no code implementations • 31 Oct 2024 • Shreelekha Revankar, Cheng Perng Phoo, Utkarsh Mall, Bharath Hariharan, Kavita Bala
depends strongly on the spatial scale of the concept and therefore the resolution of the images.
1 code implementation • 31 Oct 2024 • Hangyu Zhou, Chia-Hsiang Kao, Cheng Perng Phoo, Utkarsh Mall, Bharath Hariharan, Kavita Bala
Clouds in satellite imagery pose a significant challenge for downstream applications.
no code implementations • 3 Oct 2024 • Jinsu Yoo, Zhenyang Feng, Tai-Yu Pan, Yihong Sun, Cheng Perng Phoo, Xiangyu Chen, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao
We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector.
1 code implementation • 30 Sep 2024 • Chia-Hsiang Kao, Bharath Hariharan
Our work presents a direction for biologically inspired and plausible learning algorithms, offering an alternative mechanism of learning and adaptation in neural networks.
no code implementations • 23 Sep 2024 • Zilu Li, Guandao Yang, Qingqing Zhao, Xi Deng, Leonidas Guibas, Bharath Hariharan, Gordon Wetzstein
This paper proposes a novel approach to construct learnable parametric control variates functions from arbitrary neural network architectures.
no code implementations • 26 Jul 2024 • Gemmechu Hassena, Jonathan Moon, Ryan Fujii, Andrew Yuen, Noah Snavely, Steve Marschner, Bharath Hariharan
Given posed multi-view images and a set of user-input clicks to prompt segmentation of the individual objects, our method decomposes the scene into separate objects and reconstructs a high-quality 3D surface for each one.
1 code implementation • 17 Jun 2024 • Joseph Tung, Gene Chou, Ruojin Cai, Guandao Yang, Kai Zhang, Gordon Wetzstein, Bharath Hariharan, Noah Snavely
Scene-level novel view synthesis (NVS) is fundamental to many vision and graphics applications.
no code implementations • 25 May 2024 • Xiangyu Chen, Zhenzhen Liu, Katie Z Luo, Siddhartha Datta, Adhitya Polavaram, Yan Wang, Yurong You, Boyi Li, Marco Pavone, Wei-Lun Chao, Mark Campbell, Bharath Hariharan, Kilian Q. Weinberger
Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving.
1 code implementation • 23 May 2024 • Yihong Sun, Bharath Hariharan
As such, prior work has looked at unsupervised instance detection and segmentation, but in the absence of annotated boxes, it is unclear how pixels must be grouped into objects and which objects are of interest.
1 code implementation • 8 Apr 2024 • Yurong You, Cheng Perng Phoo, Carlos Andres Diaz-Ruiz, Katie Z Luo, Wei-Lun Chao, Mark Campbell, Bharath Hariharan, Kilian Q Weinberger
Accurate 3D object detection is crucial to autonomous driving.
no code implementations • 12 Dec 2023 • Utkarsh Mall, Cheng Perng Phoo, Meilin Kelsey Liu, Carl Vondrick, Bharath Hariharan, Kavita Bala
We introduce a method to train vision-language models for remote-sensing images without using any textual annotations.
1 code implementation • 10 Dec 2023 • Aditya Chetan, Guandao Yang, Zichen Wang, Steve Marschner, Bharath Hariharan
Yet in many applications like rendering and simulation, hybrid neural fields can cause noticeable and unreasonable artifacts.
1 code implementation • 23 Oct 2023 • Tai-Yu Pan, Chenyang Ma, Tianle Chen, Cheng Perng Phoo, Katie Z Luo, Yurong You, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train.
no code implementations • 28 Sep 2023 • Luming Tang, Nataniel Ruiz, Qinghao Chu, Yuanzhen Li, Aleksander Holynski, David E. Jacobs, Bharath Hariharan, Yael Pritch, Neal Wadhwa, Kfir Aberman, Michael Rubinstein
Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene.
1 code implementation • 21 Sep 2023 • Travis Zhang, Katie Luo, Cheng Perng Phoo, Yurong You, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
Additionally, we leverage the statistics for a novel self-training process to stabilize the training.
1 code implementation • ICCV 2023 • Ruojin Cai, Joseph Tung, Qianqian Wang, Hadar Averbuch-Elor, Bharath Hariharan, Noah Snavely
Our evaluation shows that our method can distinguish illusory matches in difficult cases, and can be integrated into SfM pipelines to produce correct, disambiguated 3D reconstructions.
