Vision-language pre-training has significantly elevated performance across a
wide range of image-language applications. Yet, the pre-training process for
video-related tasks demands exceptionally large computational and data
resources, which hinders the progress of video-language models. This paper
investigates a straightforward, highly efficient, and resource-light approach
to adapting an existing image-language pre-trained model for dense video
understanding. Our preliminary experiments reveal that directly fine-tuning
pre-trained image-language models with multiple frames as inputs on video
datasets leads to performance saturation or even a drop. Our further
investigation reveals that it is largely attributed to the bias of learned
high-norm visual features. Motivated by this finding, we propose a simple but
effective pooling strategy to smooth the feature distribution along the
temporal dimension and thus reduce the dominant impacts from the extreme
features. The new model is termed Pooling LLaVA, or \nameofmethod{} in short.
\nameofmethod{} achieves new state-of-the-art performance on modern benchmark
datasets for both video question-answer and captioning tasks. Notably, on the
recent popular Video ChatGPT benchmark, PLLaVA achieves a score of 3.48 out of
5 on average of five evaluated dimensions, exceeding the previous SOTA results
from GPT4V (IG-VLM) by 9\%. On the latest multi-choice benchmark MVBench,
PLLaVA achieves 58.1\% accuracy on average across 20 sub-tasks, 14.5\% higher
than GPT4V (IG-VLM). Code is available at
https://github.com/magic-research/PLLaVA.

通过引入一种简单但有效的汇聚策略，本文将图像 - 语言预训练模型应用于视频理解任务，并在问题回答和字幕生成等基准测试上取得了最新的最佳表现。

PLLaVA：基于图像到视频的无参数 LLaVA 扩展用于视频密集字幕

PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video  Dense Captioning

Pre-trained multilingual language models (e.g., mBERT, XLM-RoBERTa) have
significantly advanced the state-of-the-art for zero-shot cross-lingual
information extraction. These language models ubiquitously rely on word
segmentation techniques that break a word into smaller constituent subwords.
Therefore, all word labeling tasks (e.g. named entity recognition, event
detection, etc.), necessitate a pooling strategy that takes the subword
representations as input and outputs a representation for the entire word.
Taking the task of cross-lingual event detection as a motivating example, we
show that the choice of pooling strategy can have a significant impact on the
target language performance. For example, the performance varies by up to 16
absolute $f_{1}$ points depending on the pooling strategy when training in
English and testing in Arabic on the ACE task. We carry out our analysis with
five different pooling strategies across nine languages in diverse
multi-lingual datasets. Across configurations, we find that the canonical
strategy of taking just the first subword to represent the entire word is
usually sub-optimal. On the other hand, we show that attention pooling is
robust to language and dataset variations by being either the best or close to
the optimal strategy. For reproducibility, we make our code available at
this https URL.

本文研究预训练的多语言语言模型和其在零 - shot 跨语言信息抽取方面的作用，特别是在跨语言事件检测中引入不同的汇聚策略对性能的影响。结果表明，将注意力池化策略作为代表的汇聚方式能够在多种语言和数据集下均取得较好的效果。

子词池化策略对跨语言事件检测的影响

Impact of Subword Pooling Strategy on Cross-lingual Event Detection

Learning the physical simulation on large-scale meshes with flat Graph Neural
Networks (GNNs) and stacking Message Passings (MPs) is challenging due to the
scaling complexity w.r.t. the number of nodes and over-smoothing. There has
been growing interest in the community to introduce \textit{multi-scale}
structures to GNNs for physical simulation. However, current state-of-the-art
methods are limited by their reliance on the labor-intensive drawing of coarser
meshes or building coarser levels based on spatial proximity, which can
introduce wrong edges across geometry boundaries. Inspired by the bipartite
graph determination, we propose a novel pooling strategy, \textit{bi-stride} to
tackle the aforementioned limitations. Bi-stride pools nodes on every other
frontier of the breadth-first search (BFS), without the need for the manual
drawing of coarser meshes and avoiding the wrong edges by spatial proximity.
Additionally, it enables a one-MP scheme per level and non-parametrized pooling
and unpooling by interpolations, resembling U-Nets, which significantly reduces
computational costs. Experiments show that the proposed framework,
\textit{BSMS-GNN}, significantly outperforms existing methods in terms of both
accuracy and computational efficiency in representative physical simulations.

提出了一种新颖的池化策略 “bi-stride”，用于处理规模大的网格上的物理模拟，此方法能够避开手动绘制更糙的网格或基于空间接近性建立更糙的层次结构的问题，能够显著降低计算成本且比现有方法在准确性和计算效率方面表现更好。

使用 BSMS-GNN 进行基于网格的物理模拟的高效学习

Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN

Feature pooling layers (e.g., max pooling) in convolutional neural networks
(CNNs) serve the dual purpose of providing increasingly abstract
representations as well as yielding computational savings in subsequent
convolutional layers. We view the pooling operation in CNNs as a two-step
procedure: first, a pooling window (e.g., $2\times 2$) slides over the feature
map with stride one which leaves the spatial resolution intact, and second,
downsampling is performed by selecting one pixel from each non-overlapping
pooling window in an often uniform and deterministic (e.g., top-left) manner.
Our starting point in this work is the observation that this regularly spaced
downsampling arising from non-overlapping windows, although intuitive from a
signal processing perspective (which has the goal of signal reconstruction), is
not necessarily optimal for \emph{learning} (where the goal is to generalize).
We study this aspect and propose a novel pooling strategy with stochastic
spatial sampling (S3Pool), where the regular downsampling is replaced by a more
general stochastic version. We observe that this general stochasticity acts as
a strong regularizer, and can also be seen as doing implicit data augmentation
by introducing distortions in the feature maps. We further introduce a
mechanism to control the amount of distortion to suit different datasets and
architectures. To demonstrate the effectiveness of the proposed approach, we
perform extensive experiments on several popular image classification
benchmarks, observing excellent improvements over baseline models. Experimental
code is available at this https URL

本研究提出了一种具有随机空间采样的池化策略（S3Pool），其在多个流行的图像分类基准测试中表现出显著的改进。