169 research outputs found
Deep Pyramidal Residual Networks
Deep convolutional neural networks (DCNNs) have shown remarkable performance
in image classification tasks in recent years. Generally, deep neural network
architectures are stacks consisting of a large number of convolutional layers,
and they perform downsampling along the spatial dimension via pooling to reduce
memory usage. Concurrently, the feature map dimension (i.e., the number of
channels) is sharply increased at downsampling locations, which is essential to
ensure effective performance because it increases the diversity of high-level
attributes. This also applies to residual networks and is very closely related
to their performance. In this research, instead of sharply increasing the
feature map dimension at units that perform downsampling, we gradually increase
the feature map dimension at all units to involve as many locations as
possible. This design, which is discussed in depth together with our new
insights, has proven to be an effective means of improving generalization
ability. Furthermore, we propose a novel residual unit capable of further
improving the classification accuracy with our new network architecture.
Experiments on benchmark CIFAR-10, CIFAR-100, and ImageNet datasets have shown
that our network architecture has superior generalization ability compared to
the original residual networks. Code is available at
https://github.com/jhkim89/PyramidNet}Comment: Accepted to CVPR 201
Deep Saliency with Encoded Low level Distance Map and High Level Features
Recent advances in saliency detection have utilized deep learning to obtain
high level features to detect salient regions in a scene. These advances have
demonstrated superior results over previous works that utilize hand-crafted low
level features for saliency detection. In this paper, we demonstrate that
hand-crafted features can provide complementary information to enhance
performance of saliency detection that utilizes only high level features. Our
method utilizes both high level and low level features for saliency detection
under a unified deep learning framework. The high level features are extracted
using the VGG-net, and the low level features are compared with other parts of
an image to form a low level distance map. The low level distance map is then
encoded using a convolutional neural network(CNN) with multiple 1X1
convolutional and ReLU layers. We concatenate the encoded low level distance
map and the high level features, and connect them to a fully connected neural
network classifier to evaluate the saliency of a query region. Our experiments
show that our method can further improve the performance of state-of-the-art
deep learning-based saliency detection methods.Comment: Accepted by IEEE Conference on Computer Vision and Pattern
Recognition(CVPR) 2016. Project page:
https://github.com/gylee1103/SaliencyEL
Generative Approach for Probabilistic Human Mesh Recovery using Diffusion Models
This work focuses on the problem of reconstructing a 3D human body mesh from
a given 2D image. Despite the inherent ambiguity of the task of human mesh
recovery, most existing works have adopted a method of regressing a single
output. In contrast, we propose a generative approach framework, called
"Diffusion-based Human Mesh Recovery (Diff-HMR)" that takes advantage of the
denoising diffusion process to account for multiple plausible outcomes. During
the training phase, the SMPL parameters are diffused from ground-truth
parameters to random distribution, and Diff-HMR learns the reverse process of
this diffusion. In the inference phase, the model progressively refines the
given random SMPL parameters into the corresponding parameters that align with
the input image. Diff-HMR, being a generative approach, is capable of
generating diverse results for the same input image as the input noise varies.
We conduct validation experiments, and the results demonstrate that the
proposed framework effectively models the inherent ambiguity of the task of
human mesh recovery in a probabilistic manner. The code is available at
https://github.com/hanbyel0105/Diff-HMRComment: Accepted to ICCV 2023 CV4Metaverse Worksho
- β¦