We present LatinPipe, the winning submission to the EvaLatin 2024 Dependency
Parsing shared task. Our system consists of a fine-tuned concatenation of base
and large pre-trained LMs, with a dot-product attention head for parsing and
softmax classification heads for morphology to jointly learn both dependency
parsing and morphological analysis. It is trained by sampling from seven
publicly available Latin corpora, utilizing additional harmonization of
annotations to achieve a more unified annotation style. Before fine-tuning, we
train the system for a few initial epochs with frozen weights. We also add
additional local relative contextualization by stacking the BiLSTM layers on
top of the Transformer(s). Finally, we ensemble output probability
distributions from seven randomly instantiated networks for the final
submission. The code is available at
this https URL

LatinPipe 使用预训练语言模型进行依存解析和形态分析，并利用多个拉丁语语料库的样本进行训练，并通过堆叠 BiLSTM 层来增加额外的上下文关联性，最终通过集成网络的输出概率分布来完成最终的解析。

ÚFAL LatinPipe 在 EvaLatin 2024 中的拉丁语形态句法分析

ÚFAL LatinPipe at EvaLatin 2024: Morphosyntactic Analysis of Latin

Recent advances in camera equipped drone applications and their widespread
use increased the demand on vision based object detection algorithms for aerial
images. Object detection process is inherently a challenging task as a generic
computer vision problem, however, since the use of object detection algorithms
on UAVs (or on drones) is relatively a new area, it remains as a more
challenging problem to detect objects in aerial images. There are several
reasons for that including: (i) the lack of large drone datasets including
large object variance, (ii) the large orientation and scale variance in drone
images when compared to the ground images, and (iii) the difference in texture
and shape features between the ground and the aerial images. Deep learning
based object detection algorithms can be classified under two main categories:
(a) single-stage detectors and (b) multi-stage detectors. Both single-stage and
multi-stage solutions have their advantages and disadvantages over each other.
However, a technique to combine the good sides of each of those solutions could
yield even a stronger solution than each of those solutions individually. In
this paper, we propose an ensemble network, SyNet, that combines a multi-stage
method with a single-stage one with the motivation of decreasing the high false
negative rate of multi-stage detectors and increasing the quality of the
single-stage detector proposals. As building blocks, CenterNet and Cascade
R-CNN with pretrained feature extractors are utilized along with an ensembling
strategy. We report the state of the art results obtained by our proposed
solution on two different datasets: namely MS-COCO and visDrone with \%52.1
$mAP_{IoU = 0.75}$ is obtained on MS-COCO $val2017$ dataset and \%26.2
$mAP_{IoU = 0.75}$ is obtained on VisDrone $test-set$.

本文提出了一个结合单步检测和多步检测优点的集成网络 SyNet，该方案在两个不同的数据集中均获得了最先进的结果。

SyNet：用于无人机图像目标检测的集成网络

SyNet: An Ensemble Network for Object Detection in UAV Images

This paper tackles the goal of conclusion-supplement answer generation for
non-factoid questions, which is a critical issue in the field of Natural
Language Processing (NLP) and Artificial Intelligence (AI), as users often
require supplementary information before accepting a conclusion. The current
encoder-decoder framework, however, has difficulty generating such answers,
since it may become confused when it tries to learn several different long
answers to the same non-factoid question. Our solution, called an ensemble
network, goes beyond single short sentences and fuses logically connected
conclusion statements and supplementary statements. It extracts the context
from the conclusion decoder's output sequence and uses it to create
supplementary decoder states on the basis of an attention mechanism. It also
assesses the closeness of the question encoder's output sequence and the
separate outputs of the conclusion and supplement decoders as well as their
combination. As a result, it generates answers that match the questions and
have natural-sounding supplementary sequences in line with the context
expressed by the conclusion sequence. Evaluations conducted on datasets
including "Love Advice" and "Arts & Humanities" categories indicate that our
model outputs much more accurate results than the tested baseline models do.

本文探讨了解决非事实型问题的结论 - 补充回答生成目标，设计了一种基于集成网络解决方案，相较于基线模型，能够更准确地生成符合问题同时又自然流畅且与上下文有关的补充回答序列。

非事实问题的结论补充回答生成

Conclusion-Supplement Answer Generation for Non-Factoid Questions

Despite the advancement of supervised image recognition algorithms, their
dependence on the availability of labeled data and the rapid expansion of image
categories raise the significant challenge of zero-shot learning. Zero-shot
learning (ZSL) aims to transfer knowledge from labeled classes into unlabeled
classes to reduce human labeling effort. In this paper, we propose a novel
progressive ensemble network model with multiple projected label embeddings to
address zero-shot image recognition. The ensemble network is built by learning
multiple image classification functions with a shared feature extraction
network but different label embedding representations, which enhance the
diversity of the classifiers and facilitate information transfer to unlabeled
classes. A progressive training framework is then deployed to gradually label
the most confident images in each unlabeled class with predicted pseudo-labels
and update the ensemble network with the training data augmented by the
pseudo-labels. The proposed model performs training on both labeled and
unlabeled data. It can naturally bridge the domain shift problem in visual
appearances and be extended to the generalized zero-shot learning scenario. We
conduct experiments on multiple ZSL datasets and the empirical results
demonstrate the efficacy of the proposed model.

本研究提出了一种新型的渐进式集成网络模型，采用多个投影标签嵌入来解决零样本图像识别问题，在有标记和无标记的数据上进行训练，为未标记的类提供知识转移，通过渐进式训练框架和预测伪标签来逐步标注最自信的图像，并更新集成网络以扩展到广义零样本学习情境，实验证明提出的模型在多个零样本学习数据集上具有很好的性能。