Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing

Summary

This paper proposes a unified vision LLM for various downstream visual tasks, including understanding, generating, segmenting, and editing. The proposed framework addresses the problems with respect to images, videos, text, and human interactions. The authors also propose a hybrid method to integrate discrete textual instructions and continuous signal embeddings.

Strengths

From low-level to high-level semantics, the proposed method address the vision problems in a unified framework. In addition, the generation and editing tasks are also included in this framework. The proposed instruction-passing mechanism over discreet texts and continuous embeddings help to address the different downstream tasks. The experiments are comprehensive.

Weaknesses

There is no obvious technical weaknesses from my side. However, I do have some questions about this paper. 1. The authors put "pixel-level" in a highly key position of this paper. From my side, as the proposed model supports different visual tasks, among them, many downstream tasks are not pixel-level such grounding. Why did the authors highlight the concept? 2. The implemental details are missing, especially the synergy module. How can the readers know the re-training and designing process in detail?

Questions

1. To what extent, can the "unification" be used for the multimodal tasks? 2. The authors proposed the statements about instructions and cooperation in Line 57-59. How did they reflect these aspects in the designed modal and experiments? 3. How did the model distinct the features from task-specific and task-invariant parts? In other word, did the authors do something to assign the aims during the model training?

Rating

8

Confidence

4

Soundness

3

Presentation

3

Contribution

4

Limitations

As the most parts are clear for me, I am looking forward to the answers to the above questions. I suggest to add more details of the proposed module and the training scheme in the appendix.

Summary

This paper introduces a universal pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing both static images and dynamic videos. Technically, the authors propose using the LLM as the core brain, incorporating different encoders for images, videos, and pixel-level regions to extend the comprehensive capabilities of LLMs, and various decoders for images, videos, and segmentations to extend the generative capabilities of LLMs. Additionally, a well-rounded hybrid message-passer, incorporating discrete textual instructions and continuous signal embeddings, is designed to pass messages from the LLM to backend decoders. A novel cross-task synergy module is introduced to enhance the synergy between different tasks. To demonstrate the efficacy of the proposed method, the authors conduct experiments on 12 visual tasks and evaluate across 22 datasets. Extensive experiments show that the proposed method can handle multiple vision-language tasks, spanning from visual comprehension to visual generation, and from low-level pixel understanding to high-level semantic understanding.

Strengths

1. The motivation of this work is clear and reasonable. Building a universal generalist is a trending topic, achieving an "one for all" capability. Images and videos are two core vision interfaces through which humans understand the world; thus, powerful vision-language generalists should be capable of comprehending and generating images and videos. 2. This work presents a well-designed unified framework, integrating the SoTA specialists and enabling performance across various vision-language task groups from vision comprehension to generation, and from low-level pixel understanding to high-level semantic comprehension. 3. This work proposes a hybrid instruction passing method by combining discrete textual instructions and continuous signal feature embeddings to ensure effective and precise information passing to the modules. 4. The authors design a fine-grained spatial-temporal vision grounding instruction tuning method, enabling sufficient pixel-level visual perception. Furthermore, they apply adversarial training to enhance synergy between different tasks, achieving overall performance improvement. 5. Extensive experiments are solid, and conducted on various datasets and tasks, including vision segmentation, fine-grained vision understanding, and vision generation. The experimental results show that after integrating cross-task synergy learning, the framework consistently improves performance across the majority of tasks. 6. this work builds a text invocation instruction tuning dataset. If it is publicly available, it will be beneficial to the development of related research. Overall, I like this paper. I think the proposed idea is novel and interesting, and also intuitive. I tend to believe the contribution, novelty and quality of the paper to be highly aimed.

Weaknesses

1. The authors may need to clarify the advantages of their work over existing generalists like HuggingGPT and Visual ChatGPT, which also aim to be generalist models to some extent. 2. The authors propose a hybrid message-passing mechanism and provide a corresponding ablation study. But it would be better to have an intuitive demonstration of the information complementation between discrete textual instructions and continuous feature embeddings. For example, which types of information are passed by textual instructions and which are conveyed by the embeddings. 3. GPT-4 is employed to construct the text invocation-oriented instruction tuning datasets. The detailed prompt templates and some examples should be provided. 4. Some symbols need clarification, such as the J and F in Table 3. 5. A value bar should be added to intuitively show the synergy across different tasks. Additionally, the calculation of the synergy between tasks should be clarified.

Questions

1. I have a question that I would like to discuss with the authors: What are the current limitations of the unified model? Given that the performance of specialists is still not very satisfactory, what is the significance of a unified model? In the future, if the form of specialist tasks changes (e.g., the emergence of vision LMs rather than LLMs being at the core), what modifications would the current unified model need to undergo? 2. If there are better specialists available, how can they be replaced, and what would be the cost of doing so? 3. Are other modalities, such as audio or 3D, being considered for inclusion with fine-grained capabilities?

Rating

8

Confidence

5

Soundness

4

Presentation

4

Contribution

4

Limitations

N/A

Summary

This paper proposed VITRON, a multimodal generalist that supports a wide range of vision tasks, treating images and videos as a unified entity. It combines discrete textual instructions with continuous signal embeddings for effective function invocation. The model is trained on fine-grained spatiotemporal vision-language alignment to enhance its pixel-level visual capabilities. A synergy module is developed to maximize the sharing of fine-grained visual features across different visual tasks, improving overall performance. Overall, VITRON aims to be a comprehensive system for vision-related tasks, demonstrating strong capabilities across various benchmarks.

