English(EN)Transferability Between Understanding and Generation in Unified Multimodal Models
SenseNova-Vision 将计算机视觉任务统一为多模态生成 · 跟踪 6 个来源
作者PulseAugur 编辑部·[9 个来源]·
研究人员开发了 SenseNova-Vision，一个统一的多模态模型，它将所有计算机视觉任务视为生成问题。这种方法使用自然语言指令和视觉提示来指定任务，允许模型生成文本、图像或两者的组合。该模型在新创建的 SenseNova-Vision Corpus 上进行了训练，在检测、分割和姿态估计等广泛的视觉任务上的性能与专用系统相当。这项工作表明，统一的多模态生成是一种可扩展的方法，可以将各种计算机视觉能力集成到通用基础模型中，并且该模型和语料库现已公开提供。
AI
We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal model, without task-specific architectures. Under this formulation, SenseNova-Vision uses natural-l…
arXiv:2607.04423v1 Announce Type: cross Abstract: Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains understudied. We investigate $\boldsymbol{\mathsf{transferability}}$ in UMMs: wheth…
A unified multimodal model formulates computer vision tasks as generation problems using natural language and visual prompts, achieving performance comparable to specialized systems across diverse vision tasks.
Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains understudied. We investigate $\boldsymbol{\mathsf{transferability}}$ in UMMs: whether training a capability on one task improves the …
arXiv:2607.08434v1 Announce Type: new Abstract: Current Unified Large Multimodal Models (ULMMs) support interleaved multimodal reasoning through textual reasoning and intermediate visual states, but typically generate each visual state as a full image. This full-image generation …
Current Unified Large Multimodal Models (ULMMs) support interleaved multimodal reasoning through textual reasoning and intermediate visual states, but typically generate each visual state as a full image. This full-image generation paradigm introduces substantial visual-token red…
arXiv:2502.09696v3 Announce Type: replace Abstract: Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they attain high scores on many popular visual benchma…
arXiv:2607.06560v1 Announce Type: new Abstract: We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal model, without task-specific architectures. Under t…
We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal model, without task-specific architectures. Under this formulation, SenseNova-Vision uses natural-l…
