Search Results for author: Deva Ramanan

Found 116 papers, 54 papers with code

Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting

1 code implementation25 Feb 2023 Tarasha Khurana, Peiyun Hu, David Held, Deva Ramanan

One promising self-supervised task is 3D point cloud forecasting from unannotated LiDAR sequences.

Motion Planning

3D-aware Conditional Image Synthesis

2 code implementations16 Feb 2023 Kangle Deng, Gengshan Yang, Deva Ramanan, Jun-Yan Zhu

We propose pix2pix3D, a 3D-aware conditional generative model for controllable photorealistic image synthesis.

Image Generation

Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models

1 code implementation16 Jan 2023 Zhiqiu Lin, Samuel Yu, Zhiyi Kuang, Deepak Pathak, Deva Ramanan

By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation.

Audio Classification Few-Shot Learning

Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images

no code implementations10 Jan 2023 Xindi Wu, KwunFung Lau, Francesco Ferroni, Aljoša Ošep, Deva Ramanan

Moreover, we show that our retrieved maps can be used to update or expand existing maps and even show proof-of-concept results for visual localization and image retrieval from spatial graphs.

Autonomous Navigation Cross-Modal Retrieval +3

TarViS: A Unified Approach for Target-based Video Segmentation

no code implementations6 Jan 2023 Ali Athar, Alexander Hermans, Jonathon Luiten, Deva Ramanan, Bastian Leibe

A single TarViS model can be trained jointly on a collection of datasets spanning different tasks, and can hot-swap between tasks during inference without any task-specific retraining.

Instance Segmentation Panoptic Segmentation +5

Far3Det: Towards Far-Field 3D Detection

no code implementations25 Nov 2022 Shubham Gupta, Jeet Kanjani, Mengtian Li, Francesco Ferroni, James Hays, Deva Ramanan, Shu Kong

We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer, e. g., $>$50m.

Autonomous Vehicles Philosophy

Towards Long-Tailed 3D Detection

no code implementations16 Nov 2022 Neehar Peri, Achal Dave, Deva Ramanan, Shu Kong

Moreover, semantic classes are often organized within a hierarchy, e. g., tail classes such as child and construction-worker are arguably subclasses of pedestrian.

Soft Augmentation for Image Classification

no code implementations9 Nov 2022 Yang Liu, Shen Yan, Laura Leal-Taixé, James Hays, Deva Ramanan

We draw inspiration from human visual classification studies and propose generalizing augmentation with invariant transforms to soft augmentation where the learning target softens non-linearly as a function of the degree of the transform applied to the sample: e. g., more aggressive image crop augmentations produce less confident learning targets.

Classification Data Augmentation +1

Learning to Discover and Detect Objects

1 code implementation19 Oct 2022 Vladimir Fomenko, Ismail Elezi, Deva Ramanan, Laura Leal-Taixé, Aljoša Ošep

We then train our network to learn to classify each RoI, either as one of the known classes, seen in the source dataset, or one of the novel classes, with a long-tail distribution constraint on the class assignments, reflecting the natural frequency of classes in the real world.

Novel Class Discovery Object Detection +1

Continual Learning with Evolving Class Ontologies

no code implementations10 Oct 2022 Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang, Deva Ramanan, Shu Kong

LECO requires learning classifiers in distinct time periods (TPs); each TP introduces a new ontology of "fine" labels that refines old ontologies of "coarse" labels (e. g., dog breeds that refine the previous ${\tt dog}$).

Class Incremental Learning Image Classification +3

Differentiable Raycasting for Self-supervised Occupancy Forecasting

1 code implementation4 Oct 2022 Tarasha Khurana, Peiyun Hu, Achal Dave, Jason Ziglar, David Held, Deva Ramanan

Self-supervised representations proposed for large-scale planning, such as ego-centric freespace, confound these two motions, making the representation difficult to use for downstream motion planners.

