30 citations · 31 across the 12 of their papers we have counts for
12 papers
Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?
Jianyang Xie, Yitian Zhao, Yanda Meng +3
Spatial-temporal graph convolutional networks (ST-GCNs) showcase impressive performance in skeleton-based human action recognition (HAR). However, despite the development of numero…
Phi-4 Technical Report
Marah Abdin, Jyoti Aneja, Harkirat Behl +24
We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training…
A novel approach to differential expression analysis of co-occurrence networks for small-sampled microbiome data
Nandini Gadhia, Michalis Smyrnakis, Po-Yu Liu +6
Graph-based machine learning methods are useful tools in the identification and prediction of variation in genetic data. In particular, the comprehension of phenotypic effects at t…
Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidance
Toan Nguyen, Minh Nhat Vu, Baoru Huang +5
6-DoF grasp detection has been a fundamental and challenging problem in robotic vision. While previous works have focused on ensuring grasp stability, they often do not consider hu…
LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification Task
Khai Le-Duc, Ryan Zhang, Ngoc Son Nguyen +5
Vision-language models have been extensively explored across a wide range of tasks, achieving satisfactory performance; however, their application in medical imaging remains undere…
ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance Segmentation
Minh Tran, Winston Bounsavy, Khoa Vo +3
Amodal Instance Segmentation (AIS) presents a challenging task as it involves predicting both visible and occluded parts of objects within images. Existing AIS methods rely on a bi…