926 citations · 2.2k across the 54 of their papers we have counts for
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MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare
Yann Labbé, Lucas Manuelli, Arsalan Mousavian +7
We introduce MegaPose, a method to estimate the 6D pose of novel objects, that is, objects unseen during training. At inference time, the method only assumes knowledge of (i) a reg…
Multi-Task Learning of Object State Changes from Uncurated Videos
Tomáš Souček, Jean-Baptiste Alayrac, Antoine Miech +2
We aim to learn to temporally localize object state changes and the corresponding state-modifying actions by observing people interacting with objects in long uncurated web videos.…
Benchmarking Learning Efficiency in Deep Reservoir Computing
Hugo Cisneros, Josef Sivic, Tomas Mikolov
It is common to evaluate the performance of a machine learning model by measuring its predictive power on a test dataset. This approach favors complicated models that can smoothly…
Differentiable Collision Detection: a Randomized Smoothing Approach
Louis Montaut, Quentin Le Lidec, Antoine Bambade +3
Collision detection appears as a canonical operation in a large range of robotics applications from robot control to simulation, including motion planning and estimation. While the…
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators
Jiri Sedlar, Karla Stepanova, Radoslav Skoviera +5
This paper introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera. Despite the…
Learning Object Manipulation Skills from Video via Approximate Differentiable Physics
Vladimir Petrik, Mohammad Nomaan Qureshi, Josef Sivic +1
We aim to teach robots to perform simple object manipulation tasks by watching a single video demonstration. Towards this goal, we propose an optimization approach that outputs a c…
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
Antoine Yang, Antoine Miech, Josef Sivic +2
Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious…
Collision Detection Accelerated: An Optimization Perspective
Louis Montaut, Quentin Le Lidec, Vladimir Petrik +2
Collision detection between two convex shapes is an essential feature of any physics engine or robot motion planner. It has often been tackled as a computational geometry problem,…
Learning to Answer Visual Questions from Web Videos
Antoine Yang, Antoine Miech, Josef Sivic +2
Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and preve…
Focal Length and Object Pose Estimation via Render and Compare
Georgy Ponimatkin, Yann Labbé, Bryan Russell +2
We introduce FocalPose, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known…
Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos
Tomáš Souček, Jean-Baptiste Alayrac, Antoine Miech +2
Human actions often induce changes of object states such as "cutting an apple", "cleaning shoes" or "pouring coffee". In this paper, we seek to temporally localize object states (e…
TubeDETR: Spatio-Temporal Video Grounding with Transformers
Antoine Yang, Antoine Miech, Josef Sivic +2
We consider the problem of localizing a spatio-temporal tube in a video corresponding to a given text query. This is a challenging task that requires the joint and efficient modeli…
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation
Antonin Vobecky, David Hurych, Oriane Siméoni +4
This work investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars which, eq…