Search Results for author:
Found 11 papers, 4 papers with code
VRS-NeRF: Visual Relocalization with Sparse Neural Radiance Field
However, in spite of high efficiency, APRs and SCRs have limited accuracy especially in large-scale outdoor scenes; HMs are accurate but need to store a large number of 2D descriptors for matching, resulting in poor efficiency.
Annotation Free Semantic Segmentation with Vision Foundation Models
In this work, we build a lightweight module on top of a self-supervised pretrained vision encoder to align patch features with a pre-trained text encoder.
OOD Aware Supervised Contrastive Learning
Our solution is simple and efficient and acts as a natural extension of the closed-set supervised contrastive representation learning.
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning
In this work, we develop a baseline method, First Session Adaptation (FSA), that sheds light on the efficacy of existing CIL approaches and allows us to assess the relative performance contributions from head and body adaption.
Efficient Large-Scale Localization by Global Instance Recognition
Hierarchical frameworks consisting of both coarse and fine localization are often used as the standard pipeline for large-scale visual localization.
Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate new data.
Multi-Source Unsupervised Domain Adaptation
Object Detection
+1
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation
We surpass camera independent methods on the challenging KITTI3D benchmark and show the key benefits compared to camera dependent methods.
Continual Novelty Detection
This work identifies the crucial link between the two problems and investigates the Novelty Detection problem under the Continual Learning setting.
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers
In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories.
Ranked #1 on
Pedestrian Trajectory Prediction
on Euro-PVI
Road Anomaly Detection by Partial Image Reconstruction With Segmentation Coupling
To that end, we propose a reconstruction module that can be used with many existing semantic segmentation networks, and that is trained to recognize and reconstruct road (drivable) surface from a small bottleneck.
Identifying Wrongly Predicted Samples: A Method for Active Learning
State-of-the-art machine learning models require access to significant amount of annotated data in order to achieve the desired level of performance.
