|
’17 |
Structured learning of binary codes with column generation for optimizing ranking measures [...more] |
IJCV |
G. Lin, F. Liu, C. Shen, J. Wu, H. Shen |
|
’17 |
Exploring context with deep structured models for semantic segmentation |
TPAMI |
G. Lin, C. Shen, A. van den Hengel, I. Reid |
|
’17 |
Image captioning and visual question answering based on attributes and external knowledge |
TPAMI |
Q. Wu, C. Shen, A. van den Hengel, P. Wang, A. Dick |
|
’17 |
Compositional model based Fisher vector coding for image classification |
TPAMI |
L. Liu, P. Wang, C. Shen, L. Wang, A. van den Hengel, C. Wang, H. Shen |
|
’17 |
Cross-convolutional-layer pooling for image recognition |
TPAMI |
L. Liu, C. Shen, A. van den Hengel |
|
’16 |
Mining mid-level visual patterns with deep CNN activations [...more] |
IJCV |
Y. Li, L. Liu, C. Shen, A. van den Hengel |
|
’16 |
Efficient semidefinite branch-and-cut for MAP-MRF inference |
IJCV |
P. Wang, C. Shen, A. van den Hengel, P. Torr |
|
’16 |
Unsupervised feature learning for dense correspondences across scenes [...more] |
IJCV |
C. Zhang, C. Shen, T. Shen |
|
’16 |
Large-scale binary quadratic optimization using semidefinite relaxation and applications |
TPAMI |
P. Wang, C. Shen, A. van den Hengel, P. Torr |
|
’16 |
Learning depth from single monocular images using deep convolutional neural fields [...more] |
TPAMI |
F. Liu, C. Shen, G. Lin, I. Reid |
|
’16 |
Online metric-weighted linear representations for robust visual tracking |
TPAMI |
X. Li, C. Shen, A. Dick, Z. Zhang, Y. Zhuang |
|
’16 |
Pedestrian detection with spatially pooled features and structured ensemble learning [...more] |
TPAMI |
S. Paisitkriangkrai, C. Shen, A. van den Hengel |
|
’16 |
A generalized probabilistic framework for compact codebook creation |
TPAMI |
L. Liu, L. Wang, C. Shen |
|
’15 |
Extrinsic methods for coding and dictionary learning on Grassmann manifolds [...more] |
IJCV |
M. Harandi, R. Hartley, C. Shen, B. Lovell, C. Sanderson |
|
’15 |
Supervised hashing using graph cuts and boosted decision trees [...more] |
TPAMI |
G. Lin, C. Shen, A. van den Hengel |
|
’14 |
StructBoost: Boosting methods for predicting structured output variables |
TPAMI |
C. Shen, G. Lin, A. van den Hengel |
|
’14 |
A hierarchical word-merging algorithm with class separability measure |
TPAMI |
L. Wang, L. Zhou, C. Shen, L. Liu, H. Liu |
|
’13 |
Training effective node classifiers for cascade classification [...more] |
IJCV |
C. Shen, P. Wang, S. Paisitkriangkrai, A. van den Hengel |
|
’13 |
Incremental learning of 3D-DCT compact representations for robust visual tracking [...more] |
TPAMI |
X. Li, A. Dick, C. Shen, A. van den Hengel, H. Wang |
|
’12 |
Positive semidefinite metric learning using boosting-like algorithms [...more] |
JMLR |
C. Shen, J. Kim, L. Wang, A. van den Hengel |
|
’12 |
UBoost: Boosting with the Universum |
TPAMI |
C. Shen, P. Wang, F. Shen, H. Wang |
|
’10 |
On the dual formulation of boosting algorithms |
TPAMI |
C. Shen, H. Li |
|
’07 |
Adaptive object tracking based on an effective appearance filter |
TPAMI |
H. Wang, D. Suter, K. Schindler, C. Shen |