Large occlusions result in a significant decline in image classification
accuracy. During inference, diverse types of unseen occlusions introduce
out-of-distribution data to the classification model, leading to accuracy
dropping as low as 50%. As occlusions encompass spatially connected regions,
conventional methods involving feature reconstruction are inadequate for
enhancing classification performance. We introduce LEARN: Latent Enhancing
feAture Reconstruction Network -- An auto-encoder based network that can be
incorporated into the classification model before its classifier head without
modifying the weights of classification model. In addition to reconstruction
and classification losses, training of LEARN effectively combines intra- and
inter-class losses calculated over its latent space -- which lead to
improvement in recovering latent space of occluded data, while preserving its
class-specific discriminative information. On the OccludedPASCAL3D+ dataset,
the proposed LEARN outperforms standard classification models (VGG16 and
ResNet-50) by a large margin and up to 2% over state-of-the-art methods. In
cross-dataset testing, our method improves the average classification accuracy
by more than 5% over the state-of-the-art methods. In every experiment, our
model consistently maintains excellent accuracy on in-distribution data.

通过引入名为 LEARN 的基于自动编码器的网络，该网络可以在不修改分类模型权重的情况下将其合并到分类模型，该方法在大遮挡下的图像分类问题中的性能显著优于标准分类模型，并且比其他最先进的方法提高了 2% 至多达 5% 的分类准确率。

遮挡图像分类的潜在增强自动编码器

Latent Enhancing AutoEncoder for Occluded Image Classification

We introduce a novel method for robust and accurate 3D object pose estimation
from a single color image under large occlusions. Following recent approaches,
we first predict the 2D projections of 3D points related to the target object
and then compute the 3D pose from these correspondences using a geometric
method. Unfortunately, as the results of our experiments show, predicting these
2D projections using a regular CNN or a Convolutional Pose Machine is highly
sensitive to partial occlusions, even when these methods are trained with
partially occluded examples. Our solution is to predict heatmaps from multiple
small patches independently and to accumulate the results to obtain accurate
and robust predictions. Training subsequently becomes challenging because
patches with similar appearances but different positions on the object
correspond to different heatmaps. However, we provide a simple yet effective
solution to deal with such ambiguities. We show that our approach outperforms
existing methods on two challenging datasets: The Occluded LineMOD dataset and
the YCB-Video dataset, both exhibiting cluttered scenes with highly occluded
objects. Project website:
this https URL

本研究提出了一种新的方法用于从单个彩色图像中准确地估算有大遮挡的三维物体姿态，即首先预测与目标物体相关的 3D 点的 2D 投影，然后使用几何方法从这些对应关系中计算 3D 姿态，方法是从多个小补丁独立地预测热图，然后积累结果以获得准确和稳健的预测。