137 citations · 181 across the 7 of their papers we have counts for
12 papers · 1 filter
SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
Lingchen Meng, Shiyi Lan, Hengduo Li +3
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the…
BMB: Balanced Memory Bank for Imbalanced Semi-supervised Learning
Wujian Peng, Zejia Weng, Hengduo Li +1
Exploring a substantial amount of unlabeled data, semi-supervised learning (SSL) boosts the recognition performance when only a limited number of labels are provided. However, trad…
Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors
Zhen Xing, Hengduo Li, Zuxuan Wu +1
The performance of existing single-view 3D reconstruction methods heavily relies on large-scale 3D annotations. However, such annotations are tedious and expensive to collect. Semi…
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
Lingchen Meng, Hengduo Li, Bor-Chun Chen +4
Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of vision tasks recently. While achieving excellent performance…
Efficient Video Transformers with Spatial-Temporal Token Selection
Junke Wang, Xitong Yang, Hengduo Li +3
Video transformers have achieved impressive results on major video recognition benchmarks, which however suffer from high computational cost. In this paper, we present STTS, a toke…
Rethinking Pseudo Labels for Semi-Supervised Object Detection
Hengduo Li, Zuxuan Wu, Abhinav Shrivastava +1
Recent advances in semi-supervised object detection (SSOD) are largely driven by consistency-based pseudo-labeling methods for image classification tasks, producing pseudo labels a…