Autonomous Driving Motion Planning +1

BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video

1 code implementation25 Sep 2022 Ali Athar, Jonathon Luiten, Paul Voigtlaender, Tarasha Khurana, Achal Dave, Bastian Leibe, Deva Ramanan

Multiple existing benchmarks involve tracking and segmenting objects in video e. g., Video Object Segmentation (VOS) and Multi-Object Tracking and Segmentation (MOTS), but there is little interaction between them due to the use of disparate benchmark datasets and metrics (e. g. J&F, mAP, sMOTSA).

Multi-Object Tracking Multi-Object Tracking and Segmentation +4

RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild

1 code implementation11 Aug 2022 Jason Y. Zhang, Deva Ramanan, Shubham Tulsiani

We describe a data-driven method for inferring the camera viewpoints given multiple images of an arbitrary object.

Object Reconstruction

Differentiable Soft-Masked Attention

1 code implementation1 Jun 2022 Ali Athar, Jonathon Luiten, Alexander Hermans, Deva Ramanan, Bastian Leibe

Recently, "Masked Attention" was proposed in which a given object representation only attends to those image pixel features for which the segmentation mask of that object is active.

Semantic Segmentation Video Object Segmentation +2

Long-Tailed Recognition via Weight Balancing

1 code implementation CVPR 2022 Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, Shu Kong

In contrast, weight decay penalizes larger weights more heavily and so learns small balanced weights; the MaxNorm constraint encourages growing small weights within a norm ball but caps all the weights by the radius.

Classification Long-tail Learning

The CLEAR Benchmark: Continual LEArning on Real-World Imagery

1 code implementation17 Jan 2022 Zhiqiu Lin, Jia Shi, Deepak Pathak, Deva Ramanan

The major strength of CLEAR over prior CL benchmarks is the smooth temporal evolution of visual concepts with real-world imagery, including both high-quality labeled data along with abundant unlabeled samples per time period for continual semi-supervised learning.

Continual Learning Image Classification +2

Opening Up Open World Tracking

no code implementations CVPR 2022 Yang Liu, Idil Esen Zulfikar, Jonathon Luiten, Achal Dave, Deva Ramanan, Bastian Leibe, Aljoša Ošep, Laura Leal-Taixé

A benchmark that would allow us to perform an apple-to-apple comparison of existing efforts is a crucial first step towards advancing this important research field.

Multi-Object Tracking

BANMo: Building Animatable 3D Neural Models from Many Casual Videos

1 code implementation CVPR 2022 Gengshan Yang, Minh Vo, Natalia Neverova, Deva Ramanan, Andrea Vedaldi, Hanbyul Joo

Our key insight is to merge three schools of thought; (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) volumetric neural radiance fields (NeRFs) that are amenable to gradient-based optimization, and (3) canonical embeddings that generate correspondences between pixels and an articulated model.

3D Shape Reconstruction 3D Shape Reconstruction from Videos

Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs

1 code implementation CVPR 2022 Haithem Turki, Deva Ramanan, Mahadev Satyanarayanan

We use neural radiance fields (NeRFs) to build interactive 3D environments from large-scale visual captures spanning buildings or even multiple city blocks collected primarily from drones.

HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images

1 code implementation CVPR 2022 Ali Athar, Jonathon Luiten, Alexander Hermans, Deva Ramanan, Bastian Leibe

Existing state-of-the-art methods for Video Object Segmentation (VOS) learn low-level pixel-to-pixel correspondences between frames to propagate object masks across video.

Semantic Segmentation Video Object Segmentation +1

ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction

1 code implementation NeurIPS 2021 Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Ce Liu, Deva Ramanan

The surface embeddings are implemented as coordinate-based MLPs that are fit to each video via consistency and contrastive reconstruction losses. Experimental results show that ViSER compares favorably against prior work on challenging videos of humans with loose clothing and unusual poses as well as animals videos from DAVIS and YTVOS.

3D Shape Reconstruction 3D Shape Reconstruction from Videos +1

NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild

1 code implementation NeurIPS 2021 Jason Y. Zhang, Gengshan Yang, Shubham Tulsiani, Deva Ramanan

NeRS learns a neural shape representation of a closed surface that is diffeomorphic to a sphere, guaranteeing water-tight reconstructions.

