Search Results for author: Pavel Tokmakov

Found 25 papers, 12 papers with code

Zero-1-to-3: Zero-shot One Image to 3D Object

1 code implementation ICCV 2023 Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, Carl Vondrick

We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.

3D Reconstruction Image to 3D +3

Learning to Track with Object Permanence

1 code implementation ICCV 2021 Pavel Tokmakov, Jie Li, Wolfram Burgard, Adrien Gaidon

In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning.

Multi-Object Tracking Object +3

Object Permanence Emerges in a Random Walk along Memory

1 code implementation4 Apr 2022 Pavel Tokmakov, Allan Jabri, Jie Li, Adrien Gaidon

This paper proposes a self-supervised objective for learning representations that localize objects under occlusion - a property known as object permanence.

Object

Towards Segmenting Anything That Moves

1 code implementation11 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

pix2gestalt: Amodal Segmentation by Synthesizing Wholes

1 code implementation25 Jan 2024 Ege Ozguroglu, Ruoshi Liu, Dídac Surís, Dian Chen, Achal Dave, Pavel Tokmakov, Carl Vondrick

We introduce pix2gestalt, a framework for zero-shot amodal segmentation, which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions.

3D Reconstruction Object Recognition +1

Discovering Objects that Can Move

1 code implementation CVPR 2022 Zhipeng Bao, Pavel Tokmakov, Allan Jabri, Yu-Xiong Wang, Adrien Gaidon, Martial Hebert

Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects.

Motion Segmentation Object +1

Object Discovery from Motion-Guided Tokens

2 code implementations CVPR 2023 Zhipeng Bao, Pavel Tokmakov, Yu-Xiong Wang, Adrien Gaidon, Martial Hebert

Object discovery -- separating objects from the background without manual labels -- is a fundamental open challenge in computer vision.

Object Object Discovery +2

Tracking through Containers and Occluders in the Wild

1 code implementation CVPR 2023 Basile Van Hoorick, Pavel Tokmakov, Simon Stent, Jie Li, Carl Vondrick

Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems.

Visual Tracking

Learning to Segment Moving Objects

no code implementations1 Dec 2017 Pavel Tokmakov, Cordelia Schmid, Karteek Alahari

We formulate this as a learning problem and design our framework with three cues: (i) independent object motion between a pair of frames, which complements object recognition, (ii) object appearance, which helps to correct errors in motion estimation, and (iii) temporal consistency, which imposes additional constraints on the segmentation.

Motion Estimation Motion Segmentation +4

Learning Video Object Segmentation with Visual Memory

no code implementations ICCV 2017 Pavel Tokmakov, Karteek Alahari, Cordelia Schmid

The module to build a "visual memory" in video, i. e., a joint representation of all the video frames, is realized with a convolutional recurrent unit learned from a small number of training video sequences.

Motion Segmentation Object +3

Weakly-Supervised Semantic Segmentation using Motion Cues

no code implementations23 Mar 2016 Pavel Tokmakov, Karteek Alahari, Cordelia Schmid

We also demonstrate that the performance of M-CNN learned with 150 weak video annotations is on par with state-of-the-art weakly-supervised methods trained with thousands of images.

Image Segmentation Weakly supervised Semantic Segmentation +1

Learning Motion Patterns in Videos

no code implementations CVPR 2017 Pavel Tokmakov, Karteek Alahari, Cordelia Schmid

The problem of determining whether an object is in motion, irrespective of camera motion, is far from being solved.

Motion Segmentation Optical Flow Estimation +3

Relational Linear Programs

no code implementations12 Oct 2014 Kristian Kersting, Martin Mladenov, Pavel Tokmakov

A relational linear program (RLP) is a declarative LP template defining the objective and the constraints through the logical concepts of objects, relations, and quantified variables.

A Structured Model For Action Detection

no code implementations CVPR 2019 Yubo Zhang, Pavel Tokmakov, Martial Hebert, Cordelia Schmid

A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large datasets and allowing them to discover the optimal representation for the problem at hand.

Action Detection Video Understanding

Learning Compositional Representations for Few-Shot Recognition

no code implementations ICCV 2019 Pavel Tokmakov, Yu-Xiong Wang, Martial Hebert

One of the key limitations of modern deep learning approaches lies in the amount of data required to train them.

Attribute Few-Shot Learning

A Study on Action Detection in the Wild

no code implementations29 Apr 2019 Yubo Zhang, Pavel Tokmakov, Martial Hebert, Cordelia Schmid

In this work we study the problem of action detection in a highly-imbalanced dataset.

Action Detection

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 +5

TAO: A Large-Scale Benchmark for Tracking Any Object

no code implementations ECCV 2020 Achal Dave, Tarasha Khurana, Pavel Tokmakov, Cordelia Schmid, Deva Ramanan

To this end, we ask annotators to label objects that move at any point in the video, and give names to them post factum.

Multi-Object Tracking Object +2

Breaking the "Object" in Video Object Segmentation

no code implementations CVPR 2023 Pavel Tokmakov, Jie Li, Adrien Gaidon

Yet, this important phenomenon is largely absent from existing video object segmentation (VOS) benchmarks.

Object Semantic Segmentation +2

Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models

no code implementations10 Oct 2023 Wen-Hsuan Chu, Adam W. Harley, Pavel Tokmakov, Achal Dave, Leonidas Guibas, Katerina Fragkiadaki

This begs the question: can we re-purpose these large-scale pre-trained static image models for open-vocabulary video tracking?

Object Object Tracking +5

Understanding Video Transformers via Universal Concept Discovery

no code implementations19 Jan 2024 Matthew Kowal, Achal Dave, Rares Ambrus, Adrien Gaidon, Konstantinos G. Derpanis, Pavel Tokmakov

To this end, we propose an efficient approach for unsupervised identification of units of video transformer representations - concepts, and ranking their importance to the output of a model.

Decision Making

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