activity
20232025
most citedContrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos

4 citations · 22 across the 14 of their papers we have counts for

collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV2024

DePatch: Towards Robust Adversarial Patch for Evading Person Detectors in the Real World

Jikang Cheng, Ying Zhang, Zhongyuan Wang +2

Recent years have seen an increasing interest in physical adversarial attacks, which aim to craft deployable patterns for deceiving deep neural networks, especially for person dete…

cs.CV2024

TreeSBA: Tree-Transformer for Self-Supervised Sequential Brick Assembly

Mengqi Guo, Chen Li, Yuyang Zhao +1

Inferring step-wise actions to assemble 3D objects with primitive bricks from images is a challenging task due to complex constraints and the vast number of possible combinations.…

cs.CV2024

Q2A: Querying Implicit Fully Continuous Feature Pyramid to Align Features for Medical Image Segmentation

Jiahao Yu, Li Chen

Recent medical image segmentation methods apply implicit neural representation (INR) to the decoder for achieving a continuous coordinate decoding to tackle the drawback of convent…

cs.CV20234 cited

GNeSF: Generalizable Neural Semantic Fields

Hanlin Chen, Chen Li, Mengqi Guo +2

3D scene segmentation based on neural implicit representation has emerged recently with the advantage of training only on 2D supervision. However, existing approaches still require…

cs.CV20232 cited

Calibrating Uncertainty for Semi-Supervised Crowd Counting

Chen Li, Xiaoling Hu, Shahira Abousamra +1

Semi-supervised crowd counting is an important yet challenging task. A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set…

cs.CV20232 cited

Weakly-supervised 3D Pose Transfer with Keypoints

Jinnan Chen, Chen Li, Gim Hee Lee

The main challenges of 3D pose transfer are: 1) Lack of paired training data with different characters performing the same pose; 2) Disentangling pose and shape information from th…