Intrinsic Consistency Preservation With Adaptively Reliable Samples for Source-Free Domain Adaptation.

Unsupervised domain adaptation (UDA) aims to alleviate the domain shift by transferring knowledge learned from a labeled source dataset to an unlabeled target domain. Although UDA has seen promising progress recently, it requires access to data from both domains, making it problematic in source data-absent scenarios. In this article, we investigate a practical task source-free domain adaptation (SFDA) that alleviates the limitations of the widely studied UDA in simultaneously acquiring source and target data.

SDN: Semantic Decoupling Network for Temporal Language Grounding.

Temporal language grounding (TLG) is one of the most challenging cross-modal video understanding tasks, which aims at retrieving the most relevant video segment from an untrimmed video according to a natural language sentence. The existing methods can be separated into two dominant types: 1) proposal-based and 2) proposal-free methods, where the former conduct contextual interactions and the latter localizes timestamps flexibly. However, the constant-scale candidates in proposal-based methods limit the localization precision and bring extra computational costs.

Adjustable, Skin-Stretching External Fixation Device and Negative Pressure Wound Therapy Application for Infected Full-Thickness Skin Defects: A Case Series Study.

Introduction: Skin defects-especially infected, massive full-thickness defects-can be challenging to manage. Traditionally, defects are repaired using free flaps or musculocutaneous flaps. Many side effects and complications are associated with flaps, however, such as infection, pain, donor site pain, and poor cosmesis.