ExpAlign: Expectation-Guided Vision-Language Alignment for Open-Vocabulary Grounding

• URL: http://arxiv.org/abs/2601.22666v1
• Date: Fri, 30 Jan 2026 07:38:04 GMT
• Title: ExpAlign: Expectation-Guided Vision-Language Alignment for Open-Vocabulary Grounding
• Authors: Junyi Hu, Tian Bai, Fengyi Wu, Wenyan Li, Zhenming Peng, Yi Zhang,
• Abstract summary: Open-vocabulary grounding requires accurate vision-language alignment under weak supervision.<n>We propose ExpAlign, a theoretically grounded vision-language alignment framework built on a principled multiple instance learning formulation.
• Score: 6.310226357092042
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Open-vocabulary grounding requires accurate vision-language alignment under weak supervision, yet existing methods either rely on global sentence embeddings that lack fine-grained expressiveness or introduce token-level alignment with explicit supervision or heavy cross-attention designs. We propose ExpAlign, a theoretically grounded vision-language alignment framework built on a principled multiple instance learning formulation. ExpAlign introduces an Expectation Alignment Head that performs attention-based soft MIL pooling over token-region similarities, enabling implicit token and instance selection without additional annotations. To further stabilize alignment learning, we develop an energy-based multi-scale consistency regularization scheme, including a Top-K multi-positive contrastive objective and a Geometry-Aware Consistency Objective derived from a Lagrangian-constrained free-energy minimization. Extensive experiments show that ExpAlign consistently improves open-vocabulary detection and zero-shot instance segmentation, particularly on long-tail categories. Most notably, it achieves 36.2 AP$_r$ on the LVIS minival split, outperforming other state-of-the-art methods at comparable model scale, while remaining lightweight and inference-efficient.

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