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Found 9 papers, 5 papers with code
Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions
With this in mind, the descriptions people generate for videos of different dynamic events can greatly improve our understanding of the key information of interest in each video.
We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos
This allows our model to perform cognitive tasks such as set abstraction (which general concept is in common among a set of videos?
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding
Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds.
Reasoning About Human-Object Interactions Through Dual Attention Networks
The model not only finds when an action is happening and which object is being manipulated, but also identifies which part of the object is being interacted with.
Examining Interpretable Feature Relationships in Deep Networks for Action recognition
A number of recent methods to understand neural networks have focused on quantifying the role of individual features.
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context.
Moments in Time Dataset: one million videos for event understanding
We present the Moments in Time Dataset, a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds.
End to End Learning for Self-Driving Cars
The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.
Softstar: Heuristic-Guided Probabilistic Inference
Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself.
