98,543 research outputs found
Protein-Ligand Scoring with Convolutional Neural Networks
Computational approaches to drug discovery can reduce the time and cost
associated with experimental assays and enable the screening of novel
chemotypes. Structure-based drug design methods rely on scoring functions to
rank and predict binding affinities and poses. The ever-expanding amount of
protein-ligand binding and structural data enables the use of deep machine
learning techniques for protein-ligand scoring.
We describe convolutional neural network (CNN) scoring functions that take as
input a comprehensive 3D representation of a protein-ligand interaction. A CNN
scoring function automatically learns the key features of protein-ligand
interactions that correlate with binding. We train and optimize our CNN scoring
functions to discriminate between correct and incorrect binding poses and known
binders and non-binders. We find that our CNN scoring function outperforms the
AutoDock Vina scoring function when ranking poses both for pose prediction and
virtual screening
Tensor singular spectral analysis for 3D feature extraction in hyperspectral images.
Due to the cubic structure of a hyperspectral image (HSI), how to characterize its spectral and spatial properties in three dimensions is challenging. Conventional spectral-spatial methods usually extract spectral and spatial information separately, ignoring their intrinsic correlations. Recently, some 3D feature extraction methods are developed for the extraction of spectral and spatial features simultaneously, although they rely on local spatial-spectral regions and thus ignore the global spectral similarity and spatial consistency. Meanwhile, some of these methods contain huge model parameters which require a large number of training samples. In this paper, a novel Tensor Singular Spectral Analysis (TensorSSA) method is proposed to extract global and low-rank features of HSI. In TensorSSA, an adaptive embedding operation is first proposed to construct a trajectory tensor corresponding to the entire HSI, which takes full advantage of the spatial similarity and improves the adequate representation of the global low-rank properties of the HSI. Moreover, the obtained trajectory tensor, which contains the global and local spatial and spectral information of the HSI, is decomposed by the Tensor singular value decomposition (t-SVD) to explore its low-rank intrinsic features. Finally, the efficacy of the extracted features is evaluated using the accuracy of image classification with a support vector machine (SVM) classifier. Experimental results on three publicly available datasets have fully demonstrated the superiority of the proposed TensorSSA over a few state-of-the-art 2D/3D feature extraction and deep learning algorithms, even with a limited number of training samples
Bidirectional Propagation for Cross-Modal 3D Object Detection
Recent works have revealed the superiority of feature-level fusion for
cross-modal 3D object detection, where fine-grained feature propagation from 2D
image pixels to 3D LiDAR points has been widely adopted for performance
improvement. Still, the potential of heterogeneous feature propagation between
2D and 3D domains has not been fully explored. In this paper, in contrast to
existing pixel-to-point feature propagation, we investigate an opposite
point-to-pixel direction, allowing point-wise features to flow inversely into
the 2D image branch. Thus, when jointly optimizing the 2D and 3D streams, the
gradients back-propagated from the 2D image branch can boost the representation
ability of the 3D backbone network working on LiDAR point clouds. Then,
combining pixel-to-point and point-to-pixel information flow mechanisms, we
construct an bidirectional feature propagation framework, dubbed BiProDet. In
addition to the architectural design, we also propose normalized local
coordinates map estimation, a new 2D auxiliary task for the training of the 2D
image branch, which facilitates learning local spatial-aware features from the
image modality and implicitly enhances the overall 3D detection performance.
Extensive experiments and ablation studies validate the effectiveness of our
method. Notably, we rank on the highly competitive
KITTI benchmark on the cyclist class by the time of submission. The source code
is available at https://github.com/Eaphan/BiProDet.Comment: Accepted by ICLR2023. Code is avaliable at
https://github.com/Eaphan/BiProDe
Semantics-Aligned Representation Learning for Person Re-identification
Person re-identification (reID) aims to match person images to retrieve the
ones with the same identity. This is a challenging task, as the images to be
matched are generally semantically misaligned due to the diversity of human
poses and capture viewpoints, incompleteness of the visible bodies (due to
occlusion), etc. In this paper, we propose a framework that drives the reID
network to learn semantics-aligned feature representation through delicate
supervision designs. Specifically, we build a Semantics Aligning Network (SAN)
which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder
(SA-Dec) for reconstructing/regressing the densely semantics aligned full
texture image. We jointly train the SAN under the supervisions of person
re-identification and aligned texture generation. Moreover, at the decoder,
besides the reconstruction loss, we add Triplet ReID constraints over the
feature maps as the perceptual losses. The decoder is discarded in the
inference and thus our scheme is computationally efficient. Ablation studies
demonstrate the effectiveness of our design. We achieve the state-of-the-art
performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the
partial person reID dataset Partial REID. Code for our proposed method is
available at:
https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20),
code has been release
Pooling Faces: Template based Face Recognition with Pooled Face Images
We propose a novel approach to template based face recognition. Our dual goal
is to both increase recognition accuracy and reduce the computational and
storage costs of template matching. To do this, we leverage on an approach
which was proven effective in many other domains, but, to our knowledge, never
fully explored for face images: average pooling of face photos. We show how
(and why!) the space of a template's images can be partitioned and then pooled
based on image quality and head pose and the effect this has on accuracy and
template size. We perform extensive tests on the IJB-A and Janus CS2 template
based face identification and verification benchmarks. These show that not only
does our approach outperform published state of the art despite requiring far
fewer cross template comparisons, but also, surprisingly, that image pooling
performs on par with deep feature pooling.Comment: Appeared in the IEEE Computer Society Workshop on Biometrics, IEEE
Conf. on Computer Vision and Pattern Recognition (CVPR), June, 201
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