3D Reconstruction Neural Rendering

Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories

no code implementations ICCV 2021 Fait Poms, Vishnu Sarukkai, Ravi Teja Mullapudi, Nimit S. Sohoni, William R. Mark, Deva Ramanan, Kayvon Fatahalian

For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs.

Depth-supervised NeRF: Fewer Views and Faster Training for Free

1 code implementation CVPR 2022 Kangle Deng, Andrew Liu, Jun-Yan Zhu, Deva Ramanan

Crucially, SFM also produces sparse 3D points that can be used as "free" depth supervision during training: we add a loss to encourage the distribution of a ray's terminating depth matches a given 3D keypoint, incorporating depth uncertainty.

RGB-D Reconstruction

Opening up Open-World Tracking

no code implementations22 Apr 2021 Yang Liu, Idil Esen Zulfikar, Jonathon Luiten, Achal Dave, Deva Ramanan, Bastian Leibe, Aljoša Ošep, Laura Leal-Taixé

We hope to open a new front in multi-object tracking research that will hopefully bring us a step closer to intelligent systems that can operate safely in the real world.

Multi-Object Tracking

Multimodal Object Detection via Probabilistic Ensembling

2 code implementations7 Apr 2021 Yi-Ting Chen, Jinghao Shi, Zelin Ye, Christoph Mertz, Deva Ramanan, Shu Kong

Object detection with multimodal inputs can improve many safety-critical systems such as autonomous vehicles (AVs).

Autonomous Vehicles object-detection +1

Streaming Self-Training via Domain-Agnostic Unlabeled Images

no code implementations7 Apr 2021 Zhiqiu Lin, Deva Ramanan, Aayush Bansal

We present streaming self-training (SST) that aims to democratize the process of learning visual recognition models such that a non-expert user can define a new task depending on their needs via a few labeled examples and minimal domain knowledge.

Fine-Grained Image Classification Semantic Segmentation +1

OpenGAN: Open-Set Recognition via Open Data Generation

1 code implementation ICCV 2021 Shu Kong, Deva Ramanan

However, the former generalizes poorly to diverse open test data due to overfitting to the training outliers, which are unlikely to exhaustively span the open-world.

Open Set Learning

Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos

no code implementations31 Mar 2021 Kevin Wang, Deva Ramanan, Aayush Bansal

Associating latent codes of a video and manifold projection enables users to make desired edits.

Denoising Super-Resolution

Learning to Segment Rigid Motions from Two Frames

1 code implementation CVPR 2021 Gengshan Yang, Deva Ramanan

Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performance to appearance-based ones, due to noisy motion estimations and degenerate motion configurations.

Motion Segmentation Scene Flow Estimation

Learning Rare Category Classifiers on a Tight Labeling Budget

no code implementations ICCV 2021 Ravi Teja Mullapudi, Fait Poms, William R. Mark, Deva Ramanan, Kayvon Fatahalian

In this paper, we consider the scenario where we start with as-little-as five labeled positives of a rare category and a large amount of unlabeled data of which 99. 9% of it is negatives.

Active Learning Representation Learning

An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines

no code implementations1 Jan 2021 Shu Kong, Deva Ramanan

Machine-learned safety-critical systems need to be self-aware and reliably know their unknowns in the open-world.

Open Set Learning Outlier Detection +2

Detecting Invisible People

1 code implementation ICCV 2021 Tarasha Khurana, Achal Dave, Deva Ramanan

We demonstrate that current detection and tracking systems perform dramatically worse on this task.

Monocular Depth Estimation object-detection +2

Background Splitting: Finding Rare Classes in a Sea of Background

1 code implementation CVPR 2021 Ravi Teja Mullapudi, Fait Poms, William R. Mark, Deva Ramanan, Kayvon Fatahalian

We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories.

Image Classification

What-If Motion Prediction for Autonomous Driving

1 code implementation24 Aug 2020 Siddhesh Khandelwal, William Qi, Jagjeet Singh, Andrew Hartnett, Deva Ramanan

Forecasting the long-term future motion of road actors is a core challenge to the deployment of safe autonomous vehicles (AVs).

