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Analyzing the Performance of Multilayer Neural Networks for Object
  Recognition

Analyzing the Performance of Multilayer Neural Networks for Object Recognition

7 July 2014
Pulkit Agrawal
Ross B. Girshick
Jitendra Malik
    SSL
ArXivPDFHTML

Papers citing "Analyzing the Performance of Multilayer Neural Networks for Object Recognition"

50 / 153 papers shown
Title
On the Exploration of Convolutional Fusion Networks for Visual
  Recognition
On the Exploration of Convolutional Fusion Networks for Visual Recognition
Y. Liu
Yanming Guo
M. Lew
30
23
0
16 Nov 2016
Learning Sparse, Distributed Representations using the Hebbian Principle
Learning Sparse, Distributed Representations using the Hebbian Principle
Aseem Wadhwa
Upamanyu Madhow
12
9
0
14 Nov 2016
Can Ground Truth Label Propagation from Video help Semantic
  Segmentation?
Can Ground Truth Label Propagation from Video help Semantic Segmentation?
Siva Karthik Mustikovela
M. Yang
Carsten Rother
24
33
0
03 Oct 2016
A scalable convolutional neural network for task-specified scenarios via
  knowledge distillation
A scalable convolutional neural network for task-specified scenarios via knowledge distillation
Mengnan Shi
F. Qin
QiXiang Ye
Zhenjun Han
Jianbin Jiao
16
5
0
19 Sep 2016
Associating Grasp Configurations with Hierarchical Features in
  Convolutional Neural Networks
Associating Grasp Configurations with Hierarchical Features in Convolutional Neural Networks
L. Ku
Erik Learned-Miller
R. Grupen
14
12
0
13 Sep 2016
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for
  Object Skeleton Extraction in Natural Images
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
Wei Shen
Kai Zhao
Yuan Jiang
Yan Wang
X. Bai
Alan Yuille
19
99
0
13 Sep 2016
What makes ImageNet good for transfer learning?
What makes ImageNet good for transfer learning?
Minyoung Huh
Pulkit Agrawal
Alexei A. Efros
OOD
SSeg
VLM
SSL
62
671
0
30 Aug 2016
Do semantic parts emerge in Convolutional Neural Networks?
Do semantic parts emerge in Convolutional Neural Networks?
Abel Gonzalez-Garcia
Davide Modolo
V. Ferrari
158
113
0
13 Jul 2016
Learning without Forgetting
Learning without Forgetting
Zhizhong Li
Derek Hoiem
CLL
OOD
SSL
101
4,310
0
29 Jun 2016
Saliency Driven Object recognition in egocentric videos with deep CNN
Saliency Driven Object recognition in egocentric videos with deep CNN
P. Roman
J. Benois-Pineau
J. Domenger
F. Paclet
D. Cattaert
A. Rugy
44
32
0
23 Jun 2016
Low-shot Visual Recognition by Shrinking and Hallucinating Features
Low-shot Visual Recognition by Shrinking and Hallucinating Features
Bharath Hariharan
Ross B. Girshick
VLM
30
49
0
09 Jun 2016
Improving Image Captioning by Concept-based Sentence Reranking
Improving Image Captioning by Concept-based Sentence Reranking
Xirong Li
Qin Jin
17
5
0
03 May 2016
Learning Models for Actions and Person-Object Interactions with Transfer
  to Question Answering
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering
Arun Mallya
Svetlana Lazebnik
39
119
0
16 Apr 2016
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment
  Prediction
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
Victor Campos
Brendan Jou
Xavier Giró-i-Nieto
19
187
0
12 Apr 2016
Object Skeleton Extraction in Natural Images by Fusing Scale-associated
  Deep Side Outputs
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
Wei Shen
Kai Zhao
Yuan Jiang
Yan Wang
Zhijiang Zhang
X. Bai
12
104
0
31 Mar 2016
Unsupervised Learning of Visual Representations by Solving Jigsaw
  Puzzles
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
M. Noroozi
Paolo Favaro
SSL
13
2,958
0
30 Mar 2016
Instance-sensitive Fully Convolutional Networks
Instance-sensitive Fully Convolutional Networks
Jifeng Dai
Kaiming He
Yi Li
Shaoqing Ren
Jian Sun
SSeg
20
397
0
29 Mar 2016
Cascaded Subpatch Networks for Effective CNNs
Cascaded Subpatch Networks for Effective CNNs
Xiaoheng Jiang
Yanwei Pang
Manli Sun
Xuelong Li
26
39
0
01 Mar 2016
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN
  Architectures, Dataset Characteristics and Transfer Learning
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin
H. Roth
Mingchen Gao
Le Lu
Ziyue Xu
Isabella Nogues
Jianhua Yao
D. Mollura
Ronald M. Summers
11
4,573
0
10 Feb 2016
How Far are We from Solving Pedestrian Detection?
