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Zhijun Zhang

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Preprint Jul 2026

PC-Edit: Prompt-Contrastive Region Discovery and Region-Guided Editing

Replacing an object with one that differs in category or shape requires complete source removal, natural target formation unconstrained by the source silhouette, and preservation of unrelated content. Existing training-free editors either localize edits from terminal predictions under source and target prompts or preserve unrelated content through spatially unselective source-feature reuse without explicit region discovery. Before reaching the terminal predictions, prompt-induced semantic differences undergo additional network transformations that may obscure their spatial localization, reducing localization precision. Spatially unselective feature reuse forces a trade-off between edit completeness and background preservation. Therefore, we propose PC-Edit, a prompt-contrastive framework for training-free MM-DiT editing. PC-Edit contrasts the image-token attention outputs under the source and target prompts, capturing prompt-induced semantic differences directly where text-conditioned information is delivered to image tokens. The same contrast identifies a source-erasure region during inversion and a target-emergence region during denoising. Their union suppresses source remnants while allowing the target object to form naturally. PC-Edit further couples region discovery and background preservation within each sampling step by estimating the current edit region from preceding attention blocks and immediately injecting cached source K/V features outside it in subsequent blocks, thereby protecting unrelated content before the latent update. Experiments on PIE-Bench and our EditRegion-Bench, with human-verified edit-region annotations for single- and multi-object addition and replacement, show that PC-Edit achieves the best editing quality and background preservation among methods without user-specified edit regions.

Jian Zhang, Zhijun Zhang · 0 citations
Preprint Jul 2026

UBG-Net: An Uncertainty-aware Bayesian Gating Network for Robust Audio-Visual Speech Recognition

Audio-Visual speech recognition systems often degrade in real-world scenarios due to signal corruption and distribution shifts. To address this, we propose a unified uncertainty-modeling framework, namely the uncertainty-aware Bayesian gating network (UBG-Net). UBG-Net features a Modality Uncertainty-aware Bayesian Fusion (MUBF) mechanism that injects signal-level aleatoric uncertainty into a Bayesian network to model epistemic uncertainty, thereby ensuring robust fusion of pre-trained backbone features. For inference, we introduce Distribution Uncertainty-aware Hierarchical Voting (DUHV) to select transcripts from Monte Carlo samples, prioritizing frequency and using inference scores in case of a tie. Experiments on the AVCocktail and LRS2 datasets demonstrate the overall superiority of UBG-Net compared to SOTA baselines. Ablation studies confirm that MUBF and DUHV effectively filter noise, enhancing fusion and decoding robustness.

Jinjie Fu, Hang Chen, Wu Guo et al. · 0 citations