Autonomous Driving motion prediction

Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild

1 code implementation ECCV 2020 Jason Y. Zhang, Sam Pepose, Hanbyul Joo, Deva Ramanan, Jitendra Malik, Angjoo Kanazawa

We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment.

3D Human Pose Estimation 3D Shape Reconstruction From A Single 2D Image +2

4D Visualization of Dynamic Events from Unconstrained Multi-View Videos

no code implementations CVPR 2020 Aayush Bansal, Minh Vo, Yaser Sheikh, Deva Ramanan, Srinivasa Narasimhan

We present a data-driven approach for 4D space-time visualization of dynamic events from videos captured by hand-held multiple cameras.

Towards Streaming Perception

1 code implementation ECCV 2020 Mengtian Li, Yu-Xiong Wang, Deva Ramanan

While past work has studied the algorithmic trade-off between latency and accuracy, there has not been a clear metric to compare different methods along the Pareto optimal latency-accuracy curve.

Instance Segmentation Motion Forecasting +5

CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning

no code implementations ICLR 2020 Rohit Girdhar, Deva Ramanan

In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved.

Video Understanding

Learning Generative Models of Tissue Organization with Supervised GANs

1 code implementation31 Mar 2020 Ligong Han, Robert F. Murphy, Deva Ramanan

A key step in understanding the spatial organization of cells and tissues is the ability to construct generative models that accurately reflect that organization.

Image Generation

Unsupervised Audiovisual Synthesis via Exemplar Autoencoders

1 code implementation ICLR 2021 Kangle Deng, Aayush Bansal, Deva Ramanan

We present an unsupervised approach that converts the input speech of any individual into audiovisual streams of potentially-infinitely many output speakers.

Learning to Move with Affordance Maps

1 code implementation ICLR 2020 William Qi, Ravi Teja Mullapudi, Saurabh Gupta, Deva Ramanan

In this paper, we combine the best of both worlds with a modular approach that learns a spatial representation of a scene that is trained to be effective when coupled with traditional geometric planners.

Autonomous Navigation Navigate +1

What You See is What You Get: Exploiting Visibility for 3D Object Detection

1 code implementation CVPR 2020 Peiyun Hu, Jason Ziglar, David Held, Deva Ramanan

On the NuScenes 3D detection benchmark, we show that, by adding an additional stream for visibility input, we can significantly improve the overall detection accuracy of a state-of-the-art 3D detector.

3D Object Detection Data Augmentation +1

Learning to Optimally Segment Point Clouds

no code implementations10 Dec 2019 Peiyun Hu, David Held, Deva Ramanan

We prove that if we score a segmentation by the worst objectness among its individual segments, there is an efficient algorithm that finds the optimal worst-case segmentation among an exponentially large number of candidate segmentations.

Instance Segmentation Semantic Segmentation

Volumetric Correspondence Networks for Optical Flow

2 code implementations NeurIPS 2019 Gengshan Yang, Deva Ramanan

As a result, SOTA networks also employ various heuristics designed to limit volumetric processing, leading to limited accuracy and overfitting.

Optical Flow Estimation

Are we asking the right questions in MovieQA?

no code implementations8 Nov 2019 Bhavan Jasani, Rohit Girdhar, Deva Ramanan

Joint vision and language tasks like visual question answering are fascinating because they explore high-level understanding, but at the same time, can be more prone to language biases.

Question Answering Visual Question Answering

Argoverse: 3D Tracking and Forecasting with Rich Maps

2 code implementations CVPR 2019 Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, James Hays

In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.

3D Object Tracking Autonomous Vehicles +3

Learning to Track Any Object

no code implementations25 Oct 2019 Achal Dave, Pavel Tokmakov, Cordelia Schmid, Deva Ramanan

Moreover, at test time the same network can be applied to detection and tracking, resulting in a unified approach for the two tasks.

Instance Segmentation Object Tracking +4

MetaPix: Few-Shot Video Retargeting

no code implementations ICLR 2020 Jessica Lee, Deva Ramanan, Rohit Girdhar

We address the task of unsupervised retargeting of human actions from one video to another.


CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning

1 code implementation10 Oct 2019 Rohit Girdhar, Deva Ramanan

In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved.

