Human action recognition is a crucial task in computer vision systems.
However, in real-world scenarios, human actions often fall outside the
distribution of training data, requiring a model to both recognize
in-distribution (ID) actions and reject out-of-distribution (OOD) ones. Despite
its importance, there has been limited research on OOD detection in human
actions. Existing works on OOD detection mainly focus on image data with RGB
structure, and many methods are post-hoc in nature. While these methods are
convenient and computationally efficient, they often lack sufficient accuracy
and fail to consider the presence of OOD samples. To address these challenges,
we propose a novel end-to-end skeleton-based model called Action-OOD,
specifically designed for OOD human action detection. Unlike some existing
approaches that may require prior knowledge of existing OOD data distribution,
our model solely utilizes in-distribution (ID) data during the training stage,
effectively mitigating the overconfidence issue prevalent in OOD detection. We
introduce an attention-based feature fusion block, which enhances the model's
capability to recognize unknown classes while preserving classification
accuracy for known classes. Further, we present a novel energy-based loss
function and successfully integrate it with the traditional cross-entropy loss
to maximize the separation of data distributions between ID and OOD. Through
extensive experiments conducted on NTU-RGB+D 60, NTU-RGB+D 120, and
Kinetics-400 datasets, we demonstrate the superior performance of our proposed
approach compared to state-of-the-art methods. Our findings underscore the
effectiveness of classic OOD detection techniques in the context of
skeleton-based action recognition tasks, offering promising avenues for future
research in this field. Code will be available at:
this https URL

我们提出了一种新颖的端到端基于骨架的模型，称为 Action-OOD，用于识别人类动作中的 ODD，通过引入基于注意力的特征融合块和能量损失函数，我们的模型在识别未知类别的能力和分类准确性方面表现出了优越的性能。

Action-OOD: 针对鲁棒性的端到端基于骨骼的模型用于非同分布人体动作检测

Action-OOD: An End-to-End Skeleton-Based Model for Robust  Out-of-Distribution Human Action Detection

Existing neural radiance fields (NeRF)-based novel view synthesis methods for
large-scale outdoor scenes are mainly built on a single altitude. Moreover,
they often require a priori camera shooting height and scene scope, leading to
inefficient and impractical applications when camera altitude changes. In this
work, we propose an end-to-end framework, termed AG-NeRF, and seek to reduce
the training cost of building good reconstructions by synthesizing
free-viewpoint images based on varying altitudes of scenes. Specifically, to
tackle the detail variation problem from low altitude (drone-level) to high
altitude (satellite-level), a source image selection method and an
attention-based feature fusion approach are developed to extract and fuse the
most relevant features of target view from multi-height images for
high-fidelity rendering. Extensive experiments demonstrate that AG-NeRF
achieves SOTA performance on 56 Leonard and Transamerica benchmarks and only
requires a half hour of training time to reach the competitive PSNR as compared
to the latest BungeeNeRF.

提出了一种名为 AG-NeRF 的端到端框架，通过合成根据不同高度的场景生成自由视角图像，从而降低构建良好重建模型的训练成本，并通过源图像选择方法和基于注意力的特征融合方法来解决从低高度到高高度的细节变化问题，实验证明 AG-NeRF 在多个评估基准上达到了 SOTA 水平，并且仅需半小时的训练时间与最新的 BungeeNeRF 相比便可达到竞争性的峰值信噪比。

AG-NeRF：用于多高度大规模室外场景渲染的注意力引导神经辐射场

AG-NeRF: Attention-guided Neural Radiance Fields for Multi-height  Large-scale Outdoor Scene Rendering

Powerful manipulation techniques have made digital image forgeries be easily
created and widespread without leaving visual anomalies. The blind localization
of tampered regions becomes quite significant for image forensics. In this
paper, we propose an effective image tampering localization network (EITLNet)
based on a two-branch enhanced transformer encoder with attention-based feature
fusion. Specifically, a feature enhancement module is designed to enhance the
feature representation ability of the transformer encoder. The features
extracted from RGB and noise streams are fused effectively by the coordinate
attention-based fusion module at multiple scales. Extensive experimental
results verify that the proposed scheme achieves the state-of-the-art
generalization ability and robustness in various benchmark datasets. Code will
be public at this https URL

本文提出了一种基于增强型双分支变压器编码器和基于注意力的特征融合的有效图像篡改定位网络 (EITLNet)，通过特征增强模块增强变压器编码器的特征表示能力，并且通过坐标注意力融合模块在多个尺度上有效地融合从 RGB 和噪声流提取的特征。广泛的实验结果验证了该方案在各种基准数据集上达到了最先进的泛化能力和鲁棒性。代码将在此 URL 进行公开。