22,347 research outputs found
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
Nowadays, deep learning based methods have demonstrated impressive
performance on ideal super-resolution (SR) datasets, but most of these methods
incur dramatically performance drops when directly applied in real-world SR
reconstruction tasks with unpredictable blur kernels. To tackle this issue,
blind SR methods are proposed to improve the visual results on random blur
kernels, which causes unsatisfactory reconstruction effects on ideal
low-resolution images similarly. In this paper, we propose a double-win
framework for ideal and blind SR task, named S2R, including a light-weight
transformer-based SR model (S2R transformer) and a novel coarse-to-fine
training strategy, which can achieve excellent visual results on both ideal and
random fuzzy conditions. On algorithm level, S2R transformer smartly combines
some efficient and light-weight blocks to enhance the representation ability of
extracted features with relatively low number of parameters. For training
strategy, a coarse-level learning process is firstly performed to improve the
generalization of the network with the help of a large-scale external dataset,
and then, a fast fine-tune process is developed to transfer the pre-trained
model to real-world SR tasks by mining the internal features of the image.
Experimental results show that the proposed S2R outperforms other single-image
SR models in ideal SR condition with only 578K parameters. Meanwhile, it can
achieve better visual results than regular blind SR models in blind fuzzy
conditions with only 10 gradient updates, which improve convergence speed by
300 times, significantly accelerating the transfer-learning process in
real-world situations
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Face Super-Resolution (SR) is a domain-specific super-resolution problem. The
specific facial prior knowledge could be leveraged for better super-resolving
face images. We present a novel deep end-to-end trainable Face Super-Resolution
Network (FSRNet), which makes full use of the geometry prior, i.e., facial
landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR)
face images without well-aligned requirement. Specifically, we first construct
a coarse SR network to recover a coarse high-resolution (HR) image. Then, the
coarse HR image is sent to two branches: a fine SR encoder and a prior
information estimation network, which extracts the image features, and
estimates landmark heatmaps/parsing maps respectively. Both image features and
prior information are sent to a fine SR decoder to recover the HR image. To
further generate realistic faces, we propose the Face Super-Resolution
Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss
into FSRNet. Moreover, we introduce two related tasks, face alignment and
parsing, as the new evaluation metrics for face SR, which address the
inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark
experiments show that FSRNet and FSRGAN significantly outperforms state of the
arts for very LR face SR, both quantitatively and qualitatively. Code will be
made available upon publication.Comment: Chen and Tai contributed equally to this pape
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
A Reverse Hierarchy Model for Predicting Eye Fixations
A number of psychological and physiological evidences suggest that early
visual attention works in a coarse-to-fine way, which lays a basis for the
reverse hierarchy theory (RHT). This theory states that attention propagates
from the top level of the visual hierarchy that processes gist and abstract
information of input, to the bottom level that processes local details.
Inspired by the theory, we develop a computational model for saliency detection
in images. First, the original image is downsampled to different scales to
constitute a pyramid. Then, saliency on each layer is obtained by image
super-resolution reconstruction from the layer above, which is defined as
unpredictability from this coarse-to-fine reconstruction. Finally, saliency on
each layer of the pyramid is fused into stochastic fixations through a
probabilistic model, where attention initiates from the top layer and
propagates downward through the pyramid. Extensive experiments on two standard
eye-tracking datasets show that the proposed method can achieve competitive
results with state-of-the-art models.Comment: CVPR 2014, 27th IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). CVPR 201
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