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Found 11 papers, 8 papers with code
4D-Former: Multimodal 4D Panoptic Segmentation
4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individual objects to be segmented and tracked over time.
Ranked #1 on
Panoptic Tracking
on Panoptic nuScenes val
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.
Ranked #2 on
Video Panoptic Segmentation
on KITTI-STEP
(using extra training data)
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).
Ranked #4 on
Long-tail Video Object Segmentation
on BURST-val
(using extra training data)
Long-tail Video Object Segmentation
Multi-Object Tracking
+6
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.
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.
D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
We further show that D^2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
We further show that D2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.
Making a Case for 3D Convolutions for Object Segmentation in Videos
On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance.
Ranked #13 on
Unsupervised Video Object Segmentation
on DAVIS 2016 val
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos
In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos.
Ranked #5 on
Unsupervised Video Object Segmentation
on DAVIS 2017 (val)
(using extra training data)
Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification
To this end, tracklet re-identification is performed by utilizing a novel multi-stage deep network that can jointly reason about the visual appearance and spatio-temporal properties of a pair of tracklets, thereby providing a robust measure of affinity.
An Overview of Datatype Quantization Techniques for Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are becoming increasingly popular due to their superior performance in the domain of computer vision, in applications such as objection detection and recognition.
