M3Grounder sets a new state-of-the-art in grounded DocVQA, advancing from coarse boxes to hierarchical, fine-grained and contextually grounded mask evidence.
This work introduces three tasks/objectives for reverse localization of text as an instruction-tuning mechanism, where the model is guided to extract textual content based on spatial localization cues, thereby enhancing its spatial grounding ability.
The Visually-Guided Disambiguation Aggregation Aggregation (VGD-Agg) framework is proposed, a framework based on a dual-branch fast-slow architecture that enhances discriminability via two learnable tokens and achieves state-of-the-art results on the proposed benchmarks.
Minghang Zheng, Jing Wei, Hong-Yi Yang et al.· 0 citations
CapProbe is introduced, a full-scene dense QA benchmark that turns detailed caption evaluation into region-aligned factual checking and is cost-effective: by converting unconstrained scalar scoring into structured MCQ reading, it reduces open-ended scoring bias while remaining judge-conditioned and yields relatively st...
Mouxiao Huang, Qi Yan, Bo-Rui Jiang et al.· 0 citations
This work proposes a hybrid pipeline coupling T2I generation with context-aware image-to-image (I2I) editing, and introduces VRAIN (Verified Rare-class Augmentation via INstructed editing), a novel I2I editor to strengthen supervision for rare classes.
ST-Evidence is introduced, the first human-verified benchmark for both discriminative and generative pixel-level grounding, and scalable, automated generation pipelines are developed to create ST-Evidence-Instruct, a 160k-scale dataset bridging high-level reasoning with fine-grained grounding.
Shijie Wang, Honglu Zhou, Ziyang Wang et al.· arXiv.org· 0 citations
Modern text-to-image (T2I) models often have similar total scores but different strengths, making practical selection difficult. Fine-grained benchmarks decompose prompts into questions, yet often return them to prompt scores and fixed categories, weakening attribution and ignoring complexity. Related requirements are...
Shao-An Zhao, Fang Zhao, Xueqiang Guo et al.· 0 citations
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