Semi-supervised 3D object detection from point cloud aims to train a detector
with a small number of labeled data and a large number of unlabeled data. The
core of existing methods lies in how to select high-quality pseudo-labels using
the designed quality evaluation criterion. However, these methods treat each
pseudo bounding box as a whole and assign equal importance to each side during
training, which is detrimental to model performance due to many sides having
poor localization quality. Besides, existing methods filter out a large number
of low-quality pseudo-labels, which also contain some correct regression values
that can help with model training. To address the above issues, we propose a
side-aware framework for semi-supervised 3D object detection consisting of
three key designs: a 3D bounding box parameterization method, an uncertainty
estimation module, and a pseudo-label selection strategy. These modules work
together to explicitly estimate the localization quality of each side and
assign different levels of importance during the training phase. Extensive
experiment results demonstrate that the proposed method can consistently
outperform baseline models under different scenes and evaluation criteria.
Moreover, our method achieves state-of-the-art performance on three datasets
with different labeled ratios.

用于半监督三维物体检测的侧面感知框架，通过三个关键设计（三维边界框参数化方法、不确定性估计模块和伪标签选择策略）共同工作，显式地估计每个侧面的定位质量并在训练阶段分配不同级别的重要性，实验证明这种方法能在不同场景和评估指标下始终优于基准模型。此外，我们的方法在三个具有不同标记比例的数据集上达到了最先进的性能。

非所有面都平等：半监督 3D 目标检测的定位不确定性估计

Not Every Side Is Equal: Localization Uncertainty Estimation for  Semi-Supervised 3D Object Detection

We address the problem of video grounding from natural language queries. The
key challenge in this task is that one training video might only contain a few
annotated starting/ending frames that can be used as positive examples for
model training. Most conventional approaches directly train a binary classifier
using such imbalance data, thus achieving inferior results. The key idea of
this paper is to use the distances between the frame within the ground truth
and the starting (ending) frame as dense supervisions to improve the video
grounding accuracy. Specifically, we design a novel dense regression network
(DRN) to regress the distances from each frame to the starting (ending) frame
of the video segment described by the query. We also propose a simple but
effective IoU regression head module to explicitly consider the localization
quality of the grounding results (i.e., the IoU between the predicted location
and the ground truth). Experimental results show that our approach
significantly outperforms state-of-the-arts on three datasets (i.e.,
Charades-STA, ActivityNet-Captions, and TACoS).

本研究旨在解决自然语言查询的视频地面问题，并通过使用密集监管改进视频地面的准确性，相比与现有方法，我们的方法在三个数据集上均有明显的优势。

视频定位用的密集回归网络

Dense Regression Network for Video Grounding

We are motivated by the need for a generic object proposal generation
algorithm which achieves good balance between object detection recall, proposal
localization quality and computational efficiency. We propose a novel object
proposal algorithm, BING++, which inherits the virtue of good computational
efficiency of BING but significantly improves its proposal localization
quality. At high level we formulate the problem of object proposal generation
from a novel probabilistic perspective, based on which our BING++ manages to
improve the localization quality by employing edges and segments to estimate
object boundaries and update the proposals sequentially. We propose learning
the parameters efficiently by searching for approximate solutions in a
quantized parameter space for complexity reduction. We demonstrate the
generalization of BING++ with the same fixed parameters across different object
classes and datasets. Empirically our BING++ can run at half speed of BING on
CPU, but significantly improve the localization quality by 18.5% and 16.7% on
both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other
state-of-the-art approaches, BING++ can achieve comparable performance, but run
significantly faster.

提出一种新的物体提议算法 BING++，其通过基于边缘和线段估计物体边界，并进行顺序更新提议，提高提议定位质量和计算效率，并在不同的物体类别和数据集上得到了验证。