5 research outputs found
An Expressive Deep Model for Human Action Parsing from A Single Image
This paper aims at one newly raising task in vision and multimedia research:
recognizing human actions from still images. Its main challenges lie in the
large variations in human poses and appearances, as well as the lack of
temporal motion information. Addressing these problems, we propose to develop
an expressive deep model to naturally integrate human layout and surrounding
contexts for higher level action understanding from still images. In
particular, a Deep Belief Net is trained to fuse information from different
noisy sources such as body part detection and object detection. To bridge the
semantic gap, we used manually labeled data to greatly improve the
effectiveness and efficiency of the pre-training and fine-tuning stages of the
DBN training. The resulting framework is shown to be robust to sometimes
unreliable inputs (e.g., imprecise detections of human parts and objects), and
outperforms the state-of-the-art approaches.Comment: 6 pages, 8 figures, ICME 201
Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection
3D object Detection with LiDAR-camera encounters overfitting in algorithm
development which is derived from the violation of some fundamental rules. We
refer to the data annotation in dataset construction for theory complementing
and argue that the regression task prediction should not involve the feature
from the camera branch. By following the cutting-edge perspective of 'Detecting
As Labeling', we propose a novel paradigm dubbed DAL. With the most classical
elementary algorithms, a simple predicting pipeline is constructed by imitating
the data annotation process. Then we train it in the simplest way to minimize
its dependency and strengthen its portability. Though simple in construction
and training, the proposed DAL paradigm not only substantially pushes the
performance boundary but also provides a superior trade-off between speed and
accuracy among all existing methods. With comprehensive superiority, DAL is an
ideal baseline for both future work development and practical deployment. The
code has been released to facilitate future work on
https://github.com/HuangJunJie2017/BEVDet