TY  - JOUR
A1  - Long, Xiang
A1  - de Melo, Gerard
A1  - He, Dongliang
A1  - Li, Fu
A1  - Chi, Zhizhen
A1  - Wen, Shilei
A1  - Gan, Chuang
T1  - Purely attention based local feature integration for video classification
JF  - IEEE Transactions on Pattern Analysis and Machine Intelligence
N2  - Recently, substantial research effort has focused on how to apply CNNs or RNNs to better capture temporal patterns in videos, so as to improve the accuracy of video classification. In this paper, we investigate the potential of a purely attention based local feature integration. Accounting for the characteristics of such features in video classification, we first propose Basic Attention Clusters (BAC), which concatenates the output of multiple attention units applied in parallel, and introduce a shifting operation to capture more diverse signals. Experiments show that BAC can achieve excellent results on multiple datasets. However, BAC treats all feature channels as an indivisible whole, which is suboptimal for achieving a finer-grained local feature integration over the channel dimension. Additionally, it treats the entire local feature sequence as an unordered set, thus ignoring the sequential relationships. To improve over BAC, we further propose the channel pyramid attention schema by splitting features into sub-features at multiple scales for coarse-to-fine sub-feature interaction modeling, and propose the temporal pyramid attention schema by dividing the feature sequences into ordered sub-sequences of multiple lengths to account for the sequential order. Our final model pyramidxpyramid attention clusters (PPAC) combines both channel pyramid attention and temporal pyramid attention to focus on the most important sub-features, while also preserving the temporal information of the video. We demonstrate the effectiveness of PPAC on seven real-world video classification datasets. Our model achieves competitive results across all of these, showing that our proposed framework can consistently outperform the existing local feature integration methods across a range of different scenarios.
KW  - Feature extraction
KW  - Convolution
KW  - Computational modeling
KW  - Plugs
KW  - Three-dimensional displays
KW  - Task analysis
KW  - Two dimensional displays
KW  - Video classification
KW  - action recognition
KW  - attention mechanism
KW  - computer vision
KW  - Algorithms
KW  - Neural Networks
KW  - Computer
Y1  - 2020
U6  - https://doi.org/10.1109/TPAMI.2020.3029554
SN  - 0162-8828
SN  - 1939-3539
SN  - 2160-9292
VL  - 44
IS  - 4
SP  - 2140
EP  - 2154
PB  - Inst. of Electr. and Electronics Engineers
CY  - Los Alamitos
ER  - 
TY  - JOUR
A1  - Ran, Niva A.
A1  - Roland, Steffen
A1  - Love, John A.
A1  - Savikhin, Victoria
A1  - Takacs, Christopher J.
A1  - Fu, Yao-Tsung
A1  - Li, Hong
A1  - Coropceanu, Veaceslav
A1  - Liu, Xiaofeng
A1  - Bredas, Jean-Luc
A1  - Bazan, Guillermo C.
A1  - Toney, Michael F.
A1  - Neher, Dieter
A1  - Thuc-Quyen Nguyen, 
T1  - Impact of interfacial molecular orientation on radiative recombination and charge generation efficiency
JF  - Nature Communications
N2  - A long standing question in organic electronics concerns the effects of molecular orientation at donor/acceptor heterojunctions. Given a well-controlled donor/acceptor bilayer system, we uncover the genuine effects of molecular orientation on charge generation and recombination. These effects are studied through the point of view of photovoltaics-however, the results have important implications on the operation of all optoelectronic devices with donor/ acceptor interfaces, such as light emitting diodes and photodetectors. Our findings can be summarized by two points. First, devices with donor molecules face-on to the acceptor interface have a higher charge transfer state energy and less non-radiative recombination, resulting in larger open-circuit voltages and higher radiative efficiencies. Second, devices with donor molecules edge-on to the acceptor interface are more efficient at charge generation, attributed to smaller electronic coupling between the charge transfer states and the ground state, and lower activation energy for charge generation.
Y1  - 2017
U6  - https://doi.org/10.1038/s41467-017-00107-4
SN  - 2041-1723
VL  - 8
PB  - Nature Publ. Group
CY  - London
ER  -