For object detection, it is possible to view the prediction of bounding boxes
as a reverse diffusion process. Using a diffusion model, the random bounding
boxes are iteratively refined in a denoising step, conditioned on the image. We
propose a stochastic accumulator function that starts each run with random
bounding boxes and combines the slightly different predictions. We empirically
verify that this improves detection performance. The improved detections are
leveraged on unlabelled images as weighted pseudo-labels for semi-supervised
learning. We evaluate the method on a challenging out-of-domain test set. Our
method brings significant improvements and is on par with human-selected
pseudo-labels, while not requiring any human involvement.

通过扩散模型，在图像上迭代细化随机边界框的预测，在无人参与的情况下，利用改进的检测结果作为带权重的伪标签进行半监督学习。

使用更少的标签对半监督领域自适应扩展更多对象

Diffusing More Objects for Semi-Supervised Domain Adaptation with Less  Labeling

Numerous fields, such as ecology, biology, and neuroscience, use animal
recordings to track and measure animal behaviour. Over time, a significant
volume of such data has been produced, but some computer vision techniques
cannot explore it due to the lack of annotations. To address this, we propose
an approach for estimating 2D mouse body pose from unlabelled images using a
synthetically generated empirical pose prior. Our proposal is based on a recent
self-supervised method for estimating 2D human pose that uses single images and
a set of unpaired typical 2D poses within a GAN framework. We adapt this method
to the limb structure of the mouse and generate the empirical prior of 2D poses
from a synthetic 3D mouse model, thereby avoiding manual annotation. In
experiments on a new mouse video dataset, we evaluate the performance of the
approach by comparing pose predictions to a manually obtained ground truth. We
also compare predictions with those from a supervised state-of-the-art method
for animal pose estimation. The latter evaluation indicates promising results
despite the lack of paired training data. Finally, qualitative results using a
dataset of horse images show the potential of the setting to adapt to other
animal species.

使用合成的经验姿势先验从未标记图像中估计 2D 鼠标身体姿势的方法可以在缺少注释的情况下探测和测量动物行为，展示了对其他动物物种的潜力。

鼠类和姿态：来自未标记数据和合成先验的 2D 鼠标姿态估计

Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and  Synthetic Prior

Important people detection is to automatically detect the individuals who
play the most important roles in a social event image, which requires the
designed model to understand a high-level pattern. However, existing methods
rely heavily on supervised learning using large quantities of annotated image
samples, which are more costly to collect for important people detection than
for individual entity recognition (eg, object recognition). To overcome this
problem, we propose learning important people detection on partially annotated
images. Our approach iteratively learns to assign pseudo-labels to individuals
in un-annotated images and learns to update the important people detection
model based on data with both labels and pseudo-labels. To alleviate the
pseudo-labelling imbalance problem, we introduce a ranking strategy for
pseudo-label estimation, and also introduce two weighting strategies: one for
weighting the confidence that individuals are important people to strengthen
the learning on important people and the other for neglecting noisy unlabelled
images (ie, images without any important people). We have collected two
large-scale datasets for evaluation. The extensive experimental results clearly
confirm the efficacy of our method attained by leveraging unlabelled images for
improving the performance of important people detection.

本论文提出了一种使用部分注释的图像进行重要人物检测的方法，其中引入了伪标签分配的迭代学习方法，伪标签排名策略和两种加权策略，用于增强重要人物学习和忽略嘈杂的无标签图像，并证实了该方法的有效性。