This work proposes to model objects as a stronger semantic unit for visual prediction, encouraging the encoder to learn the global context and semantics among visual elements, and shows that an object-centric objective reduces pixel-averaging shortcuts and yields more globally coherent and context-consistent representations.
This paper proposes SegAnswer, which shifts the unit of zoom-in from the popular bounding box to pixel-level segmentation mask, and demonstrates its capability for reliable pixel grounding.
Yake Wei, Yuan Wang, Fengyun Rao et al.· arXiv.org· 0 citations
For the coarse attributes the authors study, MLLMs encode the visual evidence but cannot reliably control their reliance on it, indicating that for the coarse attributes they study, MLLMs cannot reliably control their reliance on it.
Jiaang Li, Chengzu Li, Zhaochong An et al.· arXiv.org· 0 citations
This work proposes masked boundary modeling, a self-supervised paradigm that dynamically learns sub-pixel boundary representations and subsequently leverages the discovered boundary-bearing tokens as masked targets to facilitate dense visual token learning.
Zelin Fu, Bin Tan, Chang Sun et al.· 3 citations· ⚡2
This work introduces \method, a Multi-scale Adaptive Vision Encoder, a Multi-scale Adaptive Vision Encoder that uses position-dependent gates to fuse shallow, intermediate, and deep features from a vision Transformer, preserving global semantics while enhancing edges, text, and local structure.
UPrompt is proposed, a U-shaped multi-granularity prompt learning framework for vision-language models that constructs parallel multi-granularity representations in both visual and textual modalities and maintains competitive performance with minimal overhead and matching PSRC with 1/3 cost.
ReVA is proposed, a region-aware VQA model that employs a frozen CLIP ViT-L/14 Vision Transformer (ViT) and a Qwen2.5-7B-Instruct large language model (LLM) connected through a dual bridge that aligns both whole-image and region-level representations with the LLM's embedding space.
A. Senthil· 0 citations
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