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Found 18 papers, 6 papers with code
IC3: Image Captioning by Committee Consensus
If you ask a human to describe an image, they might do so in a thousand different ways.
Open-Vocabulary Temporal Action Detection with Off-the-Shelf Image-Text Features
Detecting actions in untrimmed videos should not be limited to a small, closed set of classes.
Distribution Aware Metrics for Conditional Natural Language Generation
In this work we argue that existing metrics are not appropriate for domains such as visual description or summarization where ground truths are semantically diverse, and where the diversity in those captions captures useful additional information about the context.
What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics
While there have been significant gains in the field of automated video description, the generalization performance of automated description models to novel domains remains a major barrier to using these systems in the real world.
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking
Automatic video captioning aims to train models to generate text descriptions for all segments in a video, however, the most effective approaches require large amounts of manual annotation which is slow and expensive.
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster R-CNN object detection framework.
Ranked #27 on
Temporal Action Localization
on THUMOS’14
End-to-End Learning of Semantic Grasping
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images.
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1. 58M action labels with multiple labels per person occurring frequently.
Ranked #6 on
Action Detection
on UCF101-24
The Kinetics Human Action Video Dataset
We describe the DeepMind Kinetics human action video dataset.
Motion Prediction Under Multimodality with Conditional Stochastic Networks
In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables.
SfM-Net: Learning of Structure and Motion from Video
We propose SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations.
YouTube-8M: A Large-Scale Video Classification Benchmark
Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.
Ranked #1 on
Action Recognition In Videos
on ActivityNet
Efficient Large Scale Video Classification
We present two methods that build on this work, and scale it up to work with millions of videos and hundreds of thousands of classes while maintaining a low computational cost.
Beyond Short Snippets: Deep Networks for Video Classification
Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval.
Ranked #5 on
Action Recognition
on Sports-1M
Deep Networks With Large Output Spaces
Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data.
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
Many object detection systems are constrained by the time required to convolve a target image with a bank of filters that code for different aspects of an object's appearance, such as the presence of component parts.
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning
Our first approach maps the data to two-bit binary keys that are locality-sensitive for the angle between the hyperplane normal and a database point.
Multi-Level Active Prediction of Useful Image Annotations for Recognition
We introduce a framework for actively learning visual categories from a mixture of weakly and strongly labeled image examples.
