3 papers
cs.CV2025
TernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge
Shu-Hao Zhang, Wei-Cheng Tang, Chen Wu +5
Recent years have witnessed an increasing interest in image-text contrastive modeling, exemplified by models such as Contrastive Language-Image Pretraining (CLIP). In this paper, w…
cs.CL2024
Efficient Ternary Weight Embedding Model: Bridging Scalability and Performance
Jiayi Chen, Chen Wu, Shaoqun Zhang +3
Embedding models have become essential tools in both natural language processing and computer vision, enabling efficient semantic search, recommendation, clustering, and more. Howe…
eess.IV2024
Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and compu…