Search Results for author:
Found 11 papers, 8 papers with code
TIM: A Time Interval Machine for Audio-Visual Action Recognition
We address the interplay between the two modalities in long videos by explicitly modelling the temporal extents of audio and visual events.
Graph Guided Question Answer Generation for Procedural Question-Answering
The key technological enabler is a novel mechanism for automatic question-answer generation from procedural text which can ingest large amounts of textual instructions and produce exhaustive in-domain QA training data.
Epic-Sounds: A Large-scale Dataset of Actions That Sound
We introduce EPIC-SOUNDS, a large-scale dataset of audio annotations capturing temporal extents and class labels within the audio stream of the egocentric videos.
With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition
We capitalise on the action's temporal context and propose a method that learns to attend to surrounding actions in order to improve recognition performance.
Slow-Fast Auditory Streams For Audio Recognition
We propose a two-stream convolutional network for audio recognition, that operates on time-frequency spectrogram inputs.
Ranked #1 on
Human Interaction Recognition
on EPIC-SOUNDS
Rescaling Egocentric Vision
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS.
Ranked #6 on
Action Anticipation
on EPIC-KITCHENS-100
The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines
Our dataset features 55 hours of video consisting of 11. 5M frames, which we densely labelled for a total of 39. 6K action segments and 454. 2K object bounding boxes.
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition
We focus on multi-modal fusion for egocentric action recognition, and propose a novel architecture for multi-modal temporal-binding, i. e. the combination of modalities within a range of temporal offsets.
Ranked #2 on
Egocentric Activity Recognition
on EPIC-KITCHENS-55
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
First-person vision is gaining interest as it offers a unique viewpoint on people's interaction with objects, their attention, and even intention.
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning
In this work, a novel method based on the learning using privileged information (LUPI) paradigm for recognizing complex human activities is proposed that handles missing information during testing.
Inferring Human Activities Using Robust Privileged Probabilistic Learning
Classification models may often suffer from "structure imbalance" between training and testing data that may occur due to the deficient data collection process.
