Context-PIPs: Persistent Independent Particles Demands Context Features

Weikang Bian, Zhaoyang Huang, Xiaoyu Shi, Yitong Dong, Yijin Li +1 more
2/12/2026

No confident code implementation found for this paper. 1 possible match can be shown below.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

100% match2026

RH-BrainFS: Regional Heterogeneous Multimodal Brain Networks Fusion Strategy

100% match1 repo72026

DS-VLM: Diffusion Supervision Vision Language Model

100% match2026

Memorization-Dilation: Modeling Neural Collapse Under Noise

100% match2026

MS-TIP: Imputation Aware Pedestrian Trajectory Prediction

100% match1 repo242026

TD-Paint: Faster Diffusion Inpainting Through Time Aware Pixel Conditioning

100% match1 repo132026

Cite this paper

@article{bian2026contextpips,
  title  = {Context-PIPs: Persistent Independent Particles Demands Context Features},
  author = {Weikang Bian and Zhaoyang Huang and Xiaoyu Shi and Yitong Dong and Yijin Li and Hongsheng Li},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:388ad4c2e1cb47195cad0821572e7a622b362006},
  journal = {NEURIPS 2023 2023}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/388ad4c2e1cb47195cad0821572e7a622b362006.svg)](https://codeofpaper.com/paper/388ad4c2e1cb47195cad0821572e7a622b362006)

Discussion