"fine-grained classification" Papers
17 papers found
Conference
CrypticBio: A Large Multimodal Dataset for Visually Confusing Species
Georgiana Manolache, Gerard Schouten, Joaquin Vanschoren
Dataset Distillation for Pre-Trained Self-Supervised Vision Models
George Cazenavette, Antonio Torralba, Vincent Sitzmann
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
Guang Liang, Xinyao Liu, Jianxin Wu
Merlin L48 Spectrogram Dataset
Aaron Sun, Subhransu Maji, Grant Van Horn
Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring
Yuyan Chen, Nico Lang, B. Schmidt et al.
Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis
Arpita Chowdhury, Dipanjyoti Paul, Zheda Mai et al.
SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery
Zhenqi He, Yuanpei Liu, Kai Han
SIC: Similarity-Based Interpretable Image Classification with Neural Networks
Tom Nuno Wolf, Emre Kavak, Fabian Bongratz et al.
Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery
Andy V Huynh, Lauren Gillespie, Jael Lopez-Saucedo et al.
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood et al.
Fine-grained Classes and How to Find Them
Matej Grcic, Artyom Gadetsky, Maria Brbic
Learning with Unmasked Tokens Drives Stronger Vision Learners
Taekyung Kim, Sanghyuk Chun, Byeongho Heo et al.
PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers
Ananthu Aniraj, Cassio F. Dantas, Dino Ienco et al.
Rethinking Data Bias: Dataset Copyright Protection via Embedding Class-wise Hidden Bias
Jinhyeok Jang, ByungOk Han, Jaehong Kim et al.
SeiT++: Masked Token Modeling Improves Storage-efficient Training
Minhyun Lee, Song Park, Byeongho Heo et al.
TF-FAS: Twofold-Element Fine-Grained Semantic Guidance for Generalizable Face Anti-Spoofing
Xudong Wang, Ke-Yue Zhang, Taiping Yao et al.
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection
Peng Wu, Xuerong Zhou, Guansong Pang et al.