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Found 14 papers, 3 papers with code
Hierarchical Self-supervised Representation Learning for Movie Understanding
Most self-supervised video representation learning approaches focus on action recognition.
What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions
We propose a novel one-stage Transformer-based semantic and spatial refined transformer (SSRT) to solve the Human-Object Interaction detection task, which requires to localize humans and objects, and predicts their interactions.
Id-Free Person Similarity Learning
In this work, we explore training such a model by only using person box annotations, thus removing the necessity of manually labeling a training dataset with additional person identity annotation as these are expensive to collect.
TubeR: Tubelet Transformer for Video Action Detection
We propose TubeR: a simple solution for spatio-temporal video action detection.
Exploiting weakly supervised visual patterns to learn from partial annotations
Ignoring these un-annotated labels result in loss of supervisory signal which reduces the performance of the classification models.
Positive-Congruent Training: Towards Regression-Free Model Updates
Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error.
SurfConv: Bridging 3D and 2D Convolution for RGBD Images
On the other hand, 3D convolution wastes a large amount of memory on mostly unoccupied 3D space, which consists of only the surface visible to the sensor.
Pose Estimation for Objects with Rotational Symmetry
In particular, our aim is to infer poses for objects not seen at training time, but for which their 3D CAD models are available at test time.
Annotating Object Instances with a Polygon-RNN
We show that our approach speeds up the annotation process by a factor of 4. 7 across all classes in Cityscapes, while achieving 78. 4% agreement in IoU with original ground-truth, matching the typical agreement between human annotators.
3D Object Proposals using Stereo Imagery for Accurate Object Class Detection
We then exploit a CNN on top of these proposals to perform object detection.
Monocular 3D Object Detection for Autonomous Driving
The focus of this paper is on proposal generation.
Ranked #8 on
Vehicle Pose Estimation
on KITTI Cars Hard
Exploiting Semantic Information and Deep Matching for Optical Flow
We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving.
3D Object Proposals for Accurate Object Class Detection
The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving.
Ranked #10 on
Vehicle Pose Estimation
on KITTI Cars Hard
Rent3D: Floor-Plan Priors for Monocular Layout Estimation
What sets us apart from past work in layout estimation is the use of floor plans as a source of prior knowledge, as well as localization of each image within a bigger space (apartment).
