656 research outputs found
Learning to Generate Posters of Scientific Papers
Researchers often summarize their work in the form of posters. Posters
provide a coherent and efficient way to convey core ideas from scientific
papers. Generating a good scientific poster, however, is a complex and time
consuming cognitive task, since such posters need to be readable, informative,
and visually aesthetic. In this paper, for the first time, we study the
challenging problem of learning to generate posters from scientific papers. To
this end, a data-driven framework, that utilizes graphical models, is proposed.
Specifically, given content to display, the key elements of a good poster,
including panel layout and attributes of each panel, are learned and inferred
from data. Then, given inferred layout and attributes, composition of graphical
elements within each panel is synthesized. To learn and validate our model, we
collect and make public a Poster-Paper dataset, which consists of scientific
papers and corresponding posters with exhaustively labelled panels and
attributes. Qualitative and quantitative results indicate the effectiveness of
our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence
(AAAI'16), Phoenix, AZ, 201
Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields
Recently, Neural Radiance Fields (NeRF) have emerged as a potent method for
synthesizing novel views from a dense set of images. Despite its impressive
performance, NeRF is plagued by its necessity for numerous calibrated views and
its accuracy diminishes significantly in a few-shot setting. To address this
challenge, we propose Self-NeRF, a self-evolved NeRF that iteratively refines
the radiance fields with very few number of input views, without incorporating
additional priors. Basically, we train our model under the supervision of
reference and unseen views simultaneously in an iterative procedure. In each
iteration, we label unseen views with the predicted colors or warped pixels
generated by the model from the preceding iteration. However, these expanded
pseudo-views are afflicted by imprecision in color and warping artifacts, which
degrades the performance of NeRF. To alleviate this issue, we construct an
uncertainty-aware NeRF with specialized embeddings. Some techniques such as
cone entropy regularization are further utilized to leverage the pseudo-views
in the most efficient manner. Through experiments under various settings, we
verified that our Self-NeRF is robust to input with uncertainty and surpasses
existing methods when trained on limited training data.Comment: 11 pages, 11 figure
Content-Based Hyperspectral Image Compression Using a Multi-Depth Weighted Map With Dynamic Receptive Field Convolution
In content-based image compression, the importance map guides the bit allocation based on its ability to represent the importance of image contents. In this paper, we improve the representational power of importance map using Squeeze-and-Excitation (SE) block, and propose multi-depth structure to reconstruct non-important channel information at low bit rates. Furthermore, Dynamic Receptive Field convolution (DRFc) is introduced to improve the ability of normal convolution to extract edge information, so as to increase the weight of edge content in the importance map and improve the reconstruction quality of edge regions. Results indicate that our proposed method can extract an importance map with clear edges and fewer artifacts so as to provide obvious advantages for bit rate allocation in content-based image compression. Compared with typical compression methods, our proposed method can greatly improve the performance of Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and spectral angle (SAM) on three public datasets, and can produce a much better visual result with sharp edges and fewer artifacts. As a result, our proposed method reduces the SAM by 42.8% compared to the recently SOTA method to achieve the same low bpp (0.25) on the KAIST dataset
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