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Found 10 papers, 8 papers with code
Objects do not disappear: Video object detection by single-frame object location anticipation
2) Improved efficiency by only doing the expensive feature computations on a small subset of all frames.
Ranked #1 on
Video Object Detection
on Waymo Open Dataset
Convolutional Cross-View Pose Estimation
The Localization Decoder produces a dense probability distribution in a coarse-to-fine manner with a novel Localization Matching Upsampling module.
Visual Cross-View Metric Localization with Dense Uncertainty Estimates
Given a ground-level color image and a satellite patch that contains the local surroundings, the task is to identify the location of the ground camera within the satellite patch.
BoxeR: Box-Attention for 2D and 3D Transformers
Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map.
No frame left behind: Full Video Action Recognition
A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action.
Adversarial Self-Supervised Scene Flow Estimation
This work proposes a metric learning approach for self-supervised scene flow estimation.
KPRNet: Improving projection-based LiDAR semantic segmentation
Semantic segmentation is an important component in the perception systems of autonomous vehicles.
Ranked #15 on
3D Semantic Segmentation
on SemanticKITTI
Exploiting Temporality for Semi-Supervised Video Segmentation
In this paper, we tackle the issue of label scarcity by using consecutive frames of a video, where only one frame is annotated.
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation
Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions.
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Convolutional neural networks have been successfully applied to semantic segmentation problems.
Ranked #15 on
Lane Detection
on TuSimple
