A vision model with general-purpose object-level 3D understanding should be
capable of inferring both 2D (e.g., class name and bounding box) and 3D
information (e.g., 3D location and 3D viewpoint) for arbitrary rigid objects in
natural images. This is a challenging task, as it involves inferring 3D
information from 2D signals and most importantly, generalizing to rigid objects
from unseen categories. However, existing datasets with object-level 3D
annotations are often limited by the number of categories or the quality of
annotations. Models developed on these datasets become specialists for certain
categories or domains, and fail to generalize. In this work, we present
ImageNet3D, a large dataset for general-purpose object-level 3D understanding.
ImageNet3D augments 200 categories from the ImageNet dataset with 2D bounding
box, 3D pose, 3D location annotations, and image captions interleaved with 3D
information. With the new annotations available in ImageNet3D, we could (i)
analyze the object-level 3D awareness of visual foundation models, and (ii)
study and develop general-purpose models that infer both 2D and 3D information
for arbitrary rigid objects in natural images, and (iii) integrate unified 3D
models with large language models for 3D-related reasoning.. We consider two
new tasks, probing of object-level 3D awareness and open vocabulary pose
estimation, besides standard classification and pose estimation. Experimental
results on ImageNet3D demonstrate the potential of our dataset in building
vision models with stronger general-purpose object-level 3D understanding.

通过与 ImageNet 数据集相结合，ImageNet3D 数据集提供了 200 个类别的 2D 和 3D 信息，从而为构建具有更强的通用性目标级三维理解的视觉模型提供了潜力。

ImageNet3D：面向通用对象级别 3D 理解

ImageNet3D: Towards General-Purpose Object-Level 3D Understanding

Major advances in Machine Learning (ML) and Artificial Intelligence (AI)
increasingly take the form of developing and releasing general-purpose models.
These models are designed to be adapted by other businesses and agencies to
perform a particular, domain-specific function. This process has become known
as adaptation or fine-tuning. This paper offers a model of the fine-tuning
process where a Generalist brings the technological product (here an ML model)
to a certain level of performance, and one or more Domain-specialist(s) adapts
it for use in a particular domain. Both entities are profit-seeking and incur
costs when they invest in the technology, and they must reach a bargaining
agreement on how to share the revenue for the technology to reach the market.
For a relatively general class of cost and revenue functions, we characterize
the conditions under which the fine-tuning game yields a profit-sharing
solution. We observe that any potential domain-specialization will either
contribute, free-ride, or abstain in their uptake of the technology, and we
provide conditions yielding these different strategies. We show how methods
based on bargaining solutions and sub-game perfect equilibria provide insights
into the strategic behavior of firms in these types of interactions, and we
find that profit-sharing can still arise even when one firm has significantly
higher costs than another. We also provide methods for identifying
Pareto-optimal bargaining arrangements for a general set of utility functions.

在本研究中，作者提出了一个精细调整过程的模型，其中一名通才将技术产品（即机器学习模型）提升到一定的性能水平，然后一名或多名领域专家将其适应于特定领域的使用。我们为一类相对通用的成本和收益函数条件下，表征了精细调整的博弈问题如何得出一种利润共享解决方案。我们观察到，任何潜在的领域专家都可能对技术的采用做出贡献、免费骑车或者放弃，我们提供了产生这些不同策略的条件。我们展示了基于谈判解和子博弈完美均衡的方法如何揭示公司在这类交互中的战略行为，我们发现即使一家公司的成本远高于另一家公司，利润共享仍然可能出现。我们还为一类通用效用函数的 Pareto 最优谈判安排提供了方法。

优化游戏：通用模型的协商和适应

Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models

In this paper, we focus on the problem of adapting word vector-based models
to new textual data. Given a model pre-trained on large reference data, how can
we adapt it to a smaller piece of data with a slightly different language
distribution? We frame the adaptation problem as a monolingual word vector
alignment problem, and simply average models after alignment. We align vectors
using the RCSLS criterion. Our formulation results in a simple and efficient
algorithm that allows adapting general-purpose models to changing word
distributions. In our evaluation, we consider applications to word embedding
and text classification models. We show that the proposed approach yields good
performance in all setups and outperforms a baseline consisting in fine-tuning
the model on new data.

本文中，我们研究了将词向量模型适应到新的文本数据的问题，我们将适应问题转化为单语词向量对齐问题，通过使用 RCSLS 准则对向量进行对齐，再简单地对模型进行平均即可，在词嵌入和文本分类模型应用实验中，我们证明了这种方法能够在所有设置中产生良好的性能，超越了在新数据上进行微调的基线模型。