How Far are We from Solving Pedestrian Detection?
Shanshan Zhang
Rodrigo Benenson
Mohamed Omran
J. Hosang
Bernt Schiele
11
491
0
03 Feb 2016
Understanding Deep Convolutional Networks
Understanding Deep Convolutional Networks
S. Mallat
FAtt
AI4CE
21
639
0
19 Jan 2016
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and
  Recurrent Neural Networks
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Sean Bell
C. L. Zitnick
Kavita Bala
Ross B. Girshick
ObjD
41
1,205
0
14 Dec 2015
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views
Francisco Massa
Bryan C. Russell
Mathieu Aubry
3DV
ObjD
9
101
0
08 Dec 2015
Unsupervised learning of object semantic parts from internal states of
  CNNs by population encoding
Unsupervised learning of object semantic parts from internal states of CNNs by population encoding
Jianyu Wang
Zhishuai Zhang
Cihang Xie
Vittal Premachandran
Alan Yuille
10
43
0
21 Nov 2015
Adjustable Bounded Rectifiers: Towards Deep Binary Representations
Adjustable Bounded Rectifiers: Towards Deep Binary Representations
Zhirong Wu
Dahua Lin
Xiaoou Tang
MQ
16
14
0
19 Nov 2015
Deep Learning for Tactile Understanding From Visual and Haptic Data
Deep Learning for Tactile Understanding From Visual and Haptic Data
Yang Gao
Lisa Anne Hendricks
Katherine J. Kuchenbecker
Trevor Darrell
17
242
0
19 Nov 2015
Particular object retrieval with integral max-pooling of CNN activations
Particular object retrieval with integral max-pooling of CNN activations
Giorgos Tolias
R. Sicre
Hervé Jégou
19
966
0
18 Nov 2015
Cross-convolutional-layer Pooling for Image Recognition
Cross-convolutional-layer Pooling for Image Recognition
Lingqiao Liu
Chunhua Shen
Anton Van Den Hengel
30
81
0
04 Oct 2015
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
Alexander Krull
Eric Brachmann
Frank Michel
M. Yang
Stefan Gumhold
Carsten Rother
SSL
16
199
0
19 Aug 2015
What is Holding Back Convnets for Detection?
What is Holding Back Convnets for Detection?
