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Found 15 papers, 8 papers with code
EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding
Recent advances in egocentric video understanding models are promising, but their heavy computational expense is a barrier for many real-world applications.
EgoEnv: Human-centric environment representations from egocentric video
First-person video highlights a camera-wearer's activities in the context of their persistent environment.
Shaping embodied agent behavior with activity-context priors from egocentric video
For a given object, an activity-context prior represents the set of other compatible objects that are required for activities to succeed (e. g., a knife and cutting board brought together with a tomato are conducive to cutting).
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
Environment Predictive Coding for Visual Navigation
We introduce environment predictive coding, a self-supervised approach to learn environment-level representations for embodied agents.
Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos
We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets.
Environment Predictive Coding for Embodied Agents
We introduce environment predictive coding, a self-supervised approach to learn environment-level representations for embodied agents.
Differentiable Causal Discovery Under Unmeasured Confounding
In this work, we derive differentiable algebraic constraints that fully characterize the space of ancestral ADMGs, as well as more general classes of ADMGs, arid ADMGs and bow-free ADMGs, that capture all equality restrictions on the observed variables.
Learning Affordance Landscapes for Interaction Exploration in 3D Environments
We introduce a reinforcement learning approach for exploration for interaction, whereby an embodied agent autonomously discovers the affordance landscape of a new unmapped 3D environment (such as an unfamiliar kitchen).
EGO-TOPO: Environment Affordances from Egocentric Video
We introduce a model for environment affordances that is learned directly from egocentric video.
Grounded Human-Object Interaction Hotspots from Video (Extended Abstract)
Learning how to interact with objects is an important step towards embodied visual intelligence, but existing techniques suffer from heavy supervision or sensing requirements.
Grounded Human-Object Interaction Hotspots from Video
Learning how to interact with objects is an important step towards embodied visual intelligence, but existing techniques suffer from heavy supervision or sensing requirements.
Ranked #3 on
Video-to-image Affordance Grounding
on EPIC-Hotspot
Attributes as Operators: Factorizing Unseen Attribute-Object Compositions
In addition, we show that not only can our model recognize unseen compositions robustly in an open-world setting, it can also generalize to compositions where objects themselves were unseen during training.
Ranked #5 on
Image Retrieval with Multi-Modal Query
on MIT-States
Compositional Zero-Shot Learning
Image Retrieval with Multi-Modal Query
BlockDrop: Dynamic Inference Paths in Residual Networks
Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications.
CANDiS: Coupled & Attention-Driven Neural Distant Supervision
The unsupervised nature of this technique allows it to scale to web-scale relation extraction tasks, at the expense of noise in the training data.
