arXiv:2406. 00971v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have recently seen significant advancements through integrating with Large Language Models (LLMs).
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2603.29602v2 Announce Type: replace-cross Abstract: Existing multi-turn image editing paradigms are often confined to isolated single-step execution. Due to a lack of context-awareness and clos...
arXiv:2511. 16107v3 Announce Type: replace-cross Abstract: Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training.
arXiv:2607.18227v2 Announce Type: replace Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
arXiv:2606. 08016v1 Announce Type: cross Abstract: Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes.
ReViCo (Real Visual Correction) is a new benchmark that tests Vision Language Models (VLMs) on the task of correcting text errors in real‑world images, requiring deep understanding of visual text and its context. The study evaluates VLMs using both prompt‑based and targeted training approaches, revealing a significant performance gap between current models and humans. The results show that most VLMs struggle to accurately perceive visual text, leading to frequent correction mistakes, thereby underscoring the need for more robust, text‑aware VLMs.
arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.