most citedMulti-Label Plant Species Classification with Self-Supervised Vision Transformers

2 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Annotation Techniques for Judo Combat Phase Classification from Tournament Footage

Anthony Miyaguchi, Jed Moutahir, Tanmay Sutar

This paper presents a semi-supervised approach to extracting and analyzing combat phases in judo tournaments using live-streamed footage. The objective is to automate the annotatio…

cs.CL2024

DS@GT eRisk 2024: Sentence Transformers for Social Media Risk Assessment

David Guecha, Aaryan Potdar, Anthony Miyaguchi

We present working notes for DS@GT team in the eRisk 2024 for Tasks 1 and 3. We propose a ranking system for Task 1 that predicts symptoms of depression based on the Beck Depressio…

cs.CV2024

Fine-Grained Classification for Poisonous Fungi Identification with Transfer Learning

Christopher Chiu, Maximilian Heil, Teresa Kim +1

FungiCLEF 2024 addresses the fine-grained visual categorization (FGVC) of fungi species, with a focus on identifying poisonous species. This task is challenging due to the size and…

cs.CV20242 cited

Multi-Label Plant Species Classification with Self-Supervised Vision Transformers

Murilo Gustineli, Anthony Miyaguchi, Ian Stalter

We present a transfer learning approach using a self-supervised Vision Transformer (DINOv2) for the PlantCLEF 2024 competition, focusing on the multi-label plant species classifica…

cs.SD2024

Transfer Learning with Pseudo Multi-Label Birdcall Classification for DS@GT BirdCLEF 2024

Anthony Miyaguchi, Adrian Cheung, Murilo Gustineli +1

We present working notes for the DS@GT team on transfer learning with pseudo multi-label birdcall classification for the BirdCLEF 2024 competition, focused on identifying Indian bi…

cs.CV2024

Transfer Learning with Self-Supervised Vision Transformers for Snake Identification

Anthony Miyaguchi, Murilo Gustineli, Austin Fischer +1

We present our approach for the SnakeCLEF 2024 competition to predict snake species from images. We explore and use Meta's DINOv2 vision transformer model for feature extraction to…