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Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Model
Yushu Li, Yongyi Su, Adam Goodge +2
Vision-language models (VLMs) have revolutionized machine learning by leveraging large pre-trained models to tackle various downstream tasks. Although label, training, and data eff…
GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering
Zhihao Liang, Hongdong Li, Kui Jia +2
Recovering the intrinsic physical attributes of a scene from images, generally termed as the inverse rendering problem, has been a central and challenging task in computer vision a…
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
Yongyi Su, Yushu Li, Nanqing Liu +4
Test-time adaptation (TTA) updates the model weights during the inference stage using testing data to enhance generalization. However, this practice exposes TTA to adversarial risk…
Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers
Longkun Zou, Wanru Zhu, Ke Chen +4
Semantic pattern of an object point cloud is determined by its topological configuration of local geometries. Learning discriminative representations can be challenging due to larg…
Towards Human-Level 3D Relative Pose Estimation: Generalizable, Training-Free, with Single Reference
Yuan Gao, Yajing Luo, Junhong Wang +2
Humans can easily deduce the relative pose of a previously unseen object, without labeling or training, given only a single query-reference image pair. This is arguably achieved by…
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection
Yushu Li, Yongyi Su, Xulei Yang +2
Existing test-time adaptation (TTA) approaches often adapt models with the unlabeled testing data stream. A recent attempt relaxed the assumption by introducing limited human annot…