Selected work

Representative Papers

Baseline-Relative Counterfactual Refinement for Bit-Aware Visual Token Communication

Aug 17, 2026

This study addresses the misalignment between existing visual token selection criteria and reconstruction quality under fixed bandwidth constraints. We propose Gated Counterfactual Rectification (GCR-C), a method that constructs candidate sets and performs full-budget counterfactual evaluations to dynamically replace baseline actions only when positive gains are confirmed. This approach effectively bridges the gap between selection strategies and final reconstruction outcomes. Experiments demonstrate that GCR-C significantly improves reconstruction quality at low-to-medium bitrates across diverse datasets and channel conditions without increasing actual bitrate consumption. Furthermore, the method exhibits robust generalization capabilities, establishing a novel paradigm for communication-aware reconstruction tasks.

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Semantic-Aware Generative Image Transmission for Resource-Constrained Visual IoT Systems

Jun 24, 2026

This work addresses the challenge of balancing semantic fidelity and transmission efficiency in resource-constrained visual Internet-of-Things systems by proposing a semantic-aware generative image transmission framework. The approach integrates instance segmentation–driven semantic scoring with prediction entropy–guided recoverability assessment to intelligently sample discrete VQ tokens, and leverages MaskGIT for reconstructing missing content at the edge or cloud. Spatially dispersed scheduling via Halton sequences is introduced to enhance generation quality. At a bitrate of 0.074 bpp—only 44.6% of that required by DeepJSCC/WITT—the method achieves a PSNR of 29.9 dB, while downstream detection tasks demonstrate that its semantic masking strategy significantly outperforms random masking.

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Recent publications

Latest Papers

Baseline-Relative Counterfactual Refinement for Bit-Aware Visual Token Communication

Aug 17, 2026

This study addresses the misalignment between existing visual token selection criteria and reconstruction quality under fixed bandwidth constraints. We propose Gated Counterfactual Rectification (GCR-C), a method that constructs candidate sets and performs full-budget counterfactual evaluations to dynamically replace baseline actions only when positive gains are confirmed. This approach effectively bridges the gap between selection strategies and final reconstruction outcomes. Experiments demonstrate that GCR-C significantly improves reconstruction quality at low-to-medium bitrates across diverse datasets and channel conditions without increasing actual bitrate consumption. Furthermore, the method exhibits robust generalization capabilities, establishing a novel paradigm for communication-aware reconstruction tasks.

0 citationsRead paper

Semantic-Aware Generative Image Transmission for Resource-Constrained Visual IoT Systems

Jun 24, 2026

This work addresses the challenge of balancing semantic fidelity and transmission efficiency in resource-constrained visual Internet-of-Things systems by proposing a semantic-aware generative image transmission framework. The approach integrates instance segmentation–driven semantic scoring with prediction entropy–guided recoverability assessment to intelligently sample discrete VQ tokens, and leverages MaskGIT for reconstructing missing content at the edge or cloud. Spatially dispersed scheduling via Halton sequences is introduced to enhance generation quality. At a bitrate of 0.074 bpp—only 44.6% of that required by DeepJSCC/WITT—the method achieves a PSNR of 29.9 dB, while downstream detection tasks demonstrate that its semantic masking strategy significantly outperforms random masking.

0 citationsRead paper