Video Object Tracking Video Understanding

Growing a Brain: Fine-Tuning by Increasing Model Capacity

no code implementations CVPR 2017 Yu-Xiong Wang, Deva Ramanan, Martial Hebert

One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset.

Developmental Learning

Shapes and Context: In-the-Wild Image Synthesis & Manipulation

no code implementations CVPR 2019 Aayush Bansal, Yaser Sheikh, Deva Ramanan

We introduce a data-driven approach for interactively synthesizing in-the-wild images from semantic label maps.

Image Generation

Do Image Classifiers Generalize Across Time?

1 code implementation ICCV 2021 Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points.

General Classification Video Object Detection

Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints

1 code implementation ICLR 2020 Mengtian Li, Ersin Yumer, Deva Ramanan

We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.

General Classification Image Classification +6

Towards Segmenting Anything That Moves

no code implementations11 Feb 2019 Achal Dave, Pavel Tokmakov, Deva Ramanan

To address this concern, we propose two new benchmarks for generic, moving object detection, and show that our model matches top-down methods on common categories, while significantly out-performing both top-down and bottom-up methods on never-before-seen categories.

Action Detection Instance Segmentation +7

DistInit: Learning Video Representations Without a Single Labeled Video

no code implementations ICCV 2019 Rohit Girdhar, Du Tran, Lorenzo Torresani, Deva Ramanan

In this work, we propose an alternative approach to learning video representations that require no semantically labeled videos and instead leverages the years of effort in collecting and labeling large and clean still-image datasets.

Ranked #69 on Action Recognition on HMDB-51 (using extra training data)

Action Recognition Temporal Action Localization +1

Photo-Sketching: Inferring Contour Drawings from Images

3 code implementations2 Jan 2019 Mengtian Li, Zhe Lin, Radomir Mech, Ersin Yumer, Deva Ramanan

Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.

Boundary Detection BSDS500

Online Model Distillation for Efficient Video Inference

1 code implementation ICCV 2019 Ravi Teja Mullapudi, Steven Chen, Keyi Zhang, Deva Ramanan, Kayvon Fatahalian

Rather than learn a specialized student model on offline data from the video stream, we train the student in an online fashion on the live video, intermittently running the teacher to provide a target for learning.

Semantic Segmentation Video Object Segmentation +1

Few-Shot Human Motion Prediction via Meta-Learning

no code implementations ECCV 2018 Liang-Yan Gui, Yu-Xiong Wang, Deva Ramanan, Jose M. F. Moura

This paper addresses the problem of few-shot human motion prediction, in the spirit of the recent progress on few-shot learning and meta-learning.

Few-Shot Learning Human motion prediction +1

Recycle-GAN: Unsupervised Video Retargeting

1 code implementation ECCV 2018 Aayush Bansal, Shugao Ma, Deva Ramanan, Yaser Sheikh

We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i. e., if contents of John Oliver's speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert's style.

Face to Face Translation Translation +1

Cross-Domain Image Matching with Deep Feature Maps

1 code implementation6 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.

Image Retrieval Retrieval

Active Learning with Partial Feedback

1 code implementation ICLR 2019 Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan

While many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one among many classes), and the form of an annotation (typically yes/no binary feedback).

Active Learning

Brute-Force Facial Landmark Analysis With A 140,000-Way Classifier

no code implementations6 Feb 2018 Mengtian Li, Laszlo Jeni, Deva Ramanan

While most prior work treats this as a regression problem, we instead formulate it as a discrete $K$-way classification task, where a classifier is trained to return one of $K$ discrete alignments.

General Classification regression

Learning to Model the Tail

no code implementations NeurIPS 2017 Yu-Xiong Wang, Deva Ramanan, Martial Hebert

We cast this problem as transfer learning, where knowledge from the data-rich classes in the head of the distribution is transferred to the data-poor classes in the tail.

Image Classification Transfer Learning

Patch Correspondences for Interpreting Pixel-level CNNs

no code implementations29 Nov 2017 Victor Fragoso, Chunhui Liu, Aayush Bansal, Deva Ramanan

We present compositional nearest neighbors (CompNN), a simple approach to visually interpreting distributed representations learned by a convolutional neural network (CNN) for pixel-level tasks (e. g., image synthesis and segmentation).

