4 accepted papers
Turning the multi-round vanilla Federated Learning into one-shot FL (OFL) significantly reduces the communication burden and makes a big leap toward practical deployment. However, this work empirically and theoretically unravels that existing OFL falls into a garbage (inconsistent one-shot local mod…
Text-based Person Re-identification (T-ReID), which aims at retrieving a specific pedestrian image from a collection of images via text-based information, has received significant attention. However, previous research has overlooked a challenging yet practical form of T-ReID: dealing with image gall…
Personalized Federated Learning (FL) handles the data heterogeneous problem by tailoring local models for each distributed data owner. Previous studies first train a highly-adaptable global model and then transfer it for personalization. However, the additional training aggravates burden of resource…
Hyper-scale surveillance cameras have enabled seamless target (e.g., vehicles, individuals) tracking in urban scenarios, significantly improving everyday safety and emergency response capacity. However, practically tracking multiple targets among cameras would incur prohibitive huge computation cost…