1 code implementation • ICCV 2023 • Qianqian Wang, Yen-Yu Chang, Ruojin Cai, Zhengqi Li, Bharath Hariharan, Aleksander Holynski, Noah Snavely
We present a new test-time optimization method for estimating dense and long-range motion from a video sequence.
2 code implementations • NeurIPS 2023 • Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, Bharath Hariharan
We propose a simple strategy to extract this implicit knowledge out of diffusion networks as image features, namely DIffusion FeaTures (DIFT), and use them to establish correspondences between real images.
1 code implementation • ICCV 2023 • Kenneth Borup, Cheng Perng Phoo, Bharath Hariharan
To alleviate this, we propose a weighted multi-source distillation method to distill multiple source models trained on different domains weighted by their relevance for the target task into a single efficient model (named DistillWeighted).
1 code implementation • 27 Mar 2023 • Yurong You, Cheng Perng Phoo, Katie Z Luo, Travis Zhang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts.
1 code implementation • 9 Feb 2023 • Guandao Yang, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie
We use this framework to design Fourier PNFs, which match state-of-the-art performance in signal representation tasks that use neural fields.
1 code implementation • CVPR 2023 • Utkarsh Mall, Bharath Hariharan, Kavita Bala
While a vast amount of spatio-temporal satellite image data is readily available, most of it remains unlabelled.
no code implementations • 23 Sep 2022 • Youya Xia, Josephine Monica, Wei-Lun Chao, Bharath Hariharan, Kilian Q Weinberger, Mark Campbell
In this paper, we investigate the idea of turning sensor inputs (i. e., images) captured in an adverse condition into a benign one (i. e., sunny), upon which the downstream tasks (e. g., semantic segmentation) can attain high accuracy.
no code implementations • CVPR 2022 • Carlos A. Diaz-Ruiz, Youya Xia, Yurong You, Jose Nino, Junan Chen, Josephine Monica, Xiangyu Chen, Katie Luo, Yan Wang, Marc Emond, Wei-Lun Chao, Bharath Hariharan, Kilian Q. Weinberger, Mark Campbell
Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions.
no code implementations • 7 Jul 2022 • Davis Wertheimer, Luming Tang, Bharath Hariharan
Existing approaches generally assume that the shot number at test time is known in advance.
1 code implementation • 14 Apr 2022 • Samar Khanna, Bram Wallace, Kavita Bala, Bharath Hariharan
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions.
2 code implementations • CVPR 2022 • Yurong You, Katie Z Luo, Cheng Perng Phoo, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
Current 3D object detectors for autonomous driving are almost entirely trained on human-annotated data.
6 code implementations • 23 Mar 2022 • Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, Ser-Nam Lim
The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning.
Ranked #2 on Prompt Engineering on ImageNet-21k
1 code implementation • ICLR 2022 • Yurong You, Katie Z Luo, Xiangyu Chen, Junan Chen, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to operate safely.
3 code implementations • ICLR 2022 • Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, William T. Freeman
Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation.
Ranked #6 on Unsupervised Semantic Segmentation on COCO-Stuff-27
no code implementations • 26 Feb 2022 • Vikram Shree, Carlos Diaz-Ruiz, Chang Liu, Bharath Hariharan, Mark Campbell
This paper focuses on the problem of decentralized pedestrian tracking using a sensor network.
1 code implementation • CVPR 2021 • Katie Luo, Guandao Yang, Wenqi Xian, Harald Haraldsson, Bharath Hariharan, Serge Belongie
In applications such as optical see-through and projector augmented reality, producing images amounts to solving non-negative image generation, where one can only add light to an existing image.
1 code implementation • NeurIPS 2021 • Guandao Yang, Serge Belongie, Bharath Hariharan, Vladlen Koltun
Most existing geometry processing algorithms use meshes as the default shape representation.
no code implementations • 29 Sep 2021 • Bharath Hariharan, Cheng Perng Phoo
We present theoretical results showing how these measurements can be used to bound the error of the downstream classifiers, and show empirically that these bounds correlate well with actual downstream performance.
1 code implementation • ICCV 2021 • Utkarsh Mall, Bharath Hariharan, Kavita Bala
Annotating the full set of attributes for a novel category proves to be a tedious and expensive task in deployment.
