Search Results for author:
Found 13 papers, 6 papers with code
Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization
Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
3SD: Self-Supervised Saliency Detection With No Labels
Our method generates and uses pseudo-ground truth labels for training.
Learning a Proposal Classifier for Multiple Object Tracking
In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph.
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context.
Memory Warps for Learning Long-Term Online Video Representations
This paper proposes a novel memory-based online video representation that is efficient, accurate and predictive.
Learning Efficient Object Detection Models with Knowledge Distillation
In this work, we propose a new framework to learn compact and fast ob- ject detection networks with improved accuracy using knowledge distillation [20] and hint learning [34].
Deep Network Flow for Multi-Object Tracking
In this work, we demonstrate that it is possible to learn features for network-flow-based data association via backpropagation, by expressing the optimum of a smoothed network flow problem as a differentiable function of the pairwise association costs.
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i. e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents.
Ranked #1 on
Trajectory Prediction
on PAID
Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers
In this paper, we investigate two new strategies to detect objects accurately and efficiently using deep convolutional neural network: 1) scale-dependent pooling and 2) layer-wise cascaded rejection classifiers.
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
In CNN-based object detection methods, region proposal becomes a bottleneck when objects exhibit significant scale variation, occlusion or truncation.
Ranked #4 on
Vehicle Pose Estimation
on KITTI Cars Hard
Data-Driven 3D Voxel Patterns for Object Category Recognition
Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image.
Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor
In this paper, we focus on the two key aspects of multiple target tracking problem: 1) designing an accurate affinity measure to associate detections and 2) implementing an efficient and accurate (near) online multiple target tracking algorithm.
Ranked #18 on
Multiple Object Tracking
on KITTI Tracking test
Understanding Indoor Scenes Using 3D Geometric Phrases
Visual scene understanding is a difficult problem interleaving object detection, geometric reasoning and scene classification.
Ranked #7 on
Room Layout Estimation
on SUN RGB-D
