4,411 research outputs found
L-CAD: Language-based Colorization with Any-level Descriptions using Diffusion Priors
Language-based colorization produces plausible and visually pleasing colors
under the guidance of user-friendly natural language descriptions. Previous
methods implicitly assume that users provide comprehensive color descriptions
for most of the objects in the image, which leads to suboptimal performance. In
this paper, we propose a unified model to perform language-based colorization
with any-level descriptions. We leverage the pretrained cross-modality
generative model for its robust language understanding and rich color priors to
handle the inherent ambiguity of any-level descriptions. We further design
modules to align with input conditions to preserve local spatial structures and
prevent the ghosting effect. With the proposed novel sampling strategy, our
model achieves instance-aware colorization in diverse and complex scenarios.
Extensive experimental results demonstrate our advantages of effectively
handling any-level descriptions and outperforming both language-based and
automatic colorization methods. The code and pretrained models are available
at: https://github.com/changzheng123/L-CAD
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Aerial Image Segmentation is a particular semantic segmentation problem and
has several challenging characteristics that general semantic segmentation does
not have. There are two critical issues: The one is an extremely
foreground-background imbalanced distribution, and the other is multiple small
objects along with the complex background. Such problems make the recent dense
affinity context modeling perform poorly even compared with baselines due to
over-introduced background context. To handle these problems, we propose a
point-wise affinity propagation module based on the Feature Pyramid Network
(FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse
affinity map is generated upon selected points between the adjacent features,
which reduces the noise introduced by the background while keeping efficiency.
In particular, we design a dual point matcher to select points from the salient
area and object boundaries, respectively. Experimental results on three
different aerial segmentation datasets suggest that the proposed method is more
effective and efficient than state-of-the-art general semantic segmentation
methods. Especially, our methods achieve the best speed and accuracy trade-off
on three aerial benchmarks. Further experiments on three general semantic
segmentation datasets prove the generality of our method. Code will be provided
in (https: //github.com/lxtGH/PFSegNets).Comment: accepted by CVPR202
A new decay mode of higher charmonium
We calculate the partial decay width of the
excited vector charmonium states around 4.6 GeV with the quark pair creation
model. We find that the partial decay width of the
mode can reach up to several MeV for . In contrast, the
partial decay width of the states
is less than one MeV. If the enhancement reported by the Belle
Collaboration in invariant-mass distribution is the
same structure as , the resonance is most likely to be a
-wave charmonium state.Comment: 8 pages, 4 figure
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