Selective Contrastive Learning for Weakly Supervised Affordance Grounding

• URL: http://arxiv.org/abs/2508.07877v1
• Date: Mon, 11 Aug 2025 11:49:37 GMT
• Title: Selective Contrastive Learning for Weakly Supervised Affordance Grounding
• Authors: WonJun Moon, Hyun Seok Seong, Jae-Pil Heo,
• Abstract summary: Weakly supervised affordance grounding seeks to imitate human learning from third-person demonstrations.<n>We introduce selective prototypical and pixel contrastive objectives that adaptively learn affordance-relevant cues at both the part and object levels.
• Score: 21.34662128701812
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Facilitating an entity's interaction with objects requires accurately identifying parts that afford specific actions. Weakly supervised affordance grounding (WSAG) seeks to imitate human learning from third-person demonstrations, where humans intuitively grasp functional parts without needing pixel-level annotations. To achieve this, grounding is typically learned using a shared classifier across images from different perspectives, along with distillation strategies incorporating part discovery process. However, since affordance-relevant parts are not always easily distinguishable, models primarily rely on classification, often focusing on common class-specific patterns that are unrelated to affordance. To address this limitation, we move beyond isolated part-level learning by introducing selective prototypical and pixel contrastive objectives that adaptively learn affordance-relevant cues at both the part and object levels, depending on the granularity of the available information. Initially, we find the action-associated objects in both egocentric (object-focused) and exocentric (third-person example) images by leveraging CLIP. Then, by cross-referencing the discovered objects of complementary views, we excavate the precise part-level affordance clues in each perspective. By consistently learning to distinguish affordance-relevant regions from affordance-irrelevant background context, our approach effectively shifts activation from irrelevant areas toward meaningful affordance cues. Experimental results demonstrate the effectiveness of our method. Codes are available at github.com/hynnsk/SelectiveCL.

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