969 research outputs found
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution
Video Super-Resolution (VSR) aims to recover sequences of high-resolution
(HR) frames from low-resolution (LR) frames. Previous methods mainly utilize
temporally adjacent frames to assist the reconstruction of target frames.
However, in the real world, there is a lot of irrelevant information in
adjacent frames of videos with fast scene switching, these VSR methods cannot
adaptively distinguish and select useful information. In contrast, with a
transformer structure suitable for temporal tasks, we devise a novel adaptive
scenario video super-resolution method. Specifically, we use optical flow to
label the patches in each video frame, only calculate the attention of patches
with the same label. Then select the most relevant label among them to
supplement the spatial-temporal information of the target frame. This design
can directly make the supplementary information come from the same scene as
much as possible. We further propose a cross-scale feature aggregation module
to better handle the scale variation problem. Compared with other video
super-resolution methods, our method not only achieves significant performance
gains on single-scene videos but also has better robustness on cross-scene
datasets
Design of a large dynamic range readout unit for the PSD detector of DAMPE
A large dynamic range is required by the Plastic Scintillator Detector (PSD)
of DArk Matter Paricle Explorer (DAMPE), and a double-dynode readout has been
developed. To verify this design, a prototype detector module has been
constructed and tested with cosmic rays and heavy ion beams. The results match
with the estimation and the readout unit could easily cover the required
dynamic range
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Vision-based vehicle detection approaches achieve incredible success in
recent years with the development of deep convolutional neural network (CNN).
However, existing CNN based algorithms suffer from the problem that the
convolutional features are scale-sensitive in object detection task but it is
common that traffic images and videos contain vehicles with a large variance of
scales. In this paper, we delve into the source of scale sensitivity, and
reveal two key issues: 1) existing RoI pooling destroys the structure of small
scale objects, 2) the large intra-class distance for a large variance of scales
exceeds the representation capability of a single network. Based on these
findings, we present a scale-insensitive convolutional neural network (SINet)
for fast detecting vehicles with a large variance of scales. First, we present
a context-aware RoI pooling to maintain the contextual information and original
structure of small scale objects. Second, we present a multi-branch decision
network to minimize the intra-class distance of features. These lightweight
techniques bring zero extra time complexity but prominent detection accuracy
improvement. The proposed techniques can be equipped with any deep network
architectures and keep them trained end-to-end. Our SINet achieves
state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on
the KITTI benchmark and a new highway dataset, which contains a large variance
of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems
(T-ITS
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