Search Results for author:
Found 19 papers, 11 papers with code
TarViS: A Unified Approach for Target-based Video Segmentation
A single TarViS model can be trained jointly on a collection of datasets spanning different tasks, and can hot-swap between tasks during inference without any task-specific retraining.
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video
Multiple existing benchmarks involve tracking and segmenting objects in video e. g., Video Object Segmentation (VOS) and Multi-Object Tracking and Segmentation (MOTS), but there is little interaction between them due to the use of disparate benchmark datasets and metrics (e. g. J&F, mAP, sMOTSA).
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+4
Differentiable Soft-Masked Attention
Recently, "Masked Attention" was proposed in which a given object representation only attends to those image pixel features for which the segmentation mask of that object is active.
Forecasting from LiDAR via Future Object Detection
Object detection and forecasting are fundamental components of embodied perception.
Opening Up Open World Tracking
A benchmark that would allow us to perform an apple-to-apple comparison of existing efforts is a crucial first step towards advancing this important research field.
HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images
Existing state-of-the-art methods for Video Object Segmentation (VOS) learn low-level pixel-to-pixel correspondences between frames to propagate object masks across video.
Opening up Open-World Tracking
We hope to open a new front in multi-object tracking research that will hopefully bring us a step closer to intelligent systems that can operate safely in the real world.
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
Multi-Object Tracking (MOT) has been notoriously difficult to evaluate.
UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking
UnOVOST even performs competitively with many semi-supervised video object segmentation algorithms even though it is not given any input as to which objects should be tracked and segmented.
Siam R-CNN: Visual Tracking by Re-Detection
We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking.
Ranked #12 on
Visual Object Tracking
on TrackingNet
Single-Shot Panoptic Segmentation
We present a novel end-to-end single-shot method that segments countable object instances (things) as well as background regions (stuff) into a non-overlapping panoptic segmentation at almost video frame rate.
Track to Reconstruct and Reconstruct to Track
Object tracking and 3D reconstruction are often performed together, with tracking used as input for reconstruction.
BoLTVOS: Box-Level Tracking for Video Object Segmentation
Following this paradigm, we present BoLTVOS (Box-Level Tracking for VOS), which consists of an R-CNN detector conditioned on the first-frame bounding box to detect the object of interest, a temporal consistency rescoring algorithm, and a Box2Seg network that converts bounding boxes to segmentation masks.
One-shot visual object segmentation
Semantic Segmentation
+2
Large-Scale Object Mining for Object Discovery from Unlabeled Video
This paper addresses the problem of object discovery from unlabeled driving videos captured in a realistic automotive setting.
MOTS: Multi-Object Tracking and Segmentation
This paper extends the popular task of multi-object tracking to multi-object tracking and segmentation (MOTS).
Ranked #6 on
Multi-Object Tracking
on MOTS20
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+1
4D Generic Video Object Proposals
Many high-level video understanding methods require input in the form of object proposals.
Towards Large-Scale Video Video Object Mining
We propose to leverage a generic object tracker in order to perform object mining in large-scale unlabeled videos, captured in a realistic automotive setting.
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
We address semi-supervised video object segmentation, the task of automatically generating accurate and consistent pixel masks for objects in a video sequence, given the first-frame ground truth annotations.
One-shot visual object segmentation
Semantic Segmentation
+1
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
We explore object discovery and detector adaptation based on unlabeled video sequences captured from a mobile platform.
