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A recent study by LeCun, Xie, and collaborators shows that large‑scale visual self‑supervised learning (Web‑SSL) can match or surpass CLIP on diverse VQA tasks, even without any language supervision, by scaling model size and data volume.
TurboFill introduces a fast image‑inpainting model that trains a repair adapter on a few‑step text‑to‑image diffusion backbone, achieving state‑of‑the‑art results with only four diffusion steps while dramatically reducing computational cost.
NOVA introduces a highly efficient autoregressive video generation framework that eliminates vector quantization, combines frame‑by‑frame causal prediction with set‑by‑set spatial attention, and achieves state‑of‑the‑art quality on VBench and GenEval while offering strong zero‑shot generalization across text‑to‑image and text‑to‑video tasks.
EasyControl introduces a lightweight condition‑injection LoRA module, a position‑aware training paradigm, and causal attention with KV‑cache to enable plug‑and‑play multi‑condition control for DiT models, supporting arbitrary image resolutions while cutting inference latency by up to 30% and preserving high‑quality generation.
VBench-2.0 expands the original VBench suite by introducing six fine‑grained dimensions—Human Fidelity, Controllability, Creativity, Physics, Commonsense, and more—to evaluate not only the visual quality of generated videos but also their intrinsic faithfulness to physical laws, common sense, and narrative coherence, providing open‑source tools, prompts, and human‑aligned metrics for the research community.
MagicColor introduces a novel multi‑instance sketch‑coloring framework that uses a two‑stage self‑play training strategy, instance guidance, and edge‑aware pixel‑level color matching to automatically produce high‑quality, consistent colors for multiple line‑art instances, outperforming prior GAN and diffusion‑based methods.
ContinuousSR introduces a Pixel-to-Gaussian paradigm that models images as continuous Gaussian fields, enabling arbitrary‑scale super‑resolution with 0.9 dB PSNR gains and up to 19.5× faster rendering compared to existing methods.
OmniPaint introduces a unified diffusion‑based framework that achieves physically consistent object removal and insertion by leveraging a pre‑trained FLUX‑1 diffusion prior, a progressive CycleFlow training pipeline, and a novel reference‑free CFD metric for high‑fidelity image editing.
The paper introduces HVI, the first trainable color space for low‑light image enhancement, and a lightweight dual‑branch network CIDNet that jointly models intensity and chromaticity, eliminating color bias and brightness artifacts, achieving state‑of‑the‑art results on ten benchmark datasets with only 1.88 M parameters and 7.57 GFLOPs.
ImageRAG introduces a retrieval‑augmented generation framework that dynamically fetches relevant images to guide diffusion models, dramatically improving the synthesis of rare and fine‑grained concepts across multiple text‑to‑image systems, as demonstrated by extensive quantitative and user studies.
