📝 Publications

Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF

Haotian Bai, Yiqi Lin, Yize Chen, Lin Wang

Project | Video |

• A more compact and fertile PlenOctree (POT) NeRF representation.
• Inspiration: POT’s fixed structure for direct optimization is sub-optimal as the scene complexity evolves continuously with updates to cached color and density, necessitating refining the sampling distribution to capture signal complexity accordingly.
• Competitive: DOT outperforms POT by enhancing visual quality, reducing over 55.15/68.84% parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks and Temples.

Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective

Jinjing Zhu*, Haotian Bai^*, Lin Wang

Project | Video |

• Be selected as one of CVPR (highlight) papers(top 2.5%)
• Large Domain Gap: PMTrans bridges source and target domains with an intermediate domain in a relatively smooth way.
• Game Theory: Interpret UDA as a min-max CE game with three players, including the feature extractor, classifier, and PatchMix to find the Nash Equilibria.
• Competitive: PMTrans surpasses ViT-based and CNN-based SoTA methods by +3.6% on Office-Home, +1.4% on Office-31, and +17.7% on DomainNet.

Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration

Haotian Bai, Ruimao Zhang, Jiong Wang, Xiang Wan

Project | Video |

• SCM is the external transformer based solution for Weakly Supervised Object Localization.
• Lightweight: SCM is an external Transformer model that produces no additional parameters.
• Competitive: SCM outperforms most competitive frameworks (CNN & Transformer) using only about 𝟐𝟎%~𝟑𝟎% of their parameters.