Inserting cross-attention layers into a pretrained language model and conditioning them on point cloud features yields substantially higher 3D affordance detection performance on 3D AffordanceNet than cosine-similarity baselines.
InstructBLIP 2: Extending Vision- Language Models with Fine-Grained Instruction Tun- ing,
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Capturing Fine-Grained Alignments Improves 3D Affordance Detection
Inserting cross-attention layers into a pretrained language model and conditioning them on point cloud features yields substantially higher 3D affordance detection performance on 3D AffordanceNet than cosine-similarity baselines.