1 code implementation • CVPR 2021 • Bram Wallace, Ziyang Wu, Bharath Hariharan
The problem of expert model selection deals with choosing the appropriate pretrained network ("expert") to transfer to a target task.
1 code implementation • CVPR 2021 • Ruojin Cai, Bharath Hariharan, Noah Snavely, Hadar Averbuch-Elor
We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap.
2 code implementations • CVPR 2021 • Jang Hyun Cho, Utkarsh Mall, Kavita Bala, Bharath Hariharan
With our novel learning objective, our framework can learn high-level semantic concepts.
Ranked #3 on Unsupervised Semantic Segmentation on COCO-Stuff-171
no code implementations • 26 Mar 2021 • Yurong You, Carlos Andres Diaz-Ruiz, Yan Wang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q Weinberger
Self-driving cars must detect other vehicles and pedestrians in 3D to plan safe routes and avoid collisions.
1 code implementation • ICCV 2021 • Cheng Perng Phoo, Bharath Hariharan
Few-shot learning is based on the premise that labels are expensive, especially when they are fine-grained and require expertise.
1 code implementation • CVPR 2021 • Davis Wertheimer, Luming Tang, Bharath Hariharan
In this paper we reformulate few-shot classification as a reconstruction problem in latent space.
no code implementations • 25 Nov 2020 • Davis Wertheimer, Omid Poursaeed, Bharath Hariharan
We aim to build image generation models that generalize to new domains from few examples.
1 code implementation • ICLR 2021 • Cheng Perng Phoo, Bharath Hariharan
Most few-shot learning techniques are pre-trained on a large, labeled "base dataset".
1 code implementation • ECCV 2020 • Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, Bharath Hariharan
Point cloud generation thus amounts to moving randomly sampled points to high-density areas.
1 code implementation • NeurIPS 2020 • Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values.
Ranked #2 on Stereo Depth Estimation on KITTI2015 (three pixel error metric)
3D Object Detection From Stereo Images Autonomous Driving +5
1 code implementation • CVPR 2020 • Yan Wang, Xiangyu Chen, Yurong You, Li Erran, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike.
1 code implementation • ECCV 2020 • Qianqian Wang, Xiaowei Zhou, Bharath Hariharan, Noah Snavely
Recent research on learned visual descriptors has shown promising improvements in correspondence estimation, a key component of many 3D vision tasks.
5 code implementations • ECCV 2020 • Menglin Jia, Mengyun Shi, Mikhail Sirotenko, Yin Cui, Claire Cardie, Bharath Hariharan, Hartwig Adam, Serge Belongie
In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes).
1 code implementation • ECCV 2020 • Bram Wallace, Bharath Hariharan
There has been little to no work with these methods on other smaller domains, such as satellite, textural, or biological imagery.
1 code implementation • CVPR 2020 • Rui Qian, Divyansh Garg, Yan Wang, Yurong You, Serge Belongie, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
Reliable and accurate 3D object detection is a necessity for safe autonomous driving.
1 code implementation • CVPR 2020 • Luming Tang, Davis Wertheimer, Bharath Hariharan
Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e. g., birds) based on a few images alone.
1 code implementation • 30 Oct 2019 • Brian H. Wang, Wei-Lun Chao, Yan Wang, Bharath Hariharan, Kilian Q. Weinberger, Mark Campbell
We obtain 2-D segmentation predictions by applying Mask-RCNN to the RGB image, and then link this image to a 3-D lidar point cloud by building a graph of connections among 3-D points and 2-D pixels.
2 code implementations • ECCV 2020 • Jong-Chyi Su, Subhransu Maji, Bharath Hariharan
We investigate the role of self-supervised learning (SSL) in the context of few-shot learning.
no code implementations • ICCV 2019 • Jang Hyun Cho, Bharath Hariharan
In this paper, we present a thorough evaluation of the efficacy of knowledge distillation and its dependence on student and teacher architectures.
no code implementations • ICCV 2019 • Bram Wallace, Bharath Hariharan
To address this problem, we present a new model architecture that reframes single-view 3D reconstruction as learnt, category agnostic refinement of a provided, category-specific prior.
