Then, MP A denoises the learned prompts through an auto-encoding process and aligns them by maximizing the agreement of all the reconstructed prompts.

Part of this work was done during Kang Xu's internship at Shanghai Artificial Intelligence Laboratory.

SwapPrompt employs a dual prompts paradigm, i.e., an online prompt and a target prompt

Di Lin is the corresponding author of this paper.

More importantly, we employ a generative model for synthetic samples to simulate the intervention of various non-causal effects, thereby minimizing their impact on hash codes for domain invariance. Comprehensive experiments conducted on benchmark datasets confirm the superior performance of our proposed IDEA compared to a variety of competitive baselines.

In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only a few reference images. Inspired by the way human brains acquire knowledge in new domains, we present an innovative generator structure called Domain Re-Modulation (DoRM) .

Hard Norm Alignment loss (HNA): apply the HNA loss (Eq. HMDB, which demonstrates the effectiveness of our model. First, we drop feature from a specific spatial group. Method UCF HMDB STDN-T -1 59.2 STDN-T -2 58.1 STDN-T -3 59.4 STDN-T -4 58.9 Full STDN 60.2 Second, we drop feature from a space scale. In our main manuscript, we conduct all experiments based on ResNet-50.

However, existing video classification models rely on the i.i.d.