Search Results for author:
Found 7 papers, 4 papers with code
AIR-DA: Adversarial Image Reconstruction for Unsupervised Domain Adaptive Object Detection
This results in an imbalance of the adversarial training between the domain discriminator and the feature extractor.
MATE: Masked Autoencoders are Online 3D Test-Time Learners
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data.
LIMO: Latent Inceptionism for Targeted Molecule Generation
We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted $K_D$ (a measure of binding affinity) of $6 \cdot 10^{-14}$ M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets.
CycDA: Unsupervised Cycle Domain Adaptation from Image to Video
To address these challenges, we propose Cycle Domain Adaptation (CycDA), a cycle-based approach for unsupervised image-to-video domain adaptation by leveraging the joint spatial information in images and videos on the one hand and, on the other hand, training an independent spatio-temporal model to bridge the modality gap.
Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design
The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery.
ECS-Net: Improving Weakly Supervised Semantic Segmentation by Using Connections Between Class Activation Maps
Image-level weakly supervised semantic segmentation is a challenging task.
BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation
The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference.
Ranked #13 on
Real-time Instance Segmentation
on MSCOCO
