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China Aerospace Science and Industry Corporation

Industry researchasia · cn
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Research library7linked papers
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Selected work

Representative Papers

PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks

Aug 07, 2026

This work addresses the challenge of quantizing spiking neural networks (SNNs) under low-bit constraints, where membrane potentials are typically represented in floating-point format, leading to complex distributions and high sensitivity to threshold perturbations that hinder effective quantization and induce error accumulation. To overcome this, the authors propose a post-training quantization framework that jointly quantizes both weights and recurrent membrane potential states without requiring retraining, applicable to both convolutional SNNs and spiking-driven Transformers. The method introduces a channel-wise uniform scaling bridge to align the scales of membrane potentials and weights and employs a mixed-precision allocation strategy based on neuronal firing activity and quantization sensitivity, optimizing accuracy under an average bit-width budget. Experiments demonstrate that with weights quantized to 4 bits and membrane potentials to approximately 4 bits, the models maintain high accuracy on image classification and semantic segmentation tasks.

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Self-Evolving Agentic Image Restoration via Deliberate Planning and Intuitive Execution

Jun 27, 2026

This work addresses the challenge of real-world image restoration under complex, coupled degradations, where existing agent-based methods struggle to balance exploration and exploitation due to greedy search strategies and suffer from insufficient information utilization and catastrophic forgetting. To overcome these limitations, the paper formulates restoration as a sequential decision-making problem and proposes a self-evolving agent framework grounded in dual-process theory, integrating an intuitive executor with a deliberative planner. The approach introduces a pruning-aware Monte Carlo tree search for long-horizon reasoning and devises a degradation-aware state fingerprint to drive episodic memory, effectively mitigating forgetting and reducing cold-start costs. Evaluated with a no-reference hybrid reward and multimodal large language model–based assessment, the method achieves state-of-the-art perceptual quality and quantitative performance on both synthetic and real-world benchmarks.

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Recent publications

Latest Papers

PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks

Aug 07, 2026

This work addresses the challenge of quantizing spiking neural networks (SNNs) under low-bit constraints, where membrane potentials are typically represented in floating-point format, leading to complex distributions and high sensitivity to threshold perturbations that hinder effective quantization and induce error accumulation. To overcome this, the authors propose a post-training quantization framework that jointly quantizes both weights and recurrent membrane potential states without requiring retraining, applicable to both convolutional SNNs and spiking-driven Transformers. The method introduces a channel-wise uniform scaling bridge to align the scales of membrane potentials and weights and employs a mixed-precision allocation strategy based on neuronal firing activity and quantization sensitivity, optimizing accuracy under an average bit-width budget. Experiments demonstrate that with weights quantized to 4 bits and membrane potentials to approximately 4 bits, the models maintain high accuracy on image classification and semantic segmentation tasks.

0 citationsRead paper

Self-Evolving Agentic Image Restoration via Deliberate Planning and Intuitive Execution

Jun 27, 2026

This work addresses the challenge of real-world image restoration under complex, coupled degradations, where existing agent-based methods struggle to balance exploration and exploitation due to greedy search strategies and suffer from insufficient information utilization and catastrophic forgetting. To overcome these limitations, the paper formulates restoration as a sequential decision-making problem and proposes a self-evolving agent framework grounded in dual-process theory, integrating an intuitive executor with a deliberative planner. The approach introduces a pruning-aware Monte Carlo tree search for long-horizon reasoning and devises a degradation-aware state fingerprint to drive episodic memory, effectively mitigating forgetting and reducing cold-start costs. Evaluated with a no-reference hybrid reward and multimodal large language model–based assessment, the method achieves state-of-the-art perceptual quality and quantitative performance on both synthetic and real-world benchmarks.

0 citationsRead paper