Video retrieval is becoming increasingly important owing to the rapid
emergence of videos on the Internet. The dominant paradigm for video retrieval
learns video-text representations by pushing the distance between the
similarity of positive pairs and that of negative pairs apart from a fixed
margin. However, negative pairs used for training are sampled randomly, which
indicates that the semantics between negative pairs may be related or even
equivalent, while most methods still enforce dissimilar representations to
decrease their similarity. This phenomenon leads to inaccurate supervision and
poor performance in learning video-text representations.
While most video retrieval methods overlook that phenomenon, we propose an
adaptive margin changed with the distance between positive and negative pairs
to solve the aforementioned issue. First, we design the calculation framework
of the adaptive margin, including the method of distance measurement and the
function between the distance and the margin. Then, we explore a novel
implementation called "Cross-Modal Generalized Self-Distillation" (CMGSD),
which can be built on the top of most video retrieval models with few
modifications. Notably, CMGSD adds few computational overheads at train time
and adds no computational overhead at test time. Experimental results on three
widely used datasets demonstrate that the proposed method can yield
significantly better performance than the corresponding backbone model, and it
outperforms state-of-the-art methods by a large margin.

本研究提出了一种自适应边界训练机制，称为 CMGSD，通过计算正负实例的距离调整边界，以改善视频检索中的两个问题，即负样本之间的语义联系及性能下降，并利用 CMGSD 的优势在三个广泛使用的数据集上获得更好的性能。

通过自适应边缘提升视频检索

Improving Video Retrieval by Adaptive Margin

Racial equality is an important theme of international human rights law, but
it has been largely obscured when the overall face recognition accuracy is
pursued blindly. More facts indicate racial bias indeed degrades the fairness
of recognition system and the error rates on non-Caucasians are usually much
higher than Caucasians. To encourage fairness, we introduce the idea of
adaptive margin to learn balanced performance for different races based on
large margin losses. A reinforcement learning based race balance network
(RL-RBN) is proposed. We formulate the process of finding the optimal margins
for non-Caucasians as a Markov decision process and employ deep Q-learning to
learn policies for an agent to select appropriate margin by approximating the
Q-value function. Guided by the agent, the skewness of feature scatter between
races can be reduced. Besides, we provide two ethnicity aware training
datasets, called BUPT-Globalface and BUPT-Balancedface dataset, which can be
utilized to study racial bias from both data and algorithm aspects. Extensive
experiments on RFW database show that RL-RBN successfully mitigates racial bias
and learns more balanced performance for different races.

通过提出自适应边界的想法，构建了一个基于深度 Q 学习的 Race Balance Network (RL-RBN) 以及两个面部识别数据集，用以研究数据和算法层面的种族偏差，实验结果表明该方法成功减小了种族偏差并且学习到了更平衡的表现。