7 citations · 10 across the 4 of their papers we have counts for
6 papers · 1 filter
Get the Best of Both Worlds: Improving Accuracy and Transferability by Grassmann Class Representation
Haoqi Wang, Zhizhong Li, Wayne Zhang
We generalize the class vectors found in neural networks to linear subspaces (i.e.~points in the Grassmann manifold) and show that the Grassmann Class Representation (GCR) enables…
Class-Incremental Learning with Strong Pre-trained Models
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li +4
Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world…
ViM: Out-Of-Distribution with Virtual-logit Matching
Haoqi Wang, Zhizhong Li, Litong Feng +1
Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversit…
Task-Assisted Domain Adaptation with Anchor Tasks
Zhizhong Li, Linjie Luo, Sergey Tulyakov +2
Some tasks, such as surface normals or single-view depth estimation, require per-pixel ground truth that is difficult to obtain on real images but easy to obtain on synthetic. Howe…
Complete 3D Scene Parsing from an RGBD Image
Chuhang Zou, Ruiqi Guo, Zhizhong Li +1
One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our…
Learning without Forgetting
Zhizhong Li, Derek Hoiem
When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as th…