To build intelligent machine learning systems, there are two broad
approaches. One approach is to build inherently interpretable models, as
endeavored by the growing field of causal representation learning. The other
approach is to build highly-performant foundation models and then invest
efforts into understanding how they work. In this work, we relate these two
approaches and study how to learn human-interpretable concepts from data.
Weaving together ideas from both fields, we formally define a notion of
concepts and show that they can be provably recovered from diverse data.
Experiments on synthetic data and large language models show the utility of our
unified approach.

通过结合因果表示学习和理解如何从数据中学习可理解概念的思想，本研究正式定义了一个概念的概念，并证明了它们可以从多样数据中被可靠地还原，合成数据和大型语言模型上的实验表明了我们统一方法的实用性。

学习可解释概念：统一因果表示学习与基础模型

Learning Interpretable Concepts: Unifying Causal Representation Learning  and Foundation Models

Video Panoptic Segmentation (VPS) aims to achieve comprehensive pixel-level
scene understanding by segmenting all pixels and associating objects in a
video. Current solutions can be categorized into online and near-online
approaches. Evolving over the time, each category has its own specialized
designs, making it nontrivial to adapt models between different categories. To
alleviate the discrepancy, in this work, we propose a unified approach for
online and near-online VPS. The meta architecture of the proposed Video-kMaX
consists of two components: within clip segmenter (for clip-level segmentation)
and cross-clip associater (for association beyond clips). We propose clip-kMaX
(clip k-means mask transformer) and HiLA-MB (Hierarchical Location-Aware Memory
Buffer) to instantiate the segmenter and associater, respectively. Our general
formulation includes the online scenario as a special case by adopting clip
length of one. Without bells and whistles, Video-kMaX sets a new
state-of-the-art on KITTI-STEP and VIPSeg for video panoptic segmentation, and
VSPW for video semantic segmentation. Code will be made publicly available.

该研究提出了一种统一的方法来解决视频全景分割中线上和近线问题，并通过采用 clip-kMaX 和 HiLA-MB 等技术实现了此方法。该方法在 KITTI-STEP、VIPSeg 和 VSPW 等数据集上取得了最先进的成果，并将代码公开发布。

Video-kMaX：一种简单的统一方法，用于在线和近线视频全景分割

Video-kMaX: A Simple Unified Approach for Online and Near-Online Video  Panoptic Segmentation

There has been significant progresses for image object detection in recent
years. Nevertheless, video object detection has received little attention,
although it is more challenging and more important in practical scenarios.
Built upon the recent works, this work proposes a unified approach based on
the principle of multi-frame end-to-end learning of features and cross-frame
motion. Our approach extends prior works with three new techniques and steadily
pushes forward the performance envelope (speed-accuracy tradeoff), towards high
performance video object detection.

本文探讨了基于多帧端到端学习特征和帧间运动的统一方法，该方法扩展了之前的研究并引入了三个新技术来提高视频物体检测性能。