Image-to-Image Translation Semantic Segmentation +1

Attentional Pooling for Action Recognition

1 code implementation NeurIPS 2017 Rohit Girdhar, Deva Ramanan

We introduce a simple yet surprisingly powerful model to incorporate attention in action recognition and human object interaction tasks.

Action Recognition Human-Object Interaction Detection +1

PixelNN: Example-based Image Synthesis

1 code implementation ICLR 2018 Aayush Bansal, Yaser Sheikh, Deva Ramanan

We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an "incomplete" signal such as a low-resolution image, a surface normal map, or edges.

Image Generation

Learning Policies for Adaptive Tracking with Deep Feature Cascades

no code implementations ICCV 2017 Chen Huang, Simon Lucey, Deva Ramanan

Our fundamental insight is to take an adaptive approach, where easy frames are processed with cheap features (such as pixel values), while challenging frames are processed with invariant but expensive deep features.

Decision Making Visual Object Tracking

Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset

no code implementations22 Jul 2017 Zachary Pezzementi, Trenton Tabor, Peiyun Hu, Jonathan K. Chang, Deva Ramanan, Carl Wellington, Benzun P. Wisely Babu, Herman Herman

Person detection from vehicles has made rapid progress recently with the advent of multiple highquality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments.

Human Detection Pedestrian Detection

Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning

no code implementations ICCV 2017 James Steven Supancic III, Deva Ramanan

We formulate tracking as an online decision-making process, where a tracking agent must follow an object despite ambiguous image frames and a limited computational budget.

Decision Making reinforcement Learning

Predictive-Corrective Networks for Action Detection

no code implementations CVPR 2017 Achal Dave, Olga Russakovsky, Deva Ramanan

While deep feature learning has revolutionized techniques for static-image understanding, the same does not quite hold for video processing.

Action Detection Optical Flow Estimation +1

ActionVLAD: Learning spatio-temporal aggregation for action classification

no code implementations CVPR 2017 Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, Bryan Russell

In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video.

Action Classification Classification +2

Need for Speed: A Benchmark for Higher Frame Rate Object Tracking

1 code implementation ICCV 2017 Hamed Kiani Galoogahi, Ashton Fagg, Chen Huang, Deva Ramanan, Simon Lucey

In this paper, we propose the first higher frame rate video dataset (called Need for Speed - NfS) and benchmark for visual object tracking.

Visual Object Tracking

PixelNet: Representation of the pixels, by the pixels, and for the pixels

1 code implementation21 Feb 2017 Aayush Bansal, Xinlei Chen, Bryan Russell, Abhinav Gupta, Deva Ramanan

We explore design principles for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation.

Edge Detection Semantic Segmentation +1

3D Human Pose Estimation = 2D Pose Estimation + Matching

no code implementations CVPR 2017 Ching-Hang Chen, Deva Ramanan

While many approaches try to directly predict 3D pose from image measurements, we explore a simple architecture that reasons through intermediate 2D pose predictions.

3D Human Pose Estimation 3D Pose Estimation +1

Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery

no code implementations15 Dec 2016 Vivek Krishnan, Deva Ramanan

We consider the task of visual net surgery, in which a CNN can be reconfigured without extra data to recognize novel concepts that may be omitted from the training set.

Novel Concepts Zero-Shot Learning

Finding Tiny Faces

20 code implementations CVPR 2017 Peiyun Hu, Deva Ramanan

We explore three aspects of the problem in the context of finding small faces: the role of scale invariance, image resolution, and contextual reasoning.

Face Detection Object Recognition

PixelNet: Towards a General Pixel-level Architecture

no code implementations21 Sep 2016 Aayush Bansal, Xinlei Chen, Bryan Russell, Abhinav Gupta, Deva Ramanan

We explore architectures for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation.

Edge Detection Semantic Segmentation +1

The Open World of Micro-Videos

no code implementations31 Mar 2016 Phuc Xuan Nguyen, Gregory Rogez, Charless Fowlkes, Deva Ramanan

Micro-videos are six-second videos popular on social media networks with several unique properties.

