Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e. g., from LLaMA to CodeLLaMA.
To tackle the challenge, we propose a binary embedding-based retrieval (BEBR) engine equipped with a recurrent binarization algorithm that enables customized bits per dimension.
In this paper, we address this problem by developing a Cross-Modal Attentional Context (CMAC) learning framework, which enables the full exploitation of the context information from both RGB and depth data.
While significant progress has been made in monocular depth estimation with Convolutional Neural Networks (CNNs) extracting absolute features, such as edges and textures, the depth constraint of neighboring pixels, namely relative features, has been mostly ignored by recent methods.
This paper aims at task-oriented action prediction, i. e., predicting a sequence of actions towards accomplishing a specific task under a certain scene, which is a new problem in computer vision research.
Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts.