6 papers · 1 filter
A Large-Scale Study on the Accuracy vs Cost Trade-offs of Training and Evaluation Settings in Fine-Grained Image Recognition
Edwin Arkel Rios, Augusto Christian Surya, Oswin Gosal +5
Prior work on fine-grained image recognition (FGIR) has established the importance of the backbone selection, but has neglected the accuracy-vs-cost trade-offs under different trai…
How to Choose Your Teacher for Fine Grained Image Recognition
Oswin Gosal, Edwin Arkel Rios, Augusto Christian Surya +3
Fine-grained image recognition classifies subcategories such as bird species or car models. While state-of-the-art (SOTA) models are accurate, they are often too resource-intensive…
Fine-Grained Image Recognition from Scratch with Teacher-Guided Data Augmentation
Edwin Arkel Rios, Fernando Mikael, Oswin Gosal +4
Fine-grained image recognition (FGIR) aims to distinguish visually similar sub-categories within a broader class, such as identifying bird species. While most existing FGIR methods…
Cross-Layer Cache Aggregation for Token Reduction in Ultra-Fine-Grained Image Recognition
Edwin Arkel Rios, Jansen Christopher Yuanda, Vincent Leon Ghanz +3
Ultra-fine-grained image recognition (UFGIR) is a challenging task that involves classifying images within a macro-category. While traditional FGIR deals with classifying different…
Down-Sampling Inter-Layer Adapter for Parameter and Computation Efficient Ultra-Fine-Grained Image Recognition
Edwin Arkel Rios, Femiloye Oyerinde, Min-Chun Hu +1
Ultra-fine-grained image recognition (UFGIR) categorizes objects with extremely small differences between classes, such as distinguishing between cultivars within the same species,…
Global-Local Similarity for Efficient Fine-Grained Image Recognition with Vision Transformers
Edwin Arkel Rios, Min-Chun Hu, Bo-Cheng Lai
Fine-grained recognition involves the classification of images from subordinate macro-categories, and it is challenging due to small inter-class differences. To overcome this, most…