1 code implementation • 4 May 2022 • Samia Shafique, Bailey Kong, Shu Kong, Charless C. Fowlkes
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.
no code implementations • 5 Sep 2021 • Kolby Nottingham, Litian Liang, Daeyun Shin, Charless C. Fowlkes, Roy Fox, Sameer Singh
Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research.
no code implementations • 7 Apr 2020 • Zhe Wang, Daeyun Shin, Charless C. Fowlkes
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)
no code implementations • ICCV 2019 • Phuc Xuan Nguyen, Deva Ramanan, Charless C. Fowlkes
Our approach makes use of two innovations to attention-modeling in weakly-supervised learning.
Action Localization Weakly Supervised Action Localization +1
no code implementations • ICCV 2019 • Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image.
no code implementations • CVPR 2018 • Daeyun Shin, Charless C. Fowlkes, Derek Hoiem
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.
1 code implementation • 6 Apr 2018 • Bailey Kong, James Supancic, Deva Ramanan, Charless C. Fowlkes
We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene.
no code implementations • 4 Oct 2017 • Bailey Kong, Charless C. Fowlkes
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).
no code implementations • ICCV 2017 • Minhaeng Lee, Charless C. Fowlkes
This paper addresses the problem of building a spatio-temporal model of the world from a stream of time-stamped data.
no code implementations • 6 Dec 2016 • Raúl Díaz, Charless C. Fowlkes
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.
no code implementations • 5 Oct 2016 • Shaofei Wang, Charless C. Fowlkes
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.
1 code implementation • 8 May 2016 • Golnaz Ghiasi, Charless C. Fowlkes
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
no code implementations • 14 Jul 2015 • Raúl Díaz, Minhaeng Lee, Jochen Schubert, Charless C. Fowlkes
Contextual information can have a substantial impact on the performance of visual tasks such as semantic segmentation, object detection, and geometric estimation.
no code implementations • 9 Jul 2015 • Julian Yarkony, Charless C. Fowlkes
We study the problem of hierarchical clustering on planar graphs.
2 code implementations • 28 Jun 2015 • Golnaz Ghiasi, Charless C. Fowlkes
The presence of occluders significantly impacts object recognition accuracy.
no code implementations • CVPR 2015 • Sam Hallman, Charless C. Fowlkes
We present a simple, efficient model for learning boundary detection based on a random forest classifier.
no code implementations • 5 Dec 2014 • Shaofei Wang, Charless C. Fowlkes
We describe a model for multi-target tracking based on associating collections of candidate detections across frames of a video.
no code implementations • CVPR 2014 • Golnaz Ghiasi, Yi Yang, Deva Ramanan, Charless C. Fowlkes
Occlusion poses a significant difficulty for object recognition due to the combinatorial diversity of possible occlusion patterns.
no code implementations • CVPR 2014 • Golnaz Ghiasi, Charless C. Fowlkes
The presence of occluders significantly impacts performance of systems for object recognition.
no code implementations • NeurIPS 2009 • Hamed Pirsiavash, Deva Ramanan, Charless C. Fowlkes
Bilinear classifiers are a discriminative variant of bilinear models, which capture the dependence of data on multiple factors.