Finally, generate a new brief description mainly based on the originalnull description with attributes information incorporated. Thenull narratives should be similar to the original description.null

Specifically, we resort to large language models (LLM) and large multimodal models (LMM) with our proposed Statics and Dynamics Enhanced Captioning modules to generate diverse fine-grained captions for each video.

Zero-shot learning leverages semantic representations such asattributes or class prototypes to bridge source and target classes.

The history of learning for control has been an exciting back and forth between twobroad classes ofalgorithms: planning andreinforcement learning.

We consider the problem of estimating the mean of a set of vectors, which are stored in a distributed system.

We provide a few error-bound results on its convergence rates. Specially, we prove that theSPIDER-SFO algorithm achieves a gradient computation cost of O min(n1/2 2, 3) to find an -approximate first-order stationary point. In addition, we prove thatSPIDER-SFO nearly matches the algorithmic lower bound for finding stationary point under the gradient Lipschitz assumption in the finite-sum setting.

Images taken between 2013 and 2015 are used as the validation set.

Semi-Supervised Domain Generalization (SSDG) aims to learn a model that is generalizable to an unseen target domain with only a few labels, and most existing SSDG methods assume that unlabeled training and testing samples are all known classes. However, a more realistic scenario is that known classes may be mixed with some unknown classes in unlabeled training and testing data.