Visible watermarks are widely used for image copyright protection, but their removal remains a challenging restoration task due to the diversity of watermarks in color, scale, transparency, and spatial distribution. Existing methods predominantly rely on CNN-based frameworks, where limited receptive fields constrain spatial modeling, and contrastive learning is applied at intermediate feature levels rather than directly supervising the final reconstructed output. In this paper, we propose CRFormer, a single-stage Transformer network for blind visible watermark removal. CRFormer replaces the CNN backbone with a full Transformer to model watermark regions of arbitrary spatial extent. To compensate for the reduced spatial sensitivity of channel-wise attention, we introduce a deformable convolution feed-forward network that restores spatial perception and integrates watermark mask prediction directly into the backbone. We further apply contrastive learning as an output-level regularization, where multi-scale VGG features of the reconstructed image are pulled toward the watermark-free ground truth and pushed away from the watermarked input, providing direct supervision over perceptual reconstruction quality that intermediate-feature decoupling cannot offer. Extensive experiments on multiple public benchmarks demonstrate that CRFormer consistently outperforms existing state-of-the-art methods by a significant margin.
Yun Hao, Jiaju Wu, Rui Sun et al.· Neural Networks· 0 citations
Existing indoor layout generators produce globally plausible layouts yet may retain local violations such as collisions, out-of-bounds placements, obstructed openings, and blocked circulation. Most prior work focuses on full-scene synthesis or scene-level optimization, with limited support for identifying responsible objects and locally repairing affected regions. We present Roomer, a reflective repair framework that casts these violations as sparse, object-grounded repair problems. Roomer encodes layouts as ``RoState''and uses ``RoReview''to bind measured violations to implicated objects. A geometry-conditioned vision-language model planner proposes a structured local edit, while a deterministic solver validates it and generates a finite set of candidate edits when needed. Each candidate is committed only if full-scene verification confirms that it resolves the target violation without new hard violations or broken protected constraints. We train the planner on Roomer-CC, a controlled-corruption dataset that pairs faulty layouts with object-grounded violation evidence and known-feasible inverse StatePatches. Since existing benchmarks rarely assess whether physically valid layouts are usable, we introduce Roomer-Eval to assess distributional quality, physical validity, and practical usability. Experiments show that Roomer repairs residual violations while preserving valid regions, improves physical validity and usability, and transfers across external generators.
Lingwei Dang, Ziyan Qiu, Jiajia Cheng et al.· 0 citations