We present a method for joint alignment of sparse in-the-wild image
collections of an object category. Most prior works assume either ground-truth
keypoint annotations or a large dataset of images of a single object category.
However, neither of the above assumptions hold true for the long-tail of the
objects present in the world. We present a self-supervised technique that
directly optimizes on a sparse collection of images of a particular
object/object category to obtain consistent dense correspondences across the
collection. We use pairwise nearest neighbors obtained from deep features of a
pre-trained vision transformer (ViT) model as noisy and sparse keypoint matches
and make them dense and accurate matches by optimizing a neural network that
jointly maps the image collection into a learned canonical grid. Experiments on
CUB and SPair-71k benchmarks demonstrate that our method can produce globally
consistent and higher quality correspondences across the image collection when
compared to existing self-supervised methods. Code and other material will be
made available at https://kampta.github.io/asic.

该研究提出了一种自监督技术的目标对象排列方法，使用神经网络将图像集映射到学习的规范网格，从而使得得到的密集一一对应点更加准确和稳定。

ASIC: 对野外稀疏图像集的对齐

ASIC: Aligning Sparse in-the-wild Image Collections

We propose a novel framework for fine-grained object recognition that learns
to recover object variation in 3D space from a single image, trained on an
image collection without using any ground-truth 3D annotation. We accomplish
this by representing an object as a composition of 3D shape and its appearance,
while eliminating the effect of camera viewpoint, in a canonical configuration.
Unlike conventional methods modeling spatial variation in 2D images only, our
method is capable of reconfiguring the appearance feature in a canonical 3D
space, thus enabling the subsequent object classifier to be invariant under 3D
geometric variation. Our representation also allows us to go beyond existing
methods, by incorporating 3D shape variation as an additional cue for object
recognition. To learn the model without ground-truth 3D annotation, we deploy a
differentiable renderer in an analysis-by-synthesis framework. By incorporating
3D shape and appearance jointly in a deep representation, our method learns the
discriminative representation of the object and achieves competitive
performance on fine-grained image recognition and vehicle re-identification. We
also demonstrate that the performance of 3D shape reconstruction is improved by
learning fine-grained shape deformation in a boosting manner.

该研究提出了一种新型的精细物体识别框架，能够从一个单一的图像中学习恢复物体在三维空间中的变化，并在无需使用任何基准 3D 注释情况下，在图像集合上进行训练。

学习精细识别的规范化 3D 物体表示

Learning Canonical 3D Object Representation for Fine-Grained Recognition

We present a learning framework that learns to recover the 3D shape, pose and
texture from a single image, trained on an image collection without any ground
truth 3D shape, multi-view, camera viewpoints or keypoint supervision. We
approach this highly under-constrained problem in a "analysis by synthesis"
framework where the goal is to predict the likely shape, texture and camera
viewpoint that could produce the image with various learned category-specific
priors. Our particular contribution in this paper is a representation of the
distribution over cameras, which we call "camera-multiplex". Instead of picking
a point estimate, we maintain a set of camera hypotheses that are optimized
during training to best explain the image given the current shape and texture.
We call our approach Unsupervised Category-Specific Mesh Reconstruction
(U-CMR), and present qualitative and quantitative results on CUB, Pascal 3D and
new web-scraped datasets. We obtain state-of-the-art camera prediction results
and show that we can learn to predict diverse shapes and textures across
objects using an image collection without any keypoint annotations or 3D ground
truth. Project page: this https URL

本文提出了一个无监督类别特定网格重构学习框架，可以从一组没有 3D 地面真实值的图像中恢复出物体的 3D 形状、姿态和纹理，并维护了一个优化的摄像机假设集以更好地解释给定形状和纹理的图像，并在 CUB、Pascal 3D 和新的网页抓取数据集上获得了最先进的摄像机预测结果。

无需关键点的形状和视点

Shape and Viewpoint without Keypoints

Learning with complete or partial supervision is powerful but relies on
ever-growing human annotation efforts. As a way to mitigate this serious
problem, as well as to serve specific applications, unsupervised learning has
emerged as an important field of research. In computer vision, unsupervised
learning comes in various guises. We focus here on the unsupervised discovery
and matching of object categories among images in a collection, following the
work of Cho et al. 2015. We show that the original approach can be reformulated
and solved as a proper optimization problem. Experiments on several benchmarks
establish the merit of our approach.

本研究旨在通过解决一个适当的优化问题，对图像集合中的对象类别进行无监督发现和匹配，以应对日益增长的人工注释工作量和服务特定应用的需求。在多个基准测试中，我们的方法证明了其价值。