Search Results for author:
Found 9 papers, 3 papers with code
LLMs in Biomedicine: A study on clinical Named Entity Recognition
Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but encounter distinct challenges in biomedicine due to medical language complexities and data scarcity.
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models
In this paper, we study an interesting hypothesis: can we transfer the in-context learning ability from the language domain to VL domain?
MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust Classifier
We offer a method for one-shot mask-guided image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers.
BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis
Vision-and-language(V&L) models take image and text as input and learn to capture the associations between them.
DefakeHop: A Light-Weight High-Performance Deepfake Detector
A light-weight high-performance Deepfake detection method, called DefakeHop, is proposed in this work.
Successive Subspace Learning: An Overview
Successive Subspace Learning (SSL) offers a light-weight unsupervised feature learning method based on inherent statistical properties of data units (e. g. image pixels and points in point cloud sets).
Low-Resolution Face Recognition In Resource-Constrained Environments
A non-parametric low-resolution face recognition model for resource-constrained environments with limited networking and computing is proposed in this work.
FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method
For gray-scale face images of resolution $32 \times 32$ in the LFW and the CMU Multi-PIE datasets, FaceHop achieves correct gender classification rates of 94. 63% and 95. 12% with model sizes of 16. 9K and 17. 6K parameters, respectively.
PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification
In PixelHop++, one can control the learning model size of fine-granularity, offering a flexible tradeoff between the model size and the classification performance.
