MFER-VLM: Multimodal Facial Expression Recognition by Dimensionality Reduction and LowRank MoE
Authors: Yang Lei, Xiting He, Jiang Zhong, Heng Zhang, Yunlin wen, Rui Yang


Reviewer paper summaries: The paper presented MFER-VLM which incorporate a novel method for multimodal facial expression recognition through SVDFusion and LoRA-MoE. In addition the paper introduces new dataset, i.e., FacialAffect, which is derived from a dataset sourced from Kaggle. Authors need to ensure that the dataset license allows derivative works or it might lead to an ethical problem later on. The paper is not well-written, with many missing details in the method section, experiment, result, and analysis.
Summary of strengths: - The paper introduces novel approach in terms of modeling such as LoRA-MoE and SVDFusion
- The paper introduces a new facial emotion recognition dataset called FacialAffect
Summary of weaknesses: - The paper is not well-written, check comments and suggestions for more details
- The details regarding how the FacialAffect is created is unclear
- The paper lack of many details in terms of methods, results, and analysis
- The source of the dataset is never attributed, the license of the original data is also neve declared which might raise a serious ethical convern
- The ablation without the additional SVDFusion+LoRAMoE shows minimal performance difference, suggesting the ineffectiveness of the method, Nonetheless no discussion is provided in the paper.
Comments, suggestions and typos: - I argue that this is not a VLM, as VLM generally accept multimodal input with textual output, The final ouput of the model is not really clear to me as the Visual linguiostic transformer part is not explained in the paper. I suspect the method is more similar to prior multimodal works before VLM era such as [1,2,3]
- It will be great if authors provide justification why only the top-5% of SVD is retained, providing eigen value analysis similar to the preliminaries (Section 3) in the Linformer (Wang et al., 2020) paper might be beneficial
- The paper is not well written. For example, In the restuls, ablation, and case study sections, it is unclear which result table / figure should readers check.
- How is the relabelling process done in the FacialAffect dataset? How many labels are updated?
- What is label accuracy? is it just a regular accuracy? How come in table 1, the F1 is similar but the accuracy of models other than  MFER-VLM being so low? 
- It is unclear how the VLMs except of MFER-VLM is utilized on this paper, is it fine-tuned / zero-shot shot / few-shot? And is it even fair to compare zero-shot VLM with fully-finetuned models?
- What is the Visual Linguistic transformer in Figure 2?
- Line 058: why authors cite (Vaswani et al., 2017)  when introducing MFER-VLM?
- What are the final output of the MFER-VLM and what is the loss function? Is it generating text or classification labels?
- What is the license of the original data sourced from Kaggle? Does it allow derivative work and redistribution?
- The source of the data is never attributed clearly in the paper
Overall assessment scores: 2