Deception detection is an important task that has been a hot research topic
due to its potential applications. It can be applied in many areas, from
national security (e.g., airport security, jurisprudence, and law enforcement)
to real-life applications (e.g., business and computer vision). However, some
critical problems still exist and are worth more investigation. One of the
significant challenges in the deception detection tasks is the data scarcity
problem. Until now, only one multi-modal benchmark open dataset for human
deception detection has been released, which contains 121 video clips for
deception detection (i.e., 61 for deceptive class and 60 for truthful class).
Such an amount of data is hard to drive deep neural network-based methods.
Hence, those existing models often suffer from overfitting problems and low
generalization ability. Moreover, the ground truth data contains some unusable
frames for many factors. However, most of the literature did not pay attention
to these problems. Therefore, in this paper, we design a series of data
preprocessing methods to deal with the aforementioned problem first. Then, we
propose a multi-modal deception detection framework to construct our novel
emotional state-based feature and use the open toolkit openSMILE to extract the
features from the audio modality. We also design a voting scheme to combine the
emotional states information obtained from visual and audio modalities.
Finally, we can determine the novel emotion state transformation feature with
our self-designed algorithms. In the experiment, we conduct the critical
analysis and comparison of the proposed methods with the state-of-the-art
multi-modal deception detection methods. The experimental results show that the
overall performance of multi-modal deception detection has a significant
improvement in the accuracy from 87.77% to 92.78% and the ROC-AUC from 0.9221
to 0.9265.

本文介绍了一种基于多模态特征和情感状态的新的欺诈检测方法，该方法通过数据预处理、音频模式特征提取和投票方案等步骤，显著提高了多模态欺诈检测准确性，并实现了情感状态转换的新功能。

基于情感状态构建鲁棒性特征的多模式视频欺诈检测新型投票方案

Constructing Robust Emotional State-based Feature with a Novel Voting  Scheme for Multi-modal Deception Detection in Videos

We present a method for finding correspondence between 3D models. From an
initial set of feature correspondences, our method uses a fast voting scheme to
separate the inliers from the outliers. The novelty of our method lies in the
use of a combination of local and global constraints to determine if a vote
should be cast. On a local scale, we use simple, low-level geometric
invariants. On a global scale, we apply covariant constraints for finding
compatible correspondences. We guide the sampling for collecting voters by
downward dependencies on previous voting stages. All of this together results
in an accurate matching procedure. We evaluate our algorithm by controlled and
comparative testing on different datasets, giving superior performance compared
to state of the art methods. In a final experiment, we apply our method for 3D
object detection, showing potential use of our method within higher-level
vision.

本研究提出了一种基于局部和全局约束条件的快速三维模型匹配方法，通过对前置投票阶段的依赖进行采样，可以高精度地匹配不同数据集中的物体，并在高级视觉中具有很大的潜在应用。

寻找内点：局部和全局投票的 3D 对应

In search of inliers: 3d correspondence by local and global voting

In this paper, we address the task of detecting semantic parts on partially
occluded objects. We consider a scenario where the model is trained using
non-occluded images but tested on occluded images. The motivation is that there
are infinite number of occlusion patterns in real world, which cannot be fully
covered in the training data. So the models should be inherently robust and
adaptive to occlusions instead of fitting / learning the occlusion patterns in
the training data. Our approach detects semantic parts by accumulating the
confidence of local visual cues. Specifically, the method uses a simple voting
method, based on log-likelihood ratio tests and spatial constraints, to combine
the evidence of local cues. These cues are called visual concepts, which are
derived by clustering the internal states of deep networks. We evaluate our
voting scheme on the VehicleSemanticPart dataset with dense part annotations.
We randomly place two, three or four irrelevant objects onto the target object
to generate testing images with various occlusions. Experiments show that our
algorithm outperforms several competitors in semantic part detection when
occlusions are present.

本篇论文介绍了一种用于检测部分遮挡对象的语义部分的方法，该方法使用本地视觉线索的置信度进行语义部分的检测并使用简单的投票技术来处理局部线索。实验证明，该方法在存在遮挡的情况下，在语义部分检测方面性能优于其他竞争方法。

部分遮挡物体的语义部分检测

Detecting Semantic Parts on Partially Occluded Objects

In 1977, Young proposed a voting scheme that extends the Condorcet Principle
based on the fewest possible number of voters whose removal yields a Condorcet
winner. We prove that both the winner and the ranking problem for Young
elections is complete for the class of problems solvable in polynomial time by
parallel access to NP. Analogous results for Lewis Carroll's 1876 voting scheme
were recently established by Hemaspaandra et al. In contrast, we prove that the
winner and ranking problems in Fishburn's homogeneous variant of Carroll's
voting scheme can be solved efficiently by linear programming.

本文研究了三种投票方案的胜者问题和排名问题的复杂度，发现 Young 的方案和 Lewis Carroll 的方案都是 NP 难问题，而 Fishburn 的方案可以通过线性规划高效求解。