Enhancing Multi-Modal Reasoning Segmentation through a Hierarchical Chain-of-Thought Approach

แนวคิดหลัก

Chains of Reasoning and Segmenting (CoReS) provides a more accurate visual search for multi-modal fine-grained tasks through a top-down chain-like visual hierarchy.

บทคัดย่อ

The paper introduces CoReS, a multi-modal chain of thought approach for reasoning segmentation tasks. The key insights are:

Reasoning segmentation tasks require a nuanced understanding of complex queries to accurately pinpoint object regions. However, Multi-modal Large Language Models (MLLM) often struggle to accurately localize objects described in complex reasoning contexts.

The authors propose that the reasoning segmentation process should mirror the cognitive stages of human visual search, where each step is a progressive refinement of thought toward the final object.

CoReS introduces a dual-chain structure that generates multi-modal, chain-like outputs to aid the segmentation process. The reasoning chain injects semantic information for different logical levels into different tokens, while the segmentation chain utilizes this logic to iteratively optimize the segmentation results.

To steer the MLLM's outputs into this intended hierarchy, the authors incorporate in-context inputs as guidance. These are randomly sampled textual examples that indicate the desired chain-like rules of output.

Extensive experiments demonstrate the superior performance of CoReS, which surpasses the state-of-the-art method by 7.1% on the ReasonSeg dataset. The approach also outperforms other methods on referring segmentation benchmarks.

สถิติ

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คำพูด

"The reasoning segmentation process should mirror the cognitive stages of human visual search, where each step is a progressive refinement of thought toward the final object."
"CoReS introduces a dual-chain structure that generates multi-modal, chain-like outputs to aid the segmentation process."
"To steer the MLLM's outputs into this intended hierarchy, the authors incorporate in-context inputs as guidance."

ข้อมูลเชิงลึกที่สำคัญจาก

CoReS

by Xiaoyi Bao,S... ที่ arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05673.pdf

สอบถามเพิ่มเติม

How can the chain-of-thought approach in CoReS be extended to other multi-modal tasks beyond reasoning segmentation?

The chain-of-thought approach in CoReS can be extended to other multi-modal tasks by adapting the dual-chain hierarchical structure to suit the specific requirements of different tasks. For tasks that involve complex reasoning and fine-grained understanding across modalities, a similar dual-chain structure can be implemented to guide the model in a top-down logical hierarchy. By decomposing the task into logical levels and providing in-context guidance, the model can progressively refine its understanding and generate more accurate outputs. This approach can be applied to tasks such as visual question answering, image captioning, and multimodal reasoning in various domains.

What are the potential limitations of the in-context guidance provided in CoReS, and how could it be further improved?

One potential limitation of the in-context guidance in CoReS is the quality and diversity of the context library. If the examples provided in the context library are limited or not representative of the full range of reasoning scenarios, the effectiveness of the guidance may be compromised. To address this limitation, the context library can be expanded with a more diverse set of examples covering a wide range of reasoning contexts. Additionally, incorporating a mechanism for dynamic updating of the context library based on model performance and feedback could further enhance the quality of the in-context guidance.

What insights from human visual search and cognition could be leveraged to enhance multi-modal reasoning and understanding in other domains?

Insights from human visual search and cognition, such as the top-down hierarchical approach to object localization and the progressive refinement of thought towards a final object, can be leveraged to enhance multi-modal reasoning and understanding in other domains. By incorporating a similar logical hierarchy and guiding the model through different levels of reasoning, models can better understand complex queries and generate more accurate outputs. Additionally, drawing inspiration from human cognitive processes, models can benefit from pre-existing knowledge and contextual cues to improve reasoning and segmentation tasks in various domains.