Beyond Readability: Evaluating Task Information Recoverability
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2609.39142v1 Announce Type: new Abstract: Can structured text replace vision for diagram reasoning? A wrong answer after textualization can arise because the representation omits information th...
Can structured text replace vision for diagram reasoning? A wrong answer after textualization can arise because the representation omits information the question needs, or because the solver fails to...
ZoomCap is a method for fine‑grained captioning that treats missing visual evidence as recoverable information. It uses a draft caption to guide conditional knowledge retrieval, OCR‑aware reading, coverage‑gap recovery, and adaptive visual zooming, focusing deeper crops on complex regions and deriving spatial relations from grounded geometry. The accompanying FactCycle framework evaluates caption claims against the source image and a reconstruction, distinguishing fabrication from generator loss, and the approach shows strong performance gains in both general and medical vision‑language pre‑training.
arXiv:2604.03114v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) may need to forget visual concepts after deployment because of privacy, copyright, licensing, safety, or policy...
arXiv:2607. 25467v1 Announce Type: cross Abstract: Stateful multimodal assistants encode an image once but may answer questions about it many turns later.
arXiv:2607. 12364v1 Announce Type: cross Abstract: EEG-to-image evaluation should distinguish visual fidelity from recoverable meaning.