6 papers
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
Safal Thapaliya, Jiatan Huang, Chuxu Zhang
Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic content, such as paper abstracts…
Semantic Refinement with LLMs for Graph Representations
Safal Thapaliya, Zehong Wang, Jiazheng Li +3
Graph-structured data exhibit substantial heterogeneity in where their predictive signals originate: in some domains, node-level semantics dominate, while in others, structural pat…
TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models
Rabin Adhikari, Safal Thapaliya, Manish Dhakal +1
Vision-Language Models (VLMs) have shown impressive performance in vision tasks, but adapting them to new domains often requires expensive fine-tuning. Prompt tuning techniques, in…
Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images
Suprim Nakarmi, Sanam Pudasaini, Safal Thapaliya +5
The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, a…
VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks
Manish Dhakal, Rabin Adhikari, Safal Thapaliya +1
Foundation Vision-Language Models (VLMs) trained using large-scale open-domain images and text pairs have recently been adapted to develop Vision-Language Segmentation Models (VLSM…
Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models
Kanchan Poudel, Manish Dhakal, Prasiddha Bhandari +3
Medical image segmentation allows quantifying target structure size and shape, aiding in disease diagnosis, prognosis, surgery planning, and comprehension.Building upon recent adva…