Visual Question Answering (VQA) has emerged as a highly engaging field in
recent years, attracting increasing research efforts aiming to enhance VQA
accuracy through the deployment of advanced models such as Transformers.
Despite this growing interest, there has been limited exploration into the
comparative analysis and impact of textual modalities within VQA, particularly
in terms of model complexity and its effect on performance. In this work, we
conduct a comprehensive comparison between complex textual models that leverage
long dependency mechanisms and simpler models focusing on local textual
features within a well-established VQA framework. Our findings reveal that
employing complex textual encoders is not invariably the optimal approach for
the VQA-v2 dataset. Motivated by this insight, we introduce an improved model,
ConvGRU, which incorporates convolutional layers to enhance the representation
of question text. Tested on the VQA-v2 dataset, ConvGRU achieves better
performance without substantially increasing parameter complexity.

本研究论文比较了复杂的文本模型和简单的局部文本特征模型在 VQA 中的效果，并发现采用复杂的文本编码器并不总是最优的选择。基于这一发现，论文提出了一种改进的模型 ConvGRU，通过引入卷积层增强了问题文本的表示，在 VQA-v2 数据集上实现了更好的性能而不显著增加参数复杂性。

增强的视觉问答：卷积的比较分析与文本特征提取

Enhanced Visual Question Answering: A Comparative Analysis and Textual  Feature Extraction Via Convolutions

Prior work on 6-DoF object pose estimation has largely focused on
instance-level processing, in which a textured CAD model is available for each
object being detected. Category-level 6-DoF pose estimation represents an
important step toward developing robotic vision systems that operate in
unstructured, real-world scenarios. In this work, we propose a single-stage,
keypoint-based approach for category-level object pose estimation that operates
on unknown object instances within a known category using a single RGB image as
input. The proposed network performs 2D object detection, detects 2D keypoints,
estimates 6-DoF pose, and regresses relative bounding cuboid dimensions. These
quantities are estimated in a sequential fashion, leveraging the recent idea of
convGRU for propagating information from easier tasks to those that are more
difficult. We favor simplicity in our design choices: generic cuboid vertex
coordinates, single-stage network, and monocular RGB input. We conduct
extensive experiments on the challenging Objectron benchmark, outperforming
state-of-the-art methods on the 3D IoU metric (27.6% higher than the MobilePose
single-stage approach and 7.1% higher than the related two-stage approach).

该研究提出了一种基于关键点的方法，通过一个 RGB 图像作为输入，对已知类别内的未知目标实例进行目录级别的物体位姿估计，采用单阶段神经网络，并在 Objectron 基准测试中表现出比现有方法更好的性能

基于关键点的单阶段类别级别物体姿态估计

Single-Stage Keypoint-Based Category-Level Object Pose Estimation from  an RGB Image

Video anomaly detection is commonly used in many applications such as
security surveillance and is very challenging.A majority of recent video
anomaly detection approaches utilize deep reconstruction models, but their
performance is often suboptimal because of insufficient reconstruction error
differences between normal and abnormal video frames in practice. Meanwhile,
frame prediction-based anomaly detection methods have shown promising
performance. In this paper, we propose a novel and robust unsupervised video
anomaly detection method by frame prediction with proper design which is more
in line with the characteristics of surveillance videos. The proposed method is
equipped with a multi-path ConvGRU-based frame prediction network that can
better handle semantically informative objects and areas of different scales
and capture spatial-temporal dependencies in normal videos. A noise tolerance
loss is introduced during training to mitigate the interference caused by
background noise. Extensive experiments have been conducted on the CUHK Avenue,
ShanghaiTech Campus, and UCSD Pedestrian datasets, and the results show that
our proposed method outperforms existing state-of-the-art approaches.
Remarkably, our proposed method obtains the frame-level AUROC score of 88.3% on
the CUHK Avenue dataset.

提出了一种基于帧预测的视频异常检测方法，该方法采用了 Multi-path ConvGRU 网络以更好地处理语义信息对象和不同尺度的区域，并在训练期间引入了噪声容忍损失来减轻背景噪声造成的干扰，经过对 CUHK Avenue、ShanghaiTech Campus 和 UCSD Pedestrian 数据集进行了广泛的实验，并获得了比现有技术方法更好的表现，尤其是在 CUHK Avenue 数据集上，获得了 88.3％的帧级 AUROC 分数。