Despite recent advancements in the Large Reconstruction Model (LRM)
demonstrating impressive results, when extending its input from single image to
multiple images, it exhibits inefficiencies, subpar geometric and texture
quality, as well as slower convergence speed than expected.
It is attributed to that, LRM formulates 3D reconstruction as a naive
images-to-3D translation problem, ignoring the strong 3D coherence among the
input images. In this paper, we propose a Multi-view Large Reconstruction Model
(M-LRM) designed to efficiently reconstruct high-quality 3D shapes from
multi-views in a 3D-aware manner. Specifically, we introduce a multi-view
consistent cross-attention scheme to enable M-LRM to accurately query
information from the input images. Moreover, we employ the 3D priors of the
input multi-view images to initialize the tri-plane tokens. Compared to LRM,
the proposed M-LRM can produce a tri-plane NeRF with $128 \times 128$
resolution and generate 3D shapes of high fidelity. Experimental studies
demonstrate that our model achieves a significant performance gain and faster
training convergence than LRM. Project page: this https URL

通过引入跨视图一致的交叉注意力机制和利用三维先验信息，提出了一种高效的多视角大型重建模型 (M-LRM)，能够从多视图中以三维感知的方式高质量地重建三维形状，并且相比于 LRM 能够生成分辨率为 $128 	imes 128$ 的三平面 NeRF，具有更高的保真度和训练收敛速度。

M-LRM: 多视角大重建模型

M-LRM: Multi-view Large Reconstruction Model

The generalization ability of Deep Neural Networks (DNNs) is still not fully
understood, despite numerous theoretical and empirical analyses. Recently,
Allen-Zhu & Li (2023) introduced the concept of multi-views to explain the
generalization ability of DNNs, but their main target is ensemble or distilled
models, and no method for estimating multi-views used in a prediction of a
specific input is discussed. In this paper, we propose Minimal Sufficient Views
(MSVs), which is similar to multi-views but can be efficiently computed for
real images. MSVs is a set of minimal and distinct features in an input, each
of which preserves a model's prediction for the input. We empirically show that
there is a clear relationship between the number of MSVs and prediction
accuracy across models, including convolutional and transformer models,
suggesting that a multi-view like perspective is also important for
understanding the generalization ability of (non-ensemble or non-distilled)
DNNs.

我们提出了 Minimal Sufficient Views（MSVs）的概念，它类似于多视图，可以高效地计算真实图像的最小和独特特征，通过实验证明，MSVs 的数量与预测准确性之间存在明确的关系，包括卷积模型和变换器模型，这表明多视图的观点对于理解（非集合或非精馏）深度神经网络的泛化能力也很重要。

深度神经网络在预测中使用了多少视图？

How many views does your deep neural network use for prediction?

Transfer learning of large-scale Text-to-Image (T2I) models has recently
shown impressive potential for Novel View Synthesis (NVS) of diverse objects
from a single image. While previous methods typically train large models on
multi-view datasets for NVS, fine-tuning the whole parameters of T2I models not
only demands a high cost but also reduces the generalization capacity of T2I
models in generating diverse images in a new domain. In this study, we propose
an effective method, dubbed NVS-Adapter, which is a plug-and-play module for a
T2I model, to synthesize novel multi-views of visual objects while fully
exploiting the generalization capacity of T2I models. NVS-Adapter consists of
two main components; view-consistency cross-attention learns the visual
correspondences to align the local details of view features, and global
semantic conditioning aligns the semantic structure of generated views with the
reference view. Experimental results demonstrate that the NVS-Adapter can
effectively synthesize geometrically consistent multi-views and also achieve
high performance on benchmarks without full fine-tuning of T2I models. The code
and data are publicly available in
~\href{https://postech-cvlab.github.io/nvsadapter/}{this https URL}.

提出了一种名为 NVS-Adapter 的有效方法，它是用于 T2I 模型的即插即用模块，能够生成具有几何一致性的多视图，并在不完全微调 T2I 模型的情况下在基准测试上取得高性能。

NVS-Adapter: 单图像即插即用的新视角合成

NVS-Adapter: Plug-and-Play Novel View Synthesis from a Single Image

Knowledge Graph (KG) errors introduce non-negligible noise, severely
affecting KG-related downstream tasks. Detecting errors in KGs is challenging
since the patterns of errors are unknown and diverse, while ground-truth labels
are rare or even unavailable. A traditional solution is to construct logical
rules to verify triples, but it is not generalizable since different KGs have
distinct rules with domain knowledge involved. Recent studies focus on
designing tailored detectors or ranking triples based on KG embedding loss.
However, they all rely on negative samples for training, which are generated by
randomly replacing the head or tail entity of existing triples. Such a negative
sampling strategy is not enough for prototyping practical KG errors, e.g.,
(Bruce_Lee, place_of_birth, China), in which the three elements are often
relevant, although mismatched. We desire a more effective unsupervised learning
mechanism tailored for KG error detection. To this end, we propose a novel
framework - ContrAstive knowledge Graph Error Detection (CAGED). It introduces
contrastive learning into KG learning and provides a novel way of modeling KG.
Instead of following the traditional setting, i.e., considering entities as
nodes and relations as semantic edges, CAGED augments a KG into different
hyper-views, by regarding each relational triple as a node. After joint
training with KG embedding and contrastive learning loss, CAGED assesses the
trustworthiness of each triple based on two learning signals, i.e., the
consistency of triple representations across multi-views and the
self-consistency within the triple. Extensive experiments on three real-world
KGs show that CAGED outperforms state-of-the-art methods in KG error detection.
Our codes and datasets are available at this https URL.

本文提出了一种新的框架，ContrAstive knowledge Graph Error Detection (CAGED), 结合了 Knowledge Graph (KG) 的 embedding 和对比学习的 loss，对 KG 中的三元组进行可靠性评估，从而检测异常，通过在不同超视图中的建模 CAGED 比现有方法表现更好。