226 research outputs found
An Analysis of Colour Metaphors of Emotions in Jimmy’s Picture Book-Turn Lift, Turn Right
The analysis in this paper aims to find colour metaphors in the artist Jimmy Liao’s picture book Turn Lift, Turn right, and to use the pictures in this book to establish the relations between colour and emotions. This paper selects seven pictures of indoor scenes from this picture book, and brings forth an illustration by observing that different colours can be used as metaphor to convey different emotions. The results clearly indicate that the author Jimmy choose a large number of warm colour (such as yellow, red and orange) to present the positive emotions (like energy, passion and strength), while the cool colour (like blue and gray) to give out negative feelings (such as fear of loss, sorrow, heartbreaking)
Study of the interactions between glycoproteins and polysaccharides in cell walls of cotton suspension culture
Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur
Rendering novel view images is highly desirable for many applications.
Despite recent progress, it remains challenging to render high-fidelity and
view-consistent novel views of large-scale scenes from in-the-wild images with
inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid
neural rendering model that makes image-based representation and neural 3D
representation join forces to render high-quality, view-consistent images.
Besides, images captured in the wild inevitably contain artifacts, such as
motion blur, which deteriorates the quality of rendered images. Accordingly, we
propose strategies to simulate blur effects on the rendered images to mitigate
the negative influence of blurriness images and reduce their importance during
training based on precomputed quality-aware weights. Extensive experiments on
real and synthetic data demonstrate our model surpasses state-of-the-art
point-based methods for novel view synthesis. The code is available at
https://daipengwa.github.io/Hybrid-Rendering-ProjectPage
MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds
3D semantic segmentation on multi-scan large-scale point clouds plays an
important role in autonomous systems. Unlike the single-scan-based semantic
segmentation task, this task requires distinguishing the motion states of
points in addition to their semantic categories. However, methods designed for
single-scan-based segmentation tasks perform poorly on the multi-scan task due
to the lacking of an effective way to integrate temporal information. We
propose MarS3D, a plug-and-play motion-aware module for semantic segmentation
on multi-scan 3D point clouds. This module can be flexibly combined with
single-scan models to allow them to have multi-scan perception abilities. The
model encompasses two key designs: the Cross-Frame Feature Embedding module for
enriching representation learning and the Motion-Aware Feature Learning module
for enhancing motion awareness. Extensive experiments show that MarS3D can
improve the performance of the baseline model by a large margin. The code is
available at https://github.com/CVMI-Lab/MarS3D
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition
The Transformer architecture model, based on self-attention and multi-head
attention, has achieved remarkable success in offline end-to-end Automatic
Speech Recognition (ASR). However, self-attention and multi-head attention
cannot be easily applied for streaming or online ASR. For self-attention in
Transformer ASR, the softmax normalization function-based attention mechanism
makes it impossible to highlight important speech information. For multi-head
attention in Transformer ASR, it is not easy to model monotonic alignments in
different heads. To overcome these two limits, we integrate sparse attention
and monotonic attention into Transformer-based ASR. The sparse mechanism
introduces a learned sparsity scheme to enable each self-attention structure to
fit the corresponding head better. The monotonic attention deploys
regularization to prune redundant heads for the multi-head attention structure.
The experiments show that our method can effectively improve the attention
mechanism on widely used benchmarks of speech recognition.Comment: Accepted to DSAA 202
Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video
Synthesizing realistic videos according to a given speech is still an open
challenge. Previous works have been plagued by issues such as inaccurate lip
shape generation and poor image quality. The key reason is that only motions
and appearances on limited facial areas (e.g., lip area) are mainly driven by
the input speech. Therefore, directly learning a mapping function from speech
to the entire head image is prone to ambiguity, particularly when using a short
video for training. We thus propose a decomposition-synthesis-composition
framework named Speech to Lip (Speech2Lip) that disentangles speech-sensitive
and speech-insensitive motion/appearance to facilitate effective learning from
limited training data, resulting in the generation of natural-looking videos.
First, given a fixed head pose (i.e., canonical space), we present a
speech-driven implicit model for lip image generation which concentrates on
learning speech-sensitive motion and appearance. Next, to model the major
speech-insensitive motion (i.e., head movement), we introduce a geometry-aware
mutual explicit mapping (GAMEM) module that establishes geometric mappings
between different head poses. This allows us to paste generated lip images at
the canonical space onto head images with arbitrary poses and synthesize
talking videos with natural head movements. In addition, a Blend-Net and a
contrastive sync loss are introduced to enhance the overall synthesis
performance. Quantitative and qualitative results on three benchmarks
demonstrate that our model can be trained by a video of just a few minutes in
length and achieve state-of-the-art performance in both visual quality and
speech-visual synchronization. Code: https://github.com/CVMI-Lab/Speech2Lip
Early patterning of cloned mouse embryos contributes to post-implantation development
AbstractSeveral research groups have suggested that the embryonic–abembryonic (Em–Ab) axis in the mouse can be predicted by the first cleavage plane of the early embryo. Currently, it is not known whether this early patterning occurs in cloned embryos produced by nuclear transfer and whether it affects development to term. In this work, the relationship between the first cleavage plane and the Em–Ab axis was determined by the labeling of one blastomere in cloned mouse embryos at the 2-cell stage, followed by ex-vivo tracking until the blastocyst stage. The results demonstrate that approximately half of the cloned blastocysts had an Em–Ab axis perpendicular to the initial cleavage plane of the 2-cell stage. These embryos were classified as “orthogonal” and the remainder as “deviant”. Additionally, we report here that cloned embryos were significantly more often orthogonal than their naturally fertilized counterparts and overexpressed Sox2. Orthogonal cloned embryos demonstrated a higher rate of post-implantation embryonic development than deviant embryos, but cloned pups did not all survive. These results reveal that the angular relationship between the Em–Ab axis and the first cleavage plane can influence later development and they support the hypothesis that proper early patterning of mammalian embryos is required after nuclear transfer
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