CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory
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Cited by 22 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localization. CLIP-Fields learns a mapping from spatial locations to semantic embedding vectors. Importantly, we show that this mapping can be trained with supervision coming only from web-image and web-text trained models such as CLIP, Detic, and Sentence-BERT; and thus uses no direct human supervision. When compared to baselines like Mask-RCNN, our method outperforms on few-shot instance identification or semantic segmentation on the HM3D dataset with only a fraction of the examples. Finally, we show that using CLIP-Fields as a scene memory, robots can perform semantic navigation in real-world environments. Our code and demonstration videos are available here: https://mahis.life/clip-fields
Key facts. Cited by 164 papers in the map (data through 7 Oct 2026). Builds on 32 of its 51 references that are in the map. First posted on arXiv 11 Oct 2022.
Builds on
32 of 51 references.
References · 32
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Citing papers per year: 2022: 2; 2023: 45; 2024: 52; 2025: 40; 2026: 25.
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