediction (TVP). Taking the first frame and text caption as inputs, this task aims to synthesize the following frames. Specifically, appearance and motion components are provided by the image and caption separately. The key to addressing the TVP task depends on fully exploring the underlying motion information in text descriptions, thus facilitating plausible video generation. In fact, this task is intrinsically a cause-and-effect problem, as the text content directly influences the motion changes of frames. To investigate the capability of text in causal inference for progressive motion information, our TVP framework contains a Text Inference Module (TIM), producing step-wise embeddings to regulate motion inference for subsequent frames. In particular, a refinement mechanism incorporating global motion semantics guarantees coherent generation. Extensive experiments are conducted on Something-Something V2 and Single Moving MNIST datasets. Experimental results demonstrate that our model achieves better results over other baselines, verifying the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

The challenge of cross-domain few-shot learning (CD-FSL) stems from the substantial distribution disparities between target and source domain images, necessitating a model with robust generalization capabilities. In this work, we posit that large-scale pretrained models are pivotal in addressing the CD-FSL task owing to their exceptional representational and generalization prowess. To our knowledge, no existing research comprehensively investigates the utility of large-scale pretrained models in the CD-FSL context. Addressing this gap, our study presents an exhaustive empirical assessment of the Contrastive Language–Image Pre-Training model within the CD-FSL task. We undertake a comparison spanning six dimensions: base model, transfer module, classifier, loss, data augmentation, and training schedule. Furthermore, we establish a straightforward baseline model, E-base, based on our empirical analysis, underscoring the importance of our investigation. Experimental results substantiate the efficacy of our model, yielding a mean gain of 1.2% in 5-way 5-shot evaluations on the BSCD dataset. [ABSTRACT FROM AUTHOR]