Computer vision-based vehicle detection techniques are widely used in real-world
applications. However, most of these techniques aim to detect only single-view vehicles, and …

Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a
model to downstream tasks by learning a minimal set of new adaptation parameters while …

Visual Prompt Tuning (VPT) techniques have gained prominence for their capacity to adapt
pre-trained Vision Transformers (ViTs) to downstream visual tasks using specialized …

Fine-grained remote sensing object detection aims at precisely locating objects and
determining the fine-level categories. This task is exceptionally challenging due to the …

As a prominent parameter-efficient fine-tuning technique in NLP, prompt tuning is being
explored its potential in computer vision. Typical methods for visual prompt tuning follow the …

(1) Background: Evidence-based policymaking requires data about the local population's
socioeconomic status (SES) at detailed geographical level, however, such information is …

Due to increasing interest in adapting models on resource-constrained edges, parameter-
efficient transfer learning has been widely explored. Among various methods, Visual Prompt …

Neural network classifiers have become the de-facto choice for current" pre-train then fine-
tune" paradigms of visual classification. In this paper, we investigate k-Nearest-Neighbor (k …

Abstract Vision Transformers (ViTs) have become one of the dominant architectures in
computer vision, and pre-trained ViT models are commonly adapted to new tasks via fine …

It is crucial that image datasets for computer vision are representative and contain accurate
demographic information to ensure their robustness and fairness, especially for smaller …