Abstract Low-Rank Adaptation (LoRA), which updates the dense neural network layers with
pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning …

Multi-task dense prediction plays an important role in the field of computer vision and has an
abundant array of applications. Its main purpose is to reduce the amount of network training …

Recently, scene text detection has received significant attention due to its wide applications.
Accurate detection in complex scenes of multiple scales, orientations, and curvature remains …

Non-iid data is prevalent in real-world federated learning problems. Data heterogeneity can
come in different types in terms of distribution shifts. In this work, we are interested in the …

In the realm of artificial intelligence, simultaneous multi-task learning is crucial, particularly
for dense scene understanding. To address this, we introduce HirMTL, a novel hierarchical …

With the rapid advancement of remote sensing technology, high-resolution multi-modal
imagery is now more widely accessible. Conventional Object detection models are trained …

Detection Transformer (DETR) predicts object bounding boxes and classes from learned
object queries. However, DETR exhibits three major flaws:(1) Only a subset of object queries …

Vision Foundation Models (VFMs) have demonstrated outstanding performance on
numerous downstream tasks. However, due to their inherent representation biases …

Atmospheric science is intricately connected with other fields, eg, geography and
aerospace. Most existing approaches involve training a joint atmospheric and geographic …

Agglomerative models have recently emerged as a powerful approach to training vision
foundation models, leveraging multi-teacher distillation from existing models such as CLIP …