263 research outputs found
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
Recently, substantial research effort has focused on how to apply CNNs or
RNNs to better extract temporal patterns from videos, so as to improve the
accuracy of video classification. In this paper, however, we show that temporal
information, especially longer-term patterns, may not be necessary to achieve
competitive results on common video classification datasets. We investigate the
potential of a purely attention based local feature integration. Accounting for
the characteristics of such features in video classification, we propose a
local feature integration framework based on attention clusters, and introduce
a shifting operation to capture more diverse signals. We carefully analyze and
compare the effect of different attention mechanisms, cluster sizes, and the
use of the shifting operation, and also investigate the combination of
attention clusters for multimodal integration. We demonstrate the effectiveness
of our framework on three real-world video classification datasets. Our model
achieves competitive results across all of these. In particular, on the
large-scale Kinetics dataset, our framework obtains an excellent single model
accuracy of 79.4% in terms of the top-1 and 94.0% in terms of the top-5
accuracy on the validation set. The attention clusters are the backbone of our
winner solution at ActivityNet Kinetics Challenge 2017. Code and models will be
released soon.Comment: The backbone of the winner solution at ActivityNet Kinetics Challenge
201
EAST: An Efficient and Accurate Scene Text Detector
Previous approaches for scene text detection have already achieved promising
performances across various benchmarks. However, they usually fall short when
dealing with challenging scenarios, even when equipped with deep neural network
models, because the overall performance is determined by the interplay of
multiple stages and components in the pipelines. In this work, we propose a
simple yet powerful pipeline that yields fast and accurate text detection in
natural scenes. The pipeline directly predicts words or text lines of arbitrary
orientations and quadrilateral shapes in full images, eliminating unnecessary
intermediate steps (e.g., candidate aggregation and word partitioning), with a
single neural network. The simplicity of our pipeline allows concentrating
efforts on designing loss functions and neural network architecture.
Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500
demonstrate that the proposed algorithm significantly outperforms
state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR
2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps
at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3
BigTrans: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages
Large language models (LLMs) demonstrate promising translation performance
among various natural languages. However, many LLMs especially the open-sourced
ones, such as BLOOM and LLaMA, are English-dominant and support only dozens of
natural languages, making the potential of LLMs on language translation less
explored. In this work, we present BigTrans which adapts LLaMA that covers only
20 languages and enhances it with multilingual translation capability on more
than 100 languages. BigTrans is built upon LLaMA-13B and it is optimized in
three steps. First, we continue training LLaMA with massive Chinese monolingual
data. Second, we continue training the model with a large-scale parallel
dataset that covers 102 natural languages. Third, we instruct-tune the
foundation model with multilingual translation instructions, leading to our
BigTrans model. The preliminary experiments on multilingual translation show
that BigTrans performs comparably with ChatGPT and Google Translate in many
languages and even outperforms ChatGPT in 8 language pairs. We release the
BigTrans model and hope it can advance the research progress.Comment: 12 pages, 4 figures. Our model is available at
https://github.com/ZNLP/BigTran
Conditional ablation of HDAC3 in islet beta cells results in glucose intolerance and enhanced susceptibility to STZ-induced diabetes
Histone deacetylases (HDACs) are enzymes that regulate gene expression by modifying chromatin structure through removal of acetyl groups from target histones or non-histone proteins. Previous in vitro studies suggest that HDACs may be novel pharmacological targets in immune-mediated islet β-cell destruction. However, the role of specific HDAC in islet β-cell development and function remain unclear. Here, we generated a conditional islet β-cells specific HDAC3 deletion mouse model to determine the consequences of HDAC3 depletion on islet β-cell differentiation, maintenance and function. Islet morphology, insulin secretion, glucose tolerance, and multiple low-dose streptozotocin (STZ)-induced diabetes incidence were evaluated and compared between HDAC3 knockout and wild type littermate controls. Mice with β-cell-specific HDAC3 deletion displayed decreased pancreatic insulin content, disrupted glucose-stimulated insulin secretion, with intermittent spontaneous diabetes and dramatically enhanced susceptibility to STZ-induced diabetes. Furthermore, islet β-cell line, MIN6 cells with siRNA-mediated HDAC3 silence, showed decreased insulin gene transcription, which was mediated, at least partially, through the upregulation of suppressors of cytokine signaling 3 (SOCS3). These results indicate the critical role of HDAC3 in normal β-cell differentiation, maintenance and function
Finding regions of interest using location based social media
The discovery of regions of interest in city groups is increasingly important in recent years. In this light, we propose and investigate a novel problem called Region Discovery query (RD query) that finds regions of interest with respect to a user's current geographic location. Given a set of spatial objects O and a query location q, if a circular region ω is with high spatial-object density and is spatially close to q, it is returned by the query and is recommended to users. This type of query can bring significant benefit to users in many useful applications such as trip planning and region recommendation. The RD query faces a big challenge: how to prune the search space in the spatial and density domains. To overcome the challenge and process the RD query efficiently, we propose a novel collaboration search method and we define a pair of bounds to prune the search space effectively. The performance of the RD query is studied by extensive experiments on real and synthetic spatial data
The transition from incoherent to coherent random laser in defect waveguide based on organic/inorganic hybrid laser dye
This paper systematically demonstrated a variety of experimental phenomena of random lasers (RLs) of N,N′-di-(3-(isobutyl polyhedral oligomeric silsesquioxanes)propyl) perylene diimide (DPP) organic/inorganic hybrid laser dye, which is composed of perylene diimide (PDI) as gain media and polyhedral oligomeric silsesquioxanes (POSS) as scattering media at a mole ratio of 1:2. In this work, we observe the transition from incoherent RL in the DPP-doped solutions and polymer membrane systems using dip-coating method to coherent RL in the polymer membrane system with defect waveguide using semi-polymerization (SP) coating method. Meanwhile, we found that the hybrid dye-DPP has a long lasing lifetime compared with the traditional laser dyes, which indicates that the POSS group can suppress the photo-bleaching effect to extend the working life of laser dyes
- …