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Learning to count with deep object features

Learning to count with deep object features

29 May 2015
Santi Seguí
O. Pujol
Jordi Vitrià
    SSL
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Papers citing "Learning to count with deep object features"

10 / 10 papers shown
Title
Text encoders bottleneck compositionality in contrastive vision-language
  models
Text encoders bottleneck compositionality in contrastive vision-language models
Amita Kamath
Jack Hessel
Kai-Wei Chang
CoGe
CLIP
VLM
35
19
0
24 May 2023
CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection
CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection
D. Gaddam
Jean Lahoud
Fahad Shahbaz Khan
Rao Muhammad Anwer
Hisham Cholakkal
3DPC
31
0
0
13 Sep 2022
Seeing past words: Testing the cross-modal capabilities of pretrained
  V&L models on counting tasks
Seeing past words: Testing the cross-modal capabilities of pretrained V&L models on counting tasks
Letitia Parcalabescu
Albert Gatt
Anette Frank
Iacer Calixto
LRM
33
48
0
22 Dec 2020
Future semantic segmentation of time-lapsed videos with large temporal
  displacement
Future semantic segmentation of time-lapsed videos with large temporal displacement
Talha Ahmad Siddiqui
Samarth Bharadwaj
16
0
0
27 Dec 2018
Textually Enriched Neural Module Networks for Visual Question Answering
Textually Enriched Neural Module Networks for Visual Question Answering
Khyathi Raghavi Chandu
Mary Arpita Pyreddy
Matthieu Felix
N. Joshi
24
6
0
23 Sep 2018
Neural Arithmetic Logic Units
Neural Arithmetic Logic Units
Andrew Trask
Felix Hill
Scott E. Reed
Jack W. Rae
Chris Dyer
Phil Blunsom
NAI
27
204
0
01 Aug 2018
Improving Object Counting with Heatmap Regulation
Improving Object Counting with Heatmap Regulation
Shubhra Aich
Ian Stavness
27
33
0
14 Mar 2018
Classification vs. Regression in Supervised Learning for Single Channel
  Speaker Count Estimation
Classification vs. Regression in Supervised Learning for Single Channel Speaker Count Estimation
Fabian-Robert Stöter
Soumitro Chakrabarty
B. Edler
Emanuel Habets
BDL
31
38
0
12 Dec 2017
Count-ception: Counting by Fully Convolutional Redundant Counting
Count-ception: Counting by Fully Convolutional Redundant Counting
Joseph Paul Cohen
G. Boucher
C. A. Glastonbury
Henry Z. Lo
Yoshua Bengio
25
156
0
25 Mar 2017
A Large Contextual Dataset for Classification, Detection and Counting of
  Cars with Deep Learning
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
T. Nathan Mundhenk
G. Konjevod
W. Sakla
K. Boakye
38
333
0
14 Sep 2016
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