Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using textdescriptions orreference images, while preserving irrelevant attributes(e.g.,identity,background,cloth).

However, Is asingle comparison between superior and inferior responses sufficient for preference learning in MLLMs? Upon consideration, we find that a multi-level preference framework offers greater benefits for preference learning, primarily due to two main intuitive advantages.

Our experiments demonstrate that MSVMamba is highly competitive, with the MSVMamba-Tinymodel achieving83.0%

However,most existing UDAmethods arelimitedtoone-to-one domain adaptation, which tends tobeinefficient andresourceintensive when faced with multi-target domain transfer tasks.

Reward allocation, also known as the credit assignment problem, has been an important topic ineconomics, engineering, andmachine learning.

Recentstudieshaveshownthat the transferability of adversarial examples exists for CNNs, and the same holds true for ViTs.

In this paper, we establish an upper bound on the return gap between the oracle expert policy and an optimal decision tree policy. This enables us to recast the DT extraction problem into a novel non-euclidean clustering problem over the local observation and action values space of each agent, with action values as cluster labels and the upper bound on the return gap as clustering loss.

Empirically, MiTSformer achieves consistent SOTAonfivemixedtime series analysis tasks, including classification, extrinsic regression,anomalydetection,imputation,andlong-termforecasting.

InOrMo, momentum isincorporated into ASGD byorganizing the gradients in order based on their iteration indexes. We theoretically prove the convergence of OrMo with both constant and delay-adaptive learning rates for non-convexproblems.