HyperSeg: One model to segment them all - from simple objects to complex video scenes
HyperSeg introduces a universal segmentation model powered by LLMs that can perform both image and video segmentation tasks with complex reasoning capabilities. It addresses the limitations of existing methods by incorporating hybrid entity recognition, fine-grained visual perception, and temporal understanding to achieve superior performance across diverse segmentation tasks.
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https://arxiv.org/abs/2411.17606
🤔 Original Problem:
Current segmentation methods struggle with handling both image and video scenarios while lacking complex reasoning abilities. They fail to accurately understand fine-grained vision-language correlations and cannot adapt well to various challenging instructions.
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🔧 Solution in this Paper:
→ HyperSeg uses a hybrid entity recognition strategy that enhances LLM's semantic recognition capabilities by combining generation and decoding processes.
→ The Fine-grained Visual Perceiver (FVP) module merges multi-scale visual features into fixed-length fine-grained tokens, enabling rich visual detail extraction.
→ A temporal adapter module enables comprehensive video understanding through global prompt aggregation and local space-time information injection.
→ The model supports both text prompts (class names, reasoning questions) and visual prompts (box, mask) in a unified format.
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💡 Key Insights:
→ Combining LLM's generative abilities with mask token decoding improves multi-object segmentation
→ Fine-grained visual perception at multiple scales is crucial for detailed segmentation
→ Temporal understanding requires both long-term and short-term vision-language information fusion
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📊 Results:
→ Achieved superior performance on multiple segmentation benchmarks with a single model
→ Demonstrated excellent capabilities in both generic and complex reasoning tasks
→ Outperformed previous methods in video perception tasks requiring temporal understanding
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