ConClick: Element-Wise Contrastive Learning Improves GUI Grounding

ConClick introduces a contrastive learning framework that improves GUI grounding by teaching models to distinguish target interface elements from plausible distractors. The authors built ConClick-Data, a dataset of 4,292 challenging target-distractor pairs, and trained the model with listwise ranking and pairwise margin objectives, achieving significant accuracy gains on multiple benchmarks. An inference-time strategy called ReClick further boosts performance by revisiting promising regions and comparing competing predictions.

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• agents
• benchmarks