Search Results for author:
Found 20 papers, 4 papers with code
Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos
We develop a method termed ShoeRinsics that learns to predict depth by leveraging a mix of fully supervised synthetic data and unsupervised retail image data.
Modular Framework for Visuomotor Language Grounding
Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research.
Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation
Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets.
Ranked #1 on
3D Human Pose Estimation
on Geometric Pose Affordance
(MPJPE metric)
Weakly-supervised Action Localization with Background Modeling
Our approach makes use of two innovations to attention-modeling in weakly-supervised learning.
Action Localization
Weakly Supervised Action Localization
+1
3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers
We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image.
Pixels, voxels, and views: A study of shape representations for single view 3D object shape prediction
The goal of this paper is to compare surface-based and volumetric 3D object shape representations, as well as viewer-centered and object-centered reference frames for single-view 3D shape prediction.
Cross-Domain Image Matching with Deep Feature Maps
We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene.
Energy-Based Spherical Sparse Coding
In this paper, we explore an efficient variant of convolutional sparse coding with unit norm code vectors where reconstruction quality is evaluated using an inner product (cosine distance).
Space-Time Localization and Mapping
This paper addresses the problem of building a spatio-temporal model of the world from a stream of time-stamped data.
Cluster-Wise Ratio Tests for Fast Camera Localization
This approach achieves state-of-the-art camera localization results on a variety of popular benchmarks, outperforming several methods that use more complicated data structures and that make more restrictive assumptions on camera pose.
Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions
In this learning framework, we evaluate two different approaches to finding an optimal set of tracks under a quadratic model objective, one based on an LP relaxation and the other based on novel greedy variants of dynamic programming that handle pairwise interactions.
Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
CNN architectures have terrific recognition performance but rely on spatial pooling which makes it difficult to adapt them to tasks that require dense, pixel-accurate labeling.
Ranked #69 on
Semantic Segmentation
on Cityscapes test
Lifting GIS Maps into Strong Geometric Context for Scene Understanding
Contextual information can have a substantial impact on the performance of visual tasks such as semantic segmentation, object detection, and geometric estimation.
Planar Ultrametric Rounding for Image Segmentation
We study the problem of hierarchical clustering on planar graphs.
Occlusion Coherence: Detecting and Localizing Occluded Faces
The presence of occluders significantly impacts object recognition accuracy.
Oriented Edge Forests for Boundary Detection
We present a simple, efficient model for learning boundary detection based on a random forest classifier.
Learning Multi-target Tracking with Quadratic Object Interactions
We describe a model for multi-target tracking based on associating collections of candidate detections across frames of a video.
Parsing Occluded People
Occlusion poses a significant difficulty for object recognition due to the combinatorial diversity of possible occlusion patterns.
Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model
The presence of occluders significantly impacts performance of systems for object recognition.
Bilinear classifiers for visual recognition
Bilinear classifiers are a discriminative variant of bilinear models, which capture the dependence of data on multiple factors.
