Most existing image captioning evaluation metrics focus on assigning a single
numerical score to a caption by comparing it with reference captions. However,
these methods do not provide an explanation for the assigned score. Moreover,
reference captions are expensive to acquire. In this paper, we propose FLEUR,
an explainable reference-free metric to introduce explainability into image
captioning evaluation metrics. By leveraging a large multimodal model, FLEUR
can evaluate the caption against the image without the need for reference
captions, and provide the explanation for the assigned score. We introduce
score smoothing to align as closely as possible with human judgment and to be
robust to user-defined grading criteria. FLEUR achieves high correlations with
human judgment across various image captioning evaluation benchmarks and
reaches state-of-the-art results on Flickr8k-CF, COMPOSITE, and Pascal-50S
within the domain of reference-free evaluation metrics. Our source code and
results are publicly available at: this https URL

借助大型多模型，本文提出了一种解释性的无参考度量方法 FLEUR，用以评估图像说明。FLEUR 不需参考说明即能对图像说明进行评估，并提供评分的解释。通过引入评分平滑化以尽可能与人类判断接近，并对用户定义的等级标准具有鲁棒性，FLEUR 在各种图像说明评估基准上与人类判断之间达到了高度相关性，并在 Flickr8k-CF、COMPOSITE 和 Pascal-50S 上取得了最先进的结果，成为了无参考评估度量领域的最新成果。

FLEUR：基于大型多模态模型的图像字幕解释性无参考评估度量

FLEUR: An Explainable Reference-Free Evaluation Metric for Image  Captioning Using a Large Multimodal Model

Video anomaly detection under weak supervision is challenging due to the
absence of frame-level annotations during the training phase. Previous work has
employed graph convolution networks or self-attention mechanisms to model
temporal relations, along with multiple instance learning (MIL)-based
classification loss to learn discriminative features. However, most of them
utilize multi-branches to capture local and global dependencies separately,
leading to increased parameters and computational cost. Furthermore, the
binarized constraint of the MIL-based loss only ensures coarse-grained
interclass separability, ignoring fine-grained discriminability within
anomalous classes. In this paper, we propose a weakly supervised anomaly
detection framework that emphasizes efficient context modeling and enhanced
semantic discriminability. To this end, we first construct a temporal context
aggregation (TCA) module that captures complete contextual information by
reusing similarity matrix and adaptive fusion. Additionally, we propose a
prompt-enhanced learning (PEL) module that incorporates semantic priors into
the model by utilizing knowledge-based prompts, aiming at enhancing the
discriminative capacity of context features while ensuring separability between
anomaly sub-classes. Furthermore, we introduce a score smoothing (SS) module in
the testing phase to suppress individual bias and reduce false alarms.
Extensive experiments demonstrate the effectiveness of various components of
our method, which achieves competitive performance with fewer parameters and
computational effort on three challenging benchmarks: the UCF-crime,
XD-violence, and ShanghaiTech datasets. The detection accuracy of some anomaly
sub-classes is also improved with a great margin.

本文提出了一种强调高效上下文建模和增强语义可区分性的弱监督视频异常检测框架，其中包括时间上下文聚合模块、语义先验增强学习模块和评分平滑模块， 实验结果表明该方法以更少的参数和计算成本在三个具有挑战性的数据集上实现了竞争性的性能，某些异常子类的检测准确率也得到了极大的提高。