1 code implementation • ICCV 2019 • Utkarsh Mall, Kevin Matzen, Bharath Hariharan, Noah Snavely, Kavita Bala
Understanding fashion styles and trends is of great potential interest to retailers and consumers alike.
12 code implementations • ICCV 2019 • Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, Bharath Hariharan
Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.
Ranked #4 on Point Cloud Generation on ShapeNet Car
no code implementations • 17 Jun 2019 • Jong-Chyi Su, Subhransu Maji, Bharath Hariharan
We present a technique to improve the transferability of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions.
1 code implementation • ICLR 2020 • Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation.
3D Object Detection From Stereo Images Autonomous Driving +2
1 code implementation • CVPR 2019 • Davis Wertheimer, Bharath Hariharan
Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones.
2 code implementations • CVPR 2019 • Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference.
3D Object Detection From Stereo Images Autonomous Driving +2
1 code implementation • 24 Oct 2018 • Menglin Jia, Yichen Zhou, Mengyun Shi, Bharath Hariharan
Such information analyzing process is called abstracting, which recognize similarities or differences across all the garments and collections.
no code implementations • 3 Oct 2018 • Omid Poursaeed, Guandao Yang, Aditya Prakash, Qiuren Fang, Hanqing Jiang, Bharath Hariharan, Serge Belongie
Estimating fundamental matrices is a classic problem in computer vision.
1 code implementation • ECCV 2018 • Guandao Yang, Yin Cui, Serge Belongie, Bharath Hariharan
It is expensive to label images with 3D structure or precise camera pose.
1 code implementation • CVPR 2018 • Yan Wang, Lequn Wang, Yurong You, Xu Zou, Vincent Chen, Serena Li, Gao Huang, Bharath Hariharan, Kilian Q. Weinberger
Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about high- and low-level details.
Ranked #12 on Person Re-Identification on CUHK03 detected
1 code implementation • CVPR 2018 • Yu-Xiong Wang, Ross Girshick, Martial Hebert, Bharath Hariharan
Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views.
1 code implementation • CVPR 2018 • Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou
This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time.
5 code implementations • ICCV 2017 • Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick
Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.
Ranked #5 on Visual Question Answering (VQA) on CLEVR-Humans
5 code implementations • CVPR 2017 • Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick
When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings.
1 code implementation • CVPR 2017 • Deepak Pathak, Ross Girshick, Piotr Dollár, Trevor Darrell, Bharath Hariharan
Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed 'pretext' tasks studied in the literature.
85 code implementations • CVPR 2017 • Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie
Feature pyramids are a basic component in recognition systems for detecting objects at different scales.
Ranked #3 on Pedestrian Detection on TJU-Ped-campus
4 code implementations • ICCV 2017 • Bharath Hariharan, Ross Girshick
Low-shot visual learning---the ability to recognize novel object categories from very few examples---is a hallmark of human visual intelligence.
no code implementations • CVPR 2016 • Ke Li, Bharath Hariharan, Jitendra Malik
Existing methods for pixel-wise labelling tasks generally disregard the underlying structure of labellings, often leading to predictions that are visually implausible.
no code implementations • 25 Nov 2015 • Saurabh Gupta, Bharath Hariharan, Jitendra Malik
In this paper we explore two ways of using context for object detection.
1 code implementation • ICCV 2015 • Wei-cheng Kuo, Bharath Hariharan, Jitendra Malik
Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that objectness is in fact a high level construct.
6 code implementations • CVPR 2015 • Bharath Hariharan, Pablo Arbeláez, Ross Girshick, Jitendra Malik
Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as feature representation.
no code implementations • 7 Jul 2014 • Bharath Hariharan, Pablo Arbeláez, Ross Girshick, Jitendra Malik
Unlike classical semantic segmentation, we require individual object instances.
Ranked #5 on Object Detection on PASCAL VOC 2012
no code implementations • 19 Jun 2014 • Georgia Gkioxari, Bharath Hariharan, Ross Girshick, Jitendra Malik
We present convolutional neural networks for the tasks of keypoint (pose) prediction and action classification of people in unconstrained images.
no code implementations • CVPR 2014 • Georgia Gkioxari, Bharath Hariharan, Ross Girshick, Jitendra Malik
A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations.
no code implementations • CVPR 2014 • Bharath Hariharan, C. L. Zitnick, Piotr Dollar
Several popular and effective object detectors separately model intra-class variations arising from deformations and appearance changes.