arXiv Computer Vision
Collapse of Patches: Ranking Image Patches for Efficient Visual Modeling
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Channel-wise Vector Quantization
arXiv:2605. 26089v2 Announce Type: replace-cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens.
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Implementation of Quantum Implicit Neural Representation in Deterministic and Probabilistic Autoencoders for Image Reconstruction/Generation Tasks
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Scaling Quantum Machine Learning without Tricks: Full-Resolution and Diverse Image Generation
arXiv:2603. 00233v2 Announce Type: replace-cross Abstract: Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning.
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Patch Rebirth: Fast and Transferable Model Inversion of Vision Transformers
arXiv:2509.23235v3 Announce Type: replace-cross Abstract: Model inversion is a widely adopted technique in data-free learning that reconstructs synthetic inputs from a pretrained model through iterat...
arXiv Computer Vision
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Fast and Accurate Image Restoration and Generation with Rank Enhanced Linear Attention
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arXiv Computer Vision
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Relative Patch Response Learning for Generalizable AI-Generated Image Detection
The paper introduces Relative Patch Response Learning (PRL) to detect AI‑generated images by comparing patch responses across two mixed views of aligned real‑generated image pairs. PRL uses a relative response objective, a reference coherence objective, and an area ranking objective to address contextual shifts caused by self‑attention, providing precise supervision for patch‑level detection. Experiments show PRL outperforms prior methods by 4.3% and 5.9% in average balanced accuracy on standard and in‑the‑wild benchmarks.