Search Results for author:
Found 11 papers, 7 papers with code
Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning
We propose KYN, a novel method for single-view scene reconstruction that reasons about semantic and spatial context to predict each point's density.
UniDepth: Universal Monocular Metric Depth Estimation
However, the remarkable accuracy of recent MMDE methods is confined to their training domains.
Ranked #1 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking
Continual learning allows a model to learn multiple tasks sequentially while retaining the old knowledge without the training data of the preceding tasks.
DARTH: Holistic Test-time Adaptation for Multiple Object Tracking
However, the nature of a MOT system is manifold - requiring object detection and instance association - and adapting all its components is non-trivial.
Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts
Thus, we propose to perturb the channel statistics of source domain features to synthesize various latent styles, so that the trained deep model can perceive diverse potential domains and generalizes well even without observations of target domain data in training.
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation
Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems.
Generative Cooperative Learning for Unsupervised Video Anomaly Detection
Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings.
On the Practicality of Deterministic Epistemic Uncertainty
We find that, while DUMs scale to realistic vision tasks and perform well on OOD detection, the practicality of current methods is undermined by poor calibration under distributional shifts.
Out of Distribution (OOD) Detection
Semantic Segmentation
+1
Depth-Aware Action Recognition: Pose-Motion Encoding through Temporal Heatmaps
The key component of our method is the Depth-Aware Pose Motion representation (DA-PoTion), a new video descriptor that encodes the 3D movement of semantic keypoints of the human body.
3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer
Transferring the style from one image onto another is a popular and widely studied task in computer vision.
Batch Normalization Embeddings for Deep Domain Generalization
Several recent methods use multiple datasets to train models to extract domain-invariant features, hoping to generalize to unseen domains.
Ranked #63 on
Domain Generalization
on PACS