Strengths

VITRON introduces a novel hybrid method for message passing, combining discrete textual instructions with continuous signal embeddings. It proposes pixel-level spatiotemporal vision-language alignment learning, advancing fine-grained visual capabilities. The paper demonstrates extensive capabilities across 12 visual tasks evaluated on 22 datasets, showcasing high performance. It includes a cross-task synergy module that optimizes task-invariant fine-grained visual features, enhancing task cooperation. VITRON represents a significant step towards a unified AI capable of understanding, generating, segmenting, and editing both images and videos.

Weaknesses

The proposed system is complicated but the paper is not well-structured. A clearer exposition of the system’s components and their interactions would greatly benefit the reader’s comprehension. **Literature Review**: The paper would benefit from a more comprehensive literature review, particularly discussing related works such as Visual ChatGPT [1], HuggingGPT [2], InternGPT [3], ControlLLM [4], GPT4Tools [5], LLaVA-Plus [6], etc. A comparative analysis with these methods in the experimental section would provide valuable context and benchmarking. **Technical Clarifications**: There are several areas in the paper where further clarification is needed. * The necessity of both Module name and Invocation command (Line 874) could be better justified. Using Invocation command is enough? * Why does Video Segmentation module need Region results? In my opinion, the region should be predicted by the backend models? The role of the Region results in the Video Segmentation module warrants further explanation, as it seems the backend model should predict these. * The alignment of Task-specific and Task-invariant Fine-grained Features with the inputs of Backend Visual Specialists is unclear, especially since these specialists are frozen during training. Specifically, how to connect Task-specific Feature and Task-invariant Fine-grained Feature to the backend models? Or the Backend Visual Specialists can work without any input limitation? **Data Format Consistency**: The data format inconsistency between Line 880-881 and page 23 should be addressed to avoid confusion about task requirements and tool usage. Does it mean that the tasks in page 23 do not require using tools? **Impact of Features**: More insight into how the Task-specific and Task-invariant Fine-grained Features affect the overall system performance would be beneficial. **Baseline Comparisons**: Including training-free baselines that utilize GPT-4v with in-context learning and ReACT [7] for tool invocation would provide essential benchmarks for comparison. **Model Results**: As this paper claims that “VITRON surpasses existing SoTA specialists’ performance”, It is recommended to present the results of specialist models in the experimental tables as reference performances. Furthermore, As VITRON is positioned as a generalist, comparisons with other generalist methods would be fair enough. **Tool-use Accuracy**: An exploration of tool-use accuracy across the 12 tasks covered by VITRON is necessary to validate the method’s efficacy. **Execution of Backend Modules**: Clarification is needed on what constitutes the successful execution of backend modules in Figure 4. In addition, why text instruction is more conducive to the successful execution of backend modules while soft feature embedding seems to be more useful in terms of specific task performances? Generally, the paper’s contributions are incremental. To my knowledge, the single tool invocation is not complicated, especially with the limited scale of toolset. There are several papers can solve tasks by chaining multiple tools. [1] Wu C, Yin S, Qi W, et al. Visual chatgpt: Talking, drawing and editing with visual foundation models[J]. arXiv preprint arXiv:2303.04671, 2023. [2] Shen Y, Song K, Tan X, et al. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face[J]. Advances in Neural Information Processing Systems, 2024, 36. [3] Liu Z, He Y, Wang W, et al. Interngpt: Solving vision-centric tasks by interacting with chatgpt beyond language[J]. arXiv preprint arXiv:2305.05662, 2023. [4] Liu Z, Lai Z, Gao Z, et al. Controlllm: Augment language models with tools by searching on graphs[J]. arXiv preprint arXiv:2310.17796, 2023. [5] Yang R, Song L, Li Y, et al. Gpt4tools: Teaching large language model to use tools via self-instruction[J]. Advances in Neural Information Processing Systems, 2024, 36. [6] Liu S, Cheng H, Liu H, et al. Llava-plus: Learning to use tools for creating multimodal agents[J]. arXiv preprint arXiv:2311.05437, 2023. [7] Yao S, Zhao J, Yu D, et al. React: Synergizing reasoning and acting in language models[J]. arXiv preprint arXiv:2210.03629, 2022.

Questions

Please refer to **Weaknesses**.

Rating

5

Confidence

5

Soundness

2

Presentation

2

Contribution

2

Limitations

Please refer to **Weaknesses**.

Summary

This paper proposes a universal vision LLM, named Vitron, for comprehensive understanding, generating, segmenting, and editing of both images and videos. The model incorporates SOTA visual specialists as the backend to support various visual tasks. A cross-task synergy module is advised to learn to maximize the task-invariant visual features. Experiments demonstrate the effectiveness of Vitron on 12 visual tasks.

Strengths

+ The paper is well-written and easy to follow. + The proposed model integrates multiple visual tasks, including understanding, generation, segmentation, and editing. + Experiments show that the performance of Vitron is comparable to SOTA methods of each visual task.

Weaknesses

Some SOTA methods are not included in the experiments. For instance, CM3leon [1] 7B achieves 4.88 FID on COCO-Captions. MAGVIT-v2 [2] reaches 53 FVD on UCF. [1] Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning, 2023. [2] Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation, 2023.

Questions

Refer to weaknesses.

Rating

6

Confidence

4

Soundness

4

Presentation

4

Contribution

4

Limitations

The authors have adequately addressed the limitations.