Bojan Pepik
Rodrigo Benenson
Tobias Ritschel
Bernt Schiele
ObjD
24
64
0
12 Aug 2015
Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View
  Invariance
Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance
A. Bakry
Mohamed Elhoseiny
Tarek El-Gaaly
Ahmed Elgammal
28
29
0
09 Aug 2015
Deep Learning for Single-View Instance Recognition
Deep Learning for Single-View Instance Recognition
David Held
Sebastian Thrun
Silvio Savarese
11
30
0
29 Jul 2015
Understanding Intra-Class Knowledge Inside CNN
Understanding Intra-Class Knowledge Inside CNN
D. Wei
Bolei Zhou
Antonio Torrabla
William Freeman
FAtt
SSL
14
88
0
09 Jul 2015
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
Donggeun Yoo
Sunggyun Park
Joon-Young Lee
Anthony S. Paek
In So Kweon
29
160
0
25 Jun 2015
Mining Mid-level Visual Patterns with Deep CNN Activations
Mining Mid-level Visual Patterns with Deep CNN Activations
Yao Li
Lingqiao Liu
Chunhua Shen
Anton Van Den Hengel
23
54
0
21 Jun 2015
Inverting Visual Representations with Convolutional Networks
Inverting Visual Representations with Convolutional Networks
Alexey Dosovitskiy
Thomas Brox
SSL
FAtt
39
661
0
09 Jun 2015
Understanding deep features with computer-generated imagery
Understanding deep features with computer-generated imagery
Mathieu Aubry
Bryan C. Russell
24
148
0
03 Jun 2015
Unsupervised Visual Representation Learning by Context Prediction
Unsupervised Visual Representation Learning by Context Prediction
Carl Doersch
Abhinav Gupta
Alexei A. Efros
DRL
SSL
22
2,771
0
19 May 2015
Learning to See by Moving
Learning to See by Moving
Pulkit Agrawal
João Carreira
Jitendra Malik
SSL
34
552
0
07 May 2015
Webly Supervised Learning of Convolutional Networks
Webly Supervised Learning of Convolutional Networks
Xinlei Chen
Abhinav Gupta
SSL
44
370
0
07 May 2015
A Deeper Look at Dataset Bias
A Deeper Look at Dataset Bias
Tatiana Tommasi
Novi Patricia
Barbara Caputo
Tinne Tuytelaars
23
328
0
06 May 2015
Mid-level Elements for Object Detection
Mid-level Elements for Object Detection
Aayush Bansal
Abhinav Shrivastava
Carl Doersch
Abhinav Gupta
ObjD
14
12
0
27 Apr 2015
Object Detection Networks on Convolutional Feature Maps
Object Detection Networks on Convolutional Feature Maps
Shaoqing Ren
Kaiming He
Ross B. Girshick
Xinming Zhang
Jian Sun
ObjD
27
407
0
23 Apr 2015
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks
  for Semantic Segmentation
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
Jifeng Dai
Kaiming He
Jian Sun
21
1,038
0
05 Mar 2015
Freehand Sketch Recognition Using Deep Features
Freehand Sketch Recognition Using Deep Features
Ravi Kiran Sarvadevabhatla
R. Venkatesh Babu
26
36
0
01 Feb 2015
Taking a Deeper Look at Pedestrians
Taking a Deeper Look at Pedestrians
J. Hosang
Mohamed Omran
Rodrigo Benenson
Bernt Schiele
28
350
0
23 Jan 2015
DeepHash: Getting Regularization, Depth and Fine-Tuning Right
DeepHash: Getting Regularization, Depth and Fine-Tuning Right
Jie Lin
Olivier Morère
V. Chandrasekhar
A. Veillard
Hanlin Goh
16
34
0
20 Jan 2015
Deep Convolutional Neural Networks for Action Recognition Using Depth
  Map Sequences
Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences
Pichao Wang
W. Li
Zhimin Gao
Jing Zhang
Chang-Fu Tang
P. Ogunbona
25
46
0
20 Jan 2015
Object Detectors Emerge in Deep Scene CNNs
Object Detectors Emerge in Deep Scene CNNs
Bolei Zhou
A. Khosla
Àgata Lapedriza
A. Oliva
Antonio Torralba
ObjD
21
1,278
0
22 Dec 2014
Deeply learned face representations are sparse, selective, and robust
Deeply learned face representations are sparse, selective, and robust
Yi Sun
Xiaogang Wang
Xiaoou Tang
CVBM
250
921
0
03 Dec 2014
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