Search Results for author:
Found 11 papers, 3 papers with code
ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization
Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images.
SiLK -- Simple Learned Keypoints
Keypoint detection & descriptors are foundational tech-nologies for computer vision tasks like image matching, 3D reconstruction and visual odometry.
Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision
Many top-down architectures for instance segmentation achieve significant success when trained and tested on pre-defined closed-world taxonomy.
SiLK: Simple Learned Keypoints
Keypoint detection & descriptors are foundational technologies for computer vision tasks like image matching, 3D reconstruction and visual odometry.
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild
Recent advances in self-supervised learning (SSL) using large models to learn visual representations from natural images are rapidly closing the gap between the results produced by fully supervised learning and those produced by SSL on downstream vision tasks.
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity
From PA we construct a large set of pseudo-ground-truth instance masks; combined with human-annotated instance masks we train GGNs and significantly outperform the SOTA on open-world instance segmentation on various benchmarks including COCO, LVIS, ADE20K, and UVO.
Pediatric Otoscopy Video Screening with Shift Contrastive Anomaly Detection
Our method achieves an AUROC of 88. 0% on the patient-level and also outperforms the average of a group of 25 clinicians in a comparative study, which is the largest of such published to date.
Learn Proportional Derivative Controllable Latent Space from Pixels
Recent advances in latent space dynamics model from pixels show promising progress in vision-based model predictive control (MPC).
Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation
Current state-of-the-art object detection and segmentation methods work well under the closed-world assumption.
Generic Event Boundary Detection: A Benchmark for Event Segmentation
This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks.
Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation
We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori, and then adapt it to the task of category-independent articulated object pose estimation.
