5 papers
Parameter Efficient Fine-tuning for Domain-specific Gastrointestinal Disease Recognition
Sanjaya Poudel, Nikita Kunwor, Raj Simkhada +3
Despite recent advancements in the field of medical image analysis with the use of pretrained foundation models, the issue of distribution shifts between cross-source images largel…
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…
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…
Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNet
Manish Dhakal, Arman Chhetri, Aman Kumar Gupta +3
This paper presents an end-to-end deep learning model for Automatic Speech Recognition (ASR) that transcribes Nepali speech to text. The model was trained and tested on the OpenSLR…
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…