Referring Expression Comprehension (REC) aims to localize the target objects
specified by free-form natural language descriptions in images. While
state-of-the-art methods achieve impressive performance, they perform a dense
perception of images, which incorporates redundant visual regions unrelated to
linguistic queries, leading to additional computational overhead. This inspires
us to explore a question: can we eliminate linguistic-irrelevant redundant
visual regions to improve the efficiency of the model? Existing relevant
methods primarily focus on fundamental visual tasks, with limited exploration
in vision-language fields. To address this, we propose a coarse-to-fine
iterative perception framework, called ScanFormer. It can iteratively exploit
the image scale pyramid to extract linguistic-relevant visual patches from top
to bottom. In each iteration, irrelevant patches are discarded by our designed
informativeness prediction. Furthermore, we propose a patch selection strategy
for discarded patches to accelerate inference. Experiments on widely used
datasets, namely RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, verify the
effectiveness of our method, which can strike a balance between accuracy and
efficiency.

该研究论文提出了一种名为 ScanFormer 的粗粒度到细粒度迭代感知框架，通过利用图像尺度金字塔从上到下提取与语言相关的视觉路径，排除与语言无关的冗余视觉区域以提高模型的效率，并在相关数据集上验证了该方法的准确性和效率的平衡。

ScanFormer: 迭代扫描式指代表达理解

ScanFormer: Referring Expression Comprehension by Iteratively Scanning

The objective of this work is to localize the sound sources in visual scenes.
Existing audio-visual works employ contrastive learning by assigning
corresponding audio-visual pairs from the same source as positives while
randomly mismatched pairs as negatives. However, these negative pairs may
contain semantically matched audio-visual information. Thus, these semantically
correlated pairs, "hard positives", are mistakenly grouped as negatives. Our
key contribution is showing that hard positives can give similar response maps
to the corresponding pairs. Our approach incorporates these hard positives by
adding their response maps into a contrastive learning objective directly. We
demonstrate the effectiveness of our approach on VGG-SS and SoundNet-Flickr
test sets, showing favorable performance to the state-of-the-art methods.

本文的目标是在视觉场景中定位声源。我们的研究发现，之前的视听学习将语义匹配的音视频信息错误地分为负例，我们的方法直接将这些 “硬正例” 与响应图纳入对比学习目标，证明在 VGG-SS 和 SoundNet-Flickr 测试集上表现优异。

基于语义相似样本更好地学习声音定位

Learning Sound Localization Better From Semantically Similar Samples

We address the problem of localizing non-collaborative WiFi devices in a
large region. Our main motive is to localize humans by localizing their WiFi
devices, e.g. during search-and-rescue operations after a natural disaster. We
use an active sensing approach that relies on Unmanned Aerial Vehicles (UAVs)
to collect signal-strength measurements at informative locations. The problem
is challenging since the measurement is received at arbitrary times and they
are received only when the UAV is in close proximity to the device. For these
reasons, it is extremely important to make prudent decision with very few
measurements. We use the Bayesian optimization approach based on Gaussian
process (GP) regression. This approach works well for our application since GPs
give reliable predictions with very few measurements while Bayesian
optimization makes a judicious trade-off between exploration and exploitation.
In field experiments conducted over a region of 1000 $\times$ 1000 $m^2$, we
show that our approach reduces the search area to less than 100 meters around
the WiFi device within 5 minutes only. Overall, our approach localizes the
device in less than 15 minutes with an error of less than 20 meters.

使用基于贝叶斯优化的高斯过程回归方法，结合无人机采集的信号强度测量，可以在灾后搜救等应用场景下，仅仅花费少量时间和测量准确地定位 WiFi 设备的位置。