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Found 4 papers, 4 papers with code
FFAVOD: Feature Fusion Architecture for Video Object Detection
We propose FFAVOD, standing for feature fusion architecture for video object detection.
Ranked #2 on
Object Detection
on UA-DETRAC
CenterPoly: real-time instance segmentation using bounding polygons
The models were trained and evaluated on Cityscapes, KITTI and IDD and the results are reported on their public benchmark, which are state-of-the-art at real-time speeds.
Ranked #1 on
Real-time Instance Segmentation
on Cityscapes test
RN-VID: A Feature Fusion Architecture for Video Object Detection
Consecutive frames in a video are highly redundant.
Ranked #4 on
Object Detection
on UAVDT
SpotNet: Self-Attention Multi-Task Network for Object Detection
We use those segmentation maps inside the network as a self-attention mechanism to weight the feature map used to produce the bounding boxes, decreasing the signal of non-relevant areas.
Ranked #3 on
Object Detection
on UA-DETRAC
