HyperSeg: Towards Universal Visual Segmentation with Large Language Model (2411.17606v2)

Abstract: This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual LLMs (VLLMs). Despite significant progress in current unified segmentation methods, limitations in adaptation to both image and video scenarios, as well as the complex reasoning segmentation, make it difficult for them to handle various challenging instructions and achieve an accurate understanding of fine-grained vision-language correlations. We propose HyperSeg, the first VLLM-based universal segmentation model for pixel-level image and video perception, encompassing generic segmentation tasks and more complex reasoning perception tasks requiring powerful reasoning abilities and world knowledge. Besides, to fully leverage the recognition capabilities of VLLMs and the fine-grained visual information, HyperSeg incorporates hybrid entity recognition and fine-grained visual perceiver modules for various segmentation tasks. Combined with the temporal adapter, HyperSeg achieves a comprehensive understanding of temporal information. Experimental results validate the effectiveness of our insights in resolving universal image and video segmentation tasks, including the more complex reasoning perception tasks. Our code is available.

• The paper’s main contribution is HyperSeg, a universal segmentation model that unites image and video segmentation with complex reasoning using VLLMs.
• It introduces three novel components—Hybrid Entity Recognition, Fine-grained Visual Perceiver, and Temporal Adapter—that enhance pixel-level understanding across modalities.
• Extensive experiments demonstrate HyperSeg’s superior performance on multi-modal benchmarks, paving the way for advanced real-world visual perception applications.

An Analytical Perspective on HyperSeg: Towards Universal Visual Segmentation

The paper "HyperSeg: Towards Universal Visual Segmentation" introduces an innovative framework for addressing comprehensive segmentation tasks in the field of computer vision. It proposes a novel universal segmentation model based on Visual LLMs (VLLMs), dubbed HyperSeg, that can effectively handle both image and video perception tasks at the pixel level, and accommodate complex reasoning capabilities.

Contribution of HyperSeg Model

The core contribution of the HyperSeg framework lies in its ability to perform a broad spectrum of visual segmentation tasks, encompassing generic segmentation tasks like panoptic segmentation and more nuanced and complex reasoning perception tasks. This innovation is pivotal given the limitations of previous models in adapting to both image and video scenarios and their inadequate handling of complex reasoning segmentation tasks.

HyperSeg is distinguished as the first VLLM-based universal segmentation model to integrate perception and complex reasoning across both image and video domains. To achieve this, the paper outlines three critical components: Hybrid Entity Recognition, Fine-grained Visual Perceiver (FVP), and Temporal Adapter, each serving a unique role in enhancing the model's comprehension and execution of diverse segmentation tasks.

Novel Model Components

1. Hybrid Entity Recognition: This approach combines the power of generative abilities inherent in VLLMs with a decoding process to enhance mask token comprehension. This dual strategy addresses the limitations found in exclusively generation-based or decode-based methods, allowing for more robust performance in multi-object segmentation scenarios.
2. Fine-grained Visual Perceiver (FVP): The FVP module is designed to merge multi-scale visual features into fixed-length fine-grained tokens, integrating rich visual details from the hierarchical vision encoder. This design overcomes the constraints of coarse-level features typically extracted from models like CLIP, ensuring that VLLMs are equipped to capture the intricate visual details necessary for fine-grained perception tasks.
3. Temporal Adapter: The temporal adapter extends HyperSeg's capabilities to video segmentation tasks, incorporating techniques for global prompt aggregation and local space-time information injection. This enables the model to process long-term and short-term visual-linguistic information effectively, crucial for understanding temporal sequences in video data.

Evaluation and Performance

The paper provides extensive experimental results demonstrating HyperSeg's efficacy across various challenging segmentation benchmarks. Notably, it surpasses existing VLLM-based and specialist segmentation models in intricate reasoning segmentation tasks and standard perception tasks. The model's robust performance on common multi-modal benchmarks highlights its adaptability across diverse segmentation tasks, bolstered by multi-task and multi-dataset joint training strategies.

Implications and Future Directions

HyperSeg's development represents a significant advancement in the application of VLLMs for visual perception tasks. Its comprehensive approach not only bridges the gap between image and video domain segmentation but also opens avenues for more sophisticated interaction between visual and language modalities in real-world applications. The exploration into hybrid recognition strategies and the integration of fine-grained and temporal information suggest promising future directions for enhancing model capabilities further.

The implications for AI development are substantial, with potential applications extending into fields requiring detailed scene understanding and interpretation, such as autonomous driving, robotics, and advanced multimedia systems. As research on VLLMs progresses, HyperSeg provides a foundational framework upon which future universal segmentation models can build, potentially incorporating even more advanced reasoning and contextual understanding capabilities.