This paper introduces an innovative approach to open world recognition (OWR),
where we leverage knowledge acquired from known objects to address the
recognition of previously unseen objects. The traditional method of object
modeling relies on supervised learning with strict closed-set assumptions,
presupposing that objects encountered during inference are already known at the
training phase. However, this assumption proves inadequate for real-world
scenarios due to the impracticality of accounting for the immense diversity of
objects. Our hypothesis posits that object appearances can be represented as
collections of "shareable" mid-level features, arranged in constellations to
form object instances. By adopting this framework, we can efficiently dissect
and represent both known and unknown objects in terms of their appearance cues.
Our paper introduces a straightforward yet elegant method for modeling novel or
unseen objects, utilizing established appearance cues and accounting for
inherent uncertainties. This representation not only enables the detection of
out-of-distribution objects or novel categories among unseen objects but also
facilitates a deeper level of reasoning, empowering the identification of the
superclass to which an unknown instance belongs. This novel approach holds
promise for advancing open world recognition in diverse applications.

该论文介绍了一种创新的开放世界识别方法，利用从已知对象获得的知识来处理之前未见过的对象的识别。该方法通过使用中间特征的集合来表示对象的外观线索，提供了一种建模新对象的简单而优雅的方法，同时考虑了内在的不确定性。这种方法不仅可以检测在未见过的对象中的超出分布范围的对象或新类别，还可以推断未知实例所属的超类，对于推进多样化应用的开放世界识别具有潜力。

共享外观线索的未见物体推理

Unseen Object Reasoning with Shared Appearance Cues

Multi-object tracking (MOT) in video sequences remains a challenging task,
especially in scenarios with significant camera movements. This is because
targets can drift considerably on the image plane, leading to erroneous
tracking outcomes. Addressing such challenges typically requires supplementary
appearance cues or Camera Motion Compensation (CMC). While these strategies are
effective, they also introduce a considerable computational burden, posing
challenges for real-time MOT. In response to this, we introduce UCMCTrack, a
novel motion model-based tracker robust to camera movements. Unlike
conventional CMC that computes compensation parameters frame-by-frame,
UCMCTrack consistently applies the same compensation parameters throughout a
video sequence. It employs a Kalman filter on the ground plane and introduces
the Mapped Mahalanobis Distance (MMD) as an alternative to the traditional
Intersection over Union (IoU) distance measure. By leveraging projected
probability distributions on the ground plane, our approach efficiently
captures motion patterns and adeptly manages uncertainties introduced by
homography projections. Remarkably, UCMCTrack, relying solely on motion cues,
achieves state-of-the-art performance across a variety of challenging datasets,
including MOT17, MOT20, DanceTrack and KITTI, with an exceptional speed of over
1000 FPS on a single CPU. More details and code are available at
this https URL

通过引入 UCMCTrack，本研究提出了一种新颖的基于运动模型的跟踪器，它能够有效地应对视频序列中的相机运动，并仅依赖于运动线索，在各种具有挑战性的数据集上取得了最先进的性能。

UCMCTrack: 统一相机运动补偿的多目标跟踪

UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation

Reconstructing and relighting objects and scenes under varying lighting
conditions is challenging: existing neural rendering methods often cannot
handle the complex interactions between materials and light. Incorporating
pre-computed radiance transfer techniques enables global illumination, but
still struggles with materials with subsurface scattering effects. We propose a
novel framework for learning the radiance transfer field via volume rendering
and utilizing various appearance cues to refine geometry end-to-end. This
framework extends relighting and reconstruction capabilities to handle a wider
range of materials in a data-driven fashion. The resulting models produce
plausible rendering results in existing and novel conditions. We will release
our code and a novel light stage dataset of objects with subsurface scattering
effects publicly available.

本文提出了一种基于体渲染的光传输场学习框架，利用各种外观线索来优化几何，扩展重建和重新照明的能力，提高对于具有亚表面散射效应等更多种材料的处理能力。