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

• URL: http://arxiv.org/abs/2608.16192v1
• Date: Mon, 17 Aug 2026 07:16:37 GMT
• Title: Baseline-Relative Counterfactual Refinement for Bit-Aware Visual Token Communication
• Abstract summary: Gated Counterfactual Refinement for Communication (GCR-C) is a rollout-style correction layer over Local-MDL.<n>GCR-C consistently improves reconstruction quality at active low- and medium-rate operating points without increasing the realized packet rate.<n>Results also reveal a clear quality--computation tradeoff due to the additional encoder-side counterfactual evaluation.
• Score: 10.362447715626388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative visual-token communication reduces transmission load by sending only selected discrete tokens and reconstructing missing content at the receiver. However, existing token-selection criteria based on local uncertainty, importance, or diversity do not directly determine whether changing the current selection improves the final reconstruction under the same packet budget. To address this problem, we propose Gated Counterfactual Refinement for Communication (GCR-C), a rollout-style correction layer over Local-MDL. GCR-C constructs a compact diversified candidate set, evaluates each candidate through matched full-budget Local-MDL continuation, and replaces the baseline action only when a positive baseline-relative reconstruction gain is obtained. Experiments on CIFAR-10, STL-10, a coded 5G-LDPC link, and a limited high-resolution Kodak transfer show that GCR-C consistently improves reconstruction quality at active low- and medium-rate operating points without increasing the realized packet rate, while remaining effective across changes in dataset, channel condition, resolution, token grid, and tokenizer. The results also reveal a clear quality--computation tradeoff due to the additional encoder-side counterfactual evaluation.

Related papers

• Domain-Adaptive Deep Joint Source-Channel Coding for Image Classification [17.84601469865839]
  We study single-source domain adaptation for task-oriented Deep J SCC.<n>We propose a domain-adaptive framework that combines pseudo-label-based class-level adversarial alignment with supervised contrastive learning on confidence-filtered target samples.
  arXiv  Detail & Related papers  (2026-07-31T00:11:16Z)
• JSCGC: Joint Source-Channel-Generation Coding for Wireless Generative Communications [58.67925548779465]
  Joint Source-Channel-Generation Coding (JSCGC) is a generative communication paradigm that replaces the conventional decoder with a generative model at the receiver.<n>JSCGC consistently improves feature-based, semantic-level, and inferenceal quality across diverse channel conditions.
  arXiv  Detail & Related papers  (2026-06-11T03:42:03Z)
• Joint Source-Channel-Generation Coding: From Distortion-oriented Reconstruction to Semantic-consistent Generation [58.67925548779465]
  We propose Joint Source-Channel-Generation Coding (JSCGC), a novel paradigm that shifts the focus from perceptual reconstruction to probabilistic generation.<n>JSCGC improves substantially semantic quality and semantic fidelity, significantly outperforming conventional distortion-oriented J SCC methods.
  arXiv  Detail & Related papers  (2026-01-19T08:12:47Z)
• In-Context Source and Channel Coding [12.557918082908545]
  This paper proposes a receiver-side In-Context Decoding (ICD) framework that enhances SSCC robustness without modifying the transmitter.<n>ICD constructs a confidence-ranked candidate pool via reliability-guided bit flipping, samples a compact yet diverse subset of candidates, and applies an LLM-based arithmetic decoder to obtain both reconstructions and sequence-level log-likelihoods.
  arXiv  Detail & Related papers  (2026-01-15T10:37:57Z)
• Coresets from Trajectories: Selecting Data via Correlation of Loss Differences [14.31847187460321]
  Correlation of Loss Differences (CLD) is a scalable metric for coreset selection.<n>On CIFAR-100 and ImageNet-1k, CLD-based coresets typically outperform or closely match state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-27T19:18:39Z)
• Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints [61.62410595953275]
  Communication-constrained distributed conformal risk control (CD-CRC)<n>CD-CRC is a novel decision-making framework for sensor networks under communication constraints.
  arXiv  Detail & Related papers  (2024-09-12T10:12:43Z)
• Rateless Stochastic Coding for Delay-Constrained Semantic Communication [5.882972817816777]
  We consider the problem of joint source-channel coding for semantic communication from a rateless perspective.<n>We propose a more general communication objective that minimizes the perceptual distance by incorporating a semantic-level reconstruction objective.<n>We show that the proposed rateless distortion coding scheme can achieve variable rates of transmission maintaining an excellent trade-off between distortion and perception.
  arXiv  Detail & Related papers  (2024-06-28T10:27:06Z)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Sequence Transduction with Graph-based Supervision [96.04967815520193]
  We present a new transducer objective function that generalizes the RNN-T loss to accept a graph representation of the labels. We demonstrate that transducer-based ASR with CTC-like lattice achieves better results compared to standard RNN-T.
  arXiv  Detail & Related papers  (2021-11-01T21:51:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.