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20172026
most citedFCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

24 citations · 25 across the 4 of their papers we have counts for

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cs.CV2026

How to rewrite the stars: Mapping your orchard over time through constellations of fruits

Gonçalo P. Matos, Carlos Santiago, João P. Costeira +2

Following crop growth through the vegetative cycle allows farmers to predict fruit setting and yield in early stages, but it is a laborious and non-scalable task if performed by a…

cs.CV2020

Unsupervised Vehicle Counting via Multiple Camera Domain Adaptation

Luca Ciampi, Carlos Santiago, Joao Paulo Costeira +2

Monitoring vehicle flows in cities is crucial to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and assess the flow…

cs.CV2019

Pose Guided Attention for Multi-label Fashion Image Classification

Beatriz Quintino Ferreira, João P. Costeira, Ricardo G. Sousa +2

We propose a compact framework with guided attention for multi-label classification in the fashion domain. Our visual semantic attention model (VSAM) is supervised by automatic pos…

cs.CV20171 cited

Subspace Segmentation by Successive Approximations: A Method for Low-Rank and High-Rank Data with Missing Entries

João Carvalho, Manuel Marques, João P. Costeira

We propose a method to reconstruct and cluster incomplete high-dimensional data lying in a union of low-dimensional subspaces. Exploring the sparse representation model, we jointly…

cs.CV201724 cited

FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

Shanghang Zhang, Guanhang Wu, João P. Costeira +1

In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (citycams). Citycam videos have low r…