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20162026
most citedLearning Metrics from Teachers: Compact Networks for Image Embedding

8 citations · 12 across the 4 of their papers we have counts for

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cs.CV2026

ImageRAGTurbo: Towards One-step Text-to-Image Generation with Retrieval-Augmented Diffusion Models

Peijie Qiu, Hariharan Ramshankar, Arnau Ramisa +4

Diffusion models have emerged as the leading approach for text-to-image generation. However, their iterative sampling process, which gradually morphs random noise into coherent ima…

cs.CV2022

On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection

Ambar Pal, Arnau Ramisa, Amit Kumar K C +1

State-of-the-art object detectors are fast and accurate, but they require a large amount of well annotated training data to obtain good performance. However, obtaining a large amou…

cs.CV20204 cited

T-VSE: Transformer-Based Visual Semantic Embedding

Muhammet Bastan, Arnau Ramisa, Mehmet Tek

Transformer models have recently achieved impressive performance on NLP tasks, owing to new algorithms for self-supervised pre-training on very large text corpora. In contrast, rec…

cs.CV2019

Orderless Recurrent Models for Multi-label Classification

Vacit Oguz Yazici, Abel Gonzalez-Garcia, Arnau Ramisa +2

Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered f…

cs.CV20198 cited

Learning Metrics from Teachers: Compact Networks for Image Embedding

Lu Yu, Vacit Oguz Yazici, Xialei Liu +3

Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to…

cs.CV2018

Visually-Aware Personalized Recommendation using Interpretable Image Representations

Charles Packer, Julian McAuley, Arnau Ramisa

Visually-aware recommender systems use visual signals present in the underlying data to model the visual characteristics of items and users' preferences towards them. In the domain…