arXiv 2609.38079 · cs.CV · 2026-09-29
OmniTaskonomy: When Does Visual Generation Improve Visual Understanding?
Jiaxin Ge, Yiming Qin, Ji Xie, Haozhe Jiang, Xiaochuang Han, Junyi Zhang, Andrew Dai, Yinfei Yang 等 16 人
UC Berkeley
在最新榜 #198 本站收录于 2026-10-21
一句话要点
研究视觉生成训练何时能提升视觉理解：配方对时，图生图训练能带来理解任务的增益。
Studies when visual generation training improves understanding, finding image-to-image training helps under the right recipe.
阅读 arXiv 原文 PDF Hugging Face 讨论 代码仓库 项目页
主题与学科
同主题：多模态
- MJev-Spatial: System One Spatial Intelligence via Native Attention Matching ▲560
- LoopVL: Recurrent Visual Intelligence ▲417
- VideoJev: System One Model for Video Understanding and Interaction ▲375
- S-Space: Exploring Spatial Workspace in Multimodal Language Models ▲270
- UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement ▲266
数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-22。
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