2 citations · 3 across the 21 of their papers we have counts for
21 papers
DS@GT ARC at Touché: Large Language Models for Retrieval-Augmented Debate
Anthony Miyaguchi, Conor Johnston
We extend the DS@GT ARC working-note submission to the Touché 2025 Retrieval-Augmented Debate task. The task has two subtasks: generating the next utterance in a simulated debate,…
DS@GT ARC at AnimalCLEF 2026: Species-Aware Graph Construction for Multi-Species Animal Re-Identification
Evan Sinclair Smith, Anthony Miyaguchi, Snigdha Palamari +1
Automated individual animal re-identification is essential for large-scale biodiversity monitoring; however, field imagery complicates separating identity cues from nuisance variat…
Can Tokens Compete? Token Representations against Supervised CNN Backbones for BirdCLEF+ 2026
Anthony Miyaguchi, Murilo Gustineli, Adrian Cheung
This paper details the DS@GT ARC team's approach to BirdCLEF+ 2026, multi-label detection of animal vocalizations in soundscapes from the Pantanal wetlands. The 2026 edition adds a…
DS@GT at TREC TOT 2025: Bridging Vague Recollection with Fusion Retrieval and Learned Reranking
Wenxin Zhou, Ritesh Mehta, Anthony Miyaguchi
We develop a two-stage retrieval system that combines multiple complementary retrieval methods with a learned reranker and LLM-based reranking, to address the TREC Tip-of-the-Tongu…
DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting
Luciano Araujo Dourado Filho, Almir Moreira da Silva Neto, Anthony Miyaguchi +3
This paper proposes a competitive and computationally efficient approach to probabilistic rainfall nowcasting. A video projector (V-JEPA Vision Transformer) associated to a lightwe…
DS@GT AnimalCLEF: Triplet Learning over ViT Manifolds with Nearest Neighbor Classification for Animal Re-identification
Anthony Miyaguchi, Chandrasekaran Maruthaiyannan, Charles R. Clark
This paper details the DS@GT team's entry for the AnimalCLEF 2025 re-identification challenge. Our key finding is that the effectiveness of post-hoc metric learning is highly conti…
DS@GT at eRisk 2025: From prompts to predictions, benchmarking early depression detection with conversational agent based assessments and temporal attention models
Anthony Miyaguchi, David Guecha, Yuwen Chiu +1
This Working Note summarizes the participation of the DS@GT team in two eRisk 2025 challenges. For the Pilot Task on conversational depression detection with large language-models…
DS@GT at Touché: Large Language Models for Retrieval-Augmented Debate
Anthony Miyaguchi, Conor Johnston, Aaryan Potdar
Large Language Models (LLMs) demonstrate strong conversational abilities. In this Working Paper, we study them in the context of debating in two ways: their ability to perform in a…
DS@GT at LongEval: Evaluating Temporal Performance in Web Search Systems and Topics with Two-Stage Retrieval
Anthony Miyaguchi, Imran Afrulbasha, Aleksandar Pramov
Information Retrieval (IR) models are often trained on static datasets, making them vulnerable to performance degradation as web content evolves. The DS@GT competition team partici…
Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classification
Jason Kahei Tam, Murilo Gustineli, Anthony Miyaguchi
Accurate identification of fungi species presents a unique challenge in computer vision due to fine-grained inter-species variation and high intra-species variation. This paper pre…
Distilling Spectrograms into Tokens: Fast and Lightweight Bioacoustic Classification for BirdCLEF+ 2025
Anthony Miyaguchi, Murilo Gustineli, Adrian Cheung
The BirdCLEF+ 2025 challenge requires classifying 206 species, including birds, mammals, insects, and amphibians, from soundscape recordings under a strict 90-minute CPU-only infer…
Tile-Based ViT Inference with Visual-Cluster Priors for Zero-Shot Multi-Species Plant Identification
Murilo Gustineli, Anthony Miyaguchi, Adrian Cheung +1
We describe DS@GT's second-place solution to the PlantCLEF 2025 challenge on multi-species plant identification in vegetation quadrat images. Our pipeline combines (i) a fine-tuned…
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…
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…
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…
Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024
Anthony Miyaguchi, Patcharapong Aphiwetsa, Mark McDuffie
We explore methods to solve the multi-label classification task posed by the GeoLifeCLEF 2024 competition with the DS@GT team, which aims to predict the presence and absence of pla…
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…
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…
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…
Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall Classification
Anthony Miyaguchi, Nathan Zhong, Murilo Gustineli +1
We present working notes on transfer learning with semi-supervised dataset annotation for the BirdCLEF 2023 competition, focused on identifying African bird species in recorded sou…
Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022
Anthony Miyaguchi, Jiangyue Yu, Bryan Cheungvivatpant +2
We build a classification model for the BirdCLEF 2022 challenge using unsupervised methods. We implement an unsupervised representation of the training dataset using a triplet loss…