ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models

Summary

This paper proposes a method to integrate visual prompts into MLLMs without requiring additional training. The key idea is to optimize a learnable latent variable to enhance the attention response to visual tokens during inference, thereby improving the model's ability to focus on specific regions in the visual input.

Strengths

The proposed approach does not requires additional training for unseen datasets.

Weaknesses

1. Very poor writing. The use of abbreviations and full terms is very inconsistent. For example, "Multimodal Large Language Models" is spelled out in full on Line 29-30, while the abbreviation "MLLMs" is used frequently earlier in the text. Additionally, "Linguistic" in Line 34 should be "textual." There are many grammatical errors in Figure 's prompt: "What's color of hat the person wearing?" and output: "The person wearing the hat is wearing a green hat.". Even the title in section 4.2 contains grammatical errors. 2. This paper lacks novelty and is an incremental form of previously proposed methods with no innovative points. 3. In Line 3, the authors claim that attention connects visual tokens and textual tokens, but in Line 33, it changes to MLP. 4. The experimental results are insufficient and lack numerous baselines, such as LLaVA1.5[1], LLaVA-NeXT[2], Monkey[3], and Qwen-VL[4]. 5. The motivation for this study is insufficient. I think there is a baseline that by making the prompt descriptions clearer and more comprehensive based on the original MLLM. This baseline can also leverage MLLM's inherent ability to focus on specific regions. [1] Improved Baselines with Visual Instruction Tuning. [2] LLaVA-NeXT: Improved reasoning, OCR, and world knowledge. [3] Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models. [4] Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Questions

See weakness.

Rating

3

Confidence

5

Soundness

2

Presentation

1

Contribution

1

Limitations

See weakness.

Summary

The paper introduces a training-free approach to improve the referring capabilities of multimodal large language models (MLLM). In particular, the authors iteratively adjust the attention maps using a learnable latent variable, which is based on energy functions. They empirically validate the efficacy of their method on referring classification tasks, leveraging the foundation of LLaVA.

Strengths

+ The paper is generally easy to read and follow. + The figures are clear, especially the visualization of attention maps under different conditions. + The proposed method is training-free and theoretically plug-to-play with different foundation models.

Weaknesses

1. The generalization ability of the proposed method has not been fully verified. (a) It's uncertain whether the method can be applied to MLLMs beyond LLaVA. As the foundational model strengthens, the method's effectiveness could potentially diminish. (b) The energy function, based on a soft mask, heavily relies on the quality of segmentation models. (c) As seen in Table 3, the results appear to be sensitive to hyperparameters. This raises the question: do we need to meticulously adjust the parameters for each model and sample? If that's the case, the practicality of this method could be questionable. 2. There is a naive baseline that requires discussion: directly cropping the referred region and feeding it into the LLM. 3. In Tab. 2&3, the tasks and compared models are indeed limited. It is recommended to discuss more state-of-the-art MLLMs on wider tasks and datasets.

Questions

Please see the Weaknesses.

Rating

4

Confidence

4

Soundness

3

Presentation

3

Contribution

2

Limitations

The authors have discussed it in Sec. 6.

Summary

This paper introduces a training-free approach to integrate visual prompts into MLLMs using learnable latent variables, aiming to enhance the model's interpretability and generalization. It adjusts visual tokens from MLP outputs and optimizes latent variables with an energy function to improve attention on relevant visual regions.

Strengths

1. This paper demonstrates and visualizes how the attention between prompt tokens and visual tokens differs across various layers. 2. Figures 2 and 4 are helpful to understand the method. 3. The quantitative and visualization experiments validate the effectiveness of the proposed approach.

Weaknesses

1. Figures 3(a) and 3(b) show visualization results for different values of η. The paper mentions that in 3(a), η is too small to effectively control the attention. However, the focus of attention map in 3(b) does not significantly differ from that in 3(a). 2. Table 3 shows that the highest accuracy is achieved when α = 400 and T = 3. Why, then, is the value of T ultimately chosen as 4? 3. Since there are no recent developments, contributions, or updates to the MLLM, its detailed presentation in Eq.s (1)-(3) might be unnecessary and could be omitted. Besides, some symbols are not defined, such as, $I_i$, $A_i^{(ct)}$.

Questions

1. It is recommended to validate the effectiveness of the method on additional MLLMs. 2. Additionally, please note that the title listed on the paper submission does not match the title in the PDF.

Rating

5

Confidence

4

Soundness

3

Presentation

2

Contribution

3

Limitations

The limitations have been discussed.

Summary

This paper proposes a training-free ControlMLLM, which uses optimizable latent variables to inject visual prompts into multimodal large MLLMs. The core idea is to adjust the visual token outputs of the MLP during inference, to control the attention response and ensure that the text prompt tokens focus on the indicated visual regions. It enhances the intensity of the indicated regions in the attention maps by optimizing learnable latent variables based on an energy function, enabling reference to various visual prompts (including boxes, masks, scribbles, and points) without model training, fine-tuning, or additional data. The method demonstrates out-of-domain generalization and interpretability, providing a promising direction for integrating referential capabilities into MLLMs. Experiments show that the proposed model is effective.

Strengths

1), Prompt tuning for MLLMs is an interesting direction, and the proposed ideas are both effective and simple. Motivated by text-to-image works, ControlMLLM aims to control the attention map between the textual tokens and visual patches. This idea makes sense and provides a new direction to improve MLLMs. 2), The comparisons and ablations show the effectiveness of the proposed model. 3), The writing is clear, and the figures and tables help to understand the motivations.

Weaknesses

1), The visual prompt shows great improvements over the base model LLaVA, however, it requires additional guidance information and more inference time, which may limit the applications of ControlMLLM. Especially in some complex scenarios where the guidance signals are unavailable. 2), In addition, the region signal $r$ plays a core role during optimization, and it controls the output of MLLMs. What if $r$ itself is wrong? It may mislead the MLLMs.

Questions

See above

Rating

6

Confidence

4

Soundness

3

Presentation

4

Contribution

4

Limitations

None