arXiv Computer Vision
AffineTok: Semantic Affine Consistency for Diffusion-Friendly Visual Tokenizer
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V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising
V-Co investigates visual co-denoising for pixel-space diffusion models, using a unified JiT-based framework to isolate key design choices. The study identifies two essential components: a dual-stream architecture with flexible cross-stream interaction and a perceptual-drifting hybrid loss combined with RMS-based feature rescaling for stronger semantic supervision. Experiments on ImageNet-256 demonstrate that V-Co surpasses baseline pixel-space diffusion and strong prior pixel-diffusion methods at comparable model sizes while requiring fewer training epochs.
arXiv AI
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UniSpace: Unified Visual Representation and Scalable Multimodal Modeling
arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.
arXiv Machine Learning
Aug 27
Token-Oriented Semantic Communication with Pretrained Vision Transformers
The paper introduces a token‑oriented semantic communication framework that transmits only task‑relevant image latents instead of full token embeddings, reducing communication cost and improving interoperability. It leverages a spatial alignment between vision transformer patch tokens and learned image compression latents, enabling token‑level relevance estimation and selective transmission. Experiments on ImageNet demonstrate a superior rate–accuracy trade‑off compared to existing semantic communication methods and hand‑crafted codecs.
arXiv Computer Vision
Aug 27
Efficient Training with Foresight: Multi-Token Auxiliary Supervision for Autoregressive Image Generation
The paper introduces MTAR, a training framework for autoregressive image generation that enhances performance through multi-token prediction, token-level contrastive regularization, and semantic dropping. These components address sparse supervision, improve representation discriminability, and accelerate training without affecting inference. On ImageNet, MTAR outperforms LlamaGen with lower FID and faster training, achieving comparable results in only a third of the iterations.
arXiv Computer Vision
Aug 25
From Dense Prediction to Visual Editing: Structured Supervision for Unified Image and Video Creation
arXiv:2608.14740v2 Announce Type: replace Abstract: Unified image and video creation requires a model to follow diverse instructions while preserving identity, geometry, and temporal structure from v...
arXiv Computer Vision
2d ago
Gaussian Core LoRA: Distribution-Aware Dynamic Adaptation for Broad Concept Erasure
arXiv:2609.01433v1 Announce Type: new Abstract: Concept erasure aims to suppress unsafe, privacy-sensitive, or undesirable generations in text-to-image diffusion models while preserving benign semant...