CAPE: A CLIP-Aware Pointing Ensemble of Complementary Heatmap Cues for Embodied Reference Understanding

• URL: http://arxiv.org/abs/2507.21888v1
• Date: Tue, 29 Jul 2025 15:00:21 GMT
• Title: CAPE: A CLIP-Aware Pointing Ensemble of Complementary Heatmap Cues for Embodied Reference Understanding
• Authors: Fevziye Irem Eyiokur, Dogucan Yaman, Hazım Kemal Ekenel, Alexander Waibel,
• Abstract summary: Embodied Reference Understanding involves predicting the object that a person in the scene is referring to through both pointing gesture and language.<n>We propose a dual-model framework, where one model learns from the head-to-fingertip direction and the other from the wrist-to-fingertip direction.<n>We present the CLIP-Aware Pointing Ensemble module, which performs a hybrid ensemble based on CLIP features.
• Score: 55.33317649771575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of Embodied Reference Understanding, which involves predicting the object that a person in the scene is referring to through both pointing gesture and language. Accurately identifying the referent requires multimodal understanding: integrating textual instructions, visual pointing, and scene context. However, existing methods often struggle to effectively leverage visual clues for disambiguation. We also observe that, while the referent is often aligned with the head-to-fingertip line, it occasionally aligns more closely with the wrist-to-fingertip line. Therefore, relying on a single line assumption can be overly simplistic and may lead to suboptimal performance. To address this, we propose a dual-model framework, where one model learns from the head-to-fingertip direction and the other from the wrist-to-fingertip direction. We further introduce a Gaussian ray heatmap representation of these lines and use them as input to provide a strong supervisory signal that encourages the model to better attend to pointing cues. To combine the strengths of both models, we present the CLIP-Aware Pointing Ensemble module, which performs a hybrid ensemble based on CLIP features. Additionally, we propose an object center prediction head as an auxiliary task to further enhance referent localization. We validate our approach through extensive experiments and analysis on the benchmark YouRefIt dataset, achieving an improvement of approximately 4 mAP at the 0.25 IoU threshold.

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