7 citations · 8 across the 7 of their papers we have counts for
9 papers
Zero-Shot Spoken Language Understanding with SLAM-ASR: What Do Monolingual ASR-Trained Linear Projectors Enable?
Edwin Arkel Rios, Sneh Sourabh, Jocelyn Zaruma +3
The SLAM-ASR framework connects the features of a frozen speech encoder as input to a frozen LLM decoder to enable speech transcription. In this work we ask whether this training-o…
Smart Insole Human Activity Recognition for Continuous Monitoring in Elderly Care
Edwin Rios, Antony Garcia, Fengpei Yuan +1
Falls in older adults are often preceded by changes in mobility, balance, and postural transitions. This paper presents a wireless smart insole platform and machine-learning workfl…
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…
DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition
Edwin Arkel Rios, Wen-Huang Cheng, Bo-Cheng Lai
In this work we tackle the challenging problem of anime character recognition. Anime, referring to animation produced within Japan and work derived or inspired from it. For this pu…