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Found 13 papers, 5 papers with code
OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB
To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions.
Propose, Assess, Search: Harnessing LLMs for Goal-Oriented Planning in Instructional Videos
Goal-oriented planning, or anticipating a series of actions that transition an agent from its current state to a predefined objective, is crucial for developing intelligent assistants aiding users in daily procedural tasks.
Unlocking Exocentric Video-Language Data for Egocentric Video Representation Learning
We present EMBED (Egocentric Models Built with Exocentric Data), a method designed to transform exocentric video-language data for egocentric video representation learning.
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
Relational Space-Time Query in Long-Form Videos
In this paper, we propose to study these problems in a joint framework for long video understanding.
GKNet: grasp keypoint network for grasp candidates detection
Decreasing the detection difficulty by grouping keypoints into pairs boosts performance.
Recognizing Object Affordances to Support Scene Reasoning for Manipulation Tasks
Unfortunately, the top performing affordance recognition methods use object category priors to boost the accuracy of affordance detection and segmentation.
Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping
Each primitive shape is designed with parametrized grasp families, permitting the pipeline to identify multiple grasp candidates per shape primitive region.
Real-world multiobject, multigrasp detection
A deep learning architecture is proposed to predict graspable locations for robotic manipulation.
Ranked #3 on
Robotic Grasping
on Cornell Grasp Dataset
Deep Grasp: Detection and Localization of Grasps with Deep Neural Networks
By defining the learning problem to be classification with null hypothesis competition instead of regression, the deep neural network with RGB-D image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot.
Robotics
When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective
Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications.
Supervised Collective Classification for Crowdsourcing
Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e. g., labels of an item) provided by labelers.
