Early exiting has become a promising approach to im-proving the inference efficiency of
deep networks. By structuring models with multiple classifiers (exits), predictions for" easy" …

Abstract Large Vision-Language Models (LVLMs) rely on vision encoders and Large
Language Models (LLMs) to exhibit remarkable capabilities on various multi-modal tasks in …

Deep neural networks (DNNs) typically have a single exit point that makes predictions by
running the entire stack of neural layers. Since not all inputs require the same amount of …

In this paper, we explore the spatial redundancy in continuous sign language recognition
(CSLR), aiming to improve its efficiency. Despite recent advances in accuracy in CSLR, state …

Significant progress has been achieved in the field of oriented object detection (OOD) in
recent years. Compared to natural images, objects in remote sensing images exhibit …

In recent years there has been a notable increase in the size of commonly used image
classification models. This growth has empowered models to recognize thousands of …

Arbitrarily oriented object detection is one of the most-popular research fields in remote
sensing image processing. In this paper, we propose an approach to predict object angles …

The semantic segmentation of high-resolution remote sensing images (HRRSIs) faces
persistent challenges in handling complex architectural structures and shadow occlusions …

Existing Super-Resolution (SR) methods are typically trained using bicubic degradation
simulations, resulting in unsatisfactory results when applied to remote sensing images that …

Early-exit neural networks (EENNs) facilitate adaptive inference by producing predictions at
multiple stages of the forward pass. In safety-critical applications, these predictions are only …