TAG Video Understanding

Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks

no code implementations ICCV 2015 Chunshui Cao, Xian-Ming Liu, Yi Yang, Yinan Yu, Jiang Wang, Zilei Wang, Yongzhen Huang, Liang Wang, Chang Huang, Wei Xu, Deva Ramanan, Thomas S. Huang

While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to remember that the human visual contex contains generally more feedback connections than foward connections.

Understanding Everyday Hands in Action From RGB-D Images

no code implementations ICCV 2015 Gregory Rogez, James S. Supancic III, Deva Ramanan

We analyze functional manipulations of handheld objects, formalizing the problem as one of fine-grained grasp classification.

Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians

1 code implementation CVPR 2016 Peiyun Hu, Deva Ramanan

We show that RGs can be optimized with a quadratic program (QP), that can in turn be optimized with a recurrent neural network (with rectified linear units).

Pose Estimation

First-Person Pose Recognition Using Egocentric Workspaces

no code implementations CVPR 2015 Gregory Rogez, James S. Supancic III, Deva Ramanan

In egocentric views, hands and arms are observable within a well defined volume in front of the camera.

Pose Estimation

Do We Need More Training Data?

no code implementations5 Mar 2015 Xiangxin Zhu, Carl Vondrick, Charless Fowlkes, Deva Ramanan

Datasets for training object recognition systems are steadily increasing in size.

Object Recognition

Egocentric Pose Recognition in Four Lines of Code

no code implementations29 Nov 2014 Gregory Rogez, James S. Supancic III, Deva Ramanan

We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera.

Pose Estimation

Capturing Long-tail Distributions of Object Subcategories

no code implementations CVPR 2014 Xiangxin Zhu, Dragomir Anguelov, Deva Ramanan

We argue that object subcategories follow a long-tail distribution: a few subcategories are common, while many are rare.

Parsing Videos of Actions with Segmental Grammars

no code implementations CVPR 2014 Hamed Pirsiavash, Deva Ramanan

Real-world videos of human activities exhibit temporal structure at various scales; long videos are typically composed out of multiple action instances, where each instance is itself composed of sub-actions with variable durations and orderings.

Analysis by Synthesis: 3D Object Recognition by Object Reconstruction

no code implementations CVPR 2014 Mohsen Hejrati, Deva Ramanan

We introduce an efficient "brute-force" approach to inference that searches through a large number of candidate reconstructions, returning the optimal one.

3D Object Recognition Object Reconstruction

Parsing Occluded People

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.

Object Recognition Pose Estimation

Microsoft COCO: Common Objects in Context

29 code implementations1 May 2014 Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollár

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.

Instance Segmentation Object Localization +3

Dual coordinate solvers for large-scale structural SVMs

no code implementations6 Dec 2013 Deva Ramanan

This manuscript describes a method for training linear SVMs (including binary SVMs, SVM regression, and structural SVMs) from large, out-of-core training datasets.

3D Object Recognition Action Classification +2

Histograms of Sparse Codes for Object Detection

no code implementations CVPR 2013 Xiaofeng Ren, Deva Ramanan

Object detection has seen huge progress in recent years, much thanks to the heavily-engineered Histograms of Oriented Gradients (HOG) features.

Dimensionality Reduction object-detection +1

Self-Paced Learning for Long-Term Tracking

no code implementations CVPR 2013 James S. Supancic III, Deva Ramanan

We address the problem of long-term object tracking, where the object may become occluded or leave-the-view.

object-detection Object Detection +1

Analyzing 3D Objects in Cluttered Images

no code implementations NeurIPS 2012 Mohsen Hejrati, Deva Ramanan

We use a morphable model to capture 3D within-class variation, and use a weak-perspective camera model to capture viewpoint.

3D Shape Reconstruction Viewpoint Estimation

Statistical Tests for Optimization Efficiency

no code implementations NeurIPS 2011 Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling

Learning problems such as logistic regression are typically formulated as pure optimization problems defined on some loss function.


Bilinear classifiers for visual 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.

Action Classification General Classification +1

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