6 citations · 9 across the 10 of their papers we have counts for
10 papers
Multi-Scale ViT Inference with Habitat-Fit Priors and kNN Retrieval for Multi-Species Plant Identification
Alper Erten, Murilo Gustineli, Adrian Cheung
This paper describes DS@GT ARC's third-place solution to the PlantCLEF 2026 challenge on multi-species plant identification in vegetation quadrat images, where systems must predict…
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…
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…
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…
A survey on recently proposed activation functions for Deep Learning
Murilo Gustineli
Artificial neural networks (ANN), typically referred to as neural networks, are a class of Machine Learning algorithms and have achieved widespread success, having been inspired by…