Sports videos pose complex challenges, including cluttered backgrounds,
camera angle changes, small action-representing objects, and imbalanced action
class distribution. Existing methods for detecting actions in sports videos
heavily rely on global features, utilizing a backbone network as a black box
that encompasses the entire spatial frame. However, these approaches tend to
overlook the nuances of the scene and struggle with detecting actions that
occupy a small portion of the frame. In particular, they face difficulties when
dealing with action classes involving small objects, such as balls or
yellow/red cards in soccer, which only occupy a fraction of the screen space.
To address these challenges, we introduce a novel approach that analyzes and
models scene entities using an adaptive attention mechanism. Particularly, our
model disentangles the scene content into the global environment feature and
local relevant scene entities feature. To efficiently extract environmental
features while considering temporal information with less computational cost,
we propose the use of a 2D backbone network with a time-shift mechanism. To
accurately capture relevant scene entities, we employ a Vision-Language model
in conjunction with the adaptive attention mechanism. Our model has
demonstrated outstanding performance, securing the 1st place in the
SoccerNet-v2 Action Spotting, FineDiving, and FineGym challenge with a
substantial performance improvement of 1.6, 2.0, and 1.3 points in avg-mAP
compared to the runner-up methods. Furthermore, our approach offers
interpretability capabilities in contrast to other deep learning models, which
are often designed as black boxes. Our code and models are released at:
this https URL

我们提出了一种新的方法，通过自适应注意机制来分析和建模场景实体，以解决在体育视频中探测行为的复杂挑战，并取得了出色的性能改进。

全球与本地场景实体建模以实现精确的动作检测

Unifying Global and Local Scene Entities Modelling for Precise Action  Spotting

Pretrained language models (PLMs) have made remarkable progress in text
generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in
a data-scarce situation. Therefore, it is non-trivial to develop a general and
lightweight model that can adapt to various text generation tasks based on
PLMs. To fulfill this purpose, the recent prompt-based learning offers a
potential solution. In this paper, we improve this technique and propose a
novel prompt-based method (PTG) for text generation in a transferable setting.
First, PTG learns a set of source prompts for various source generation tasks
and then transfers these prompts as target prompts to perform target generation
tasks. To consider both task- and instance-level information, we design an
adaptive attention mechanism to derive the target prompts. For each data
instance, PTG learns a specific target prompt by attending to highly relevant
source prompts. In extensive experiments, PTG yields competitive or better
results than fine-tuning methods. We release our source prompts as an open
resource, where users can add or reuse them to improve new text generation
tasks for future research. Code and data can be available at
this https URL.

本文提出一种基于提示的文本生成方法（PTG），采用预训练语言模型，在面对数据匮乏的情况下通过学习一组源提示并将其转移为目标提示执行目标生成任务，设计了一个自适应注意机制以得出目标提示，实验结果表明 PTG 针对多个任务产生了有竞争力的结果，源提示已开放并可用于未来研究。