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Found 16 papers, 7 papers with code
SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition
To overcome both limitations, we introduce Self-Supervised Learning Over Sets (SOS), an approach to pre-train a generic Objects In Contact (OIC) representation model from video object regions detected by an off-the-shelf hand-object contact detector.
OWL (Observe, Watch, Listen): Audiovisual Temporal Context for Localizing Actions in Egocentric Videos
We validate our approach in two large-scale datasets, EPIC-Kitchens, and HOMAGE.
vCLIMB: A Novel Video Class Incremental Learning Benchmark
We perform in-depth evaluations of existing CL methods in vCLIMB, and observe two unique challenges in video data.
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
Recently, few-shot learning has received increasing interest.
Boundary-sensitive Pre-training for Temporal Localization in Videos
However, most existing models developed for these tasks are pre-trained on general video action classification tasks.
Ranked #23 on
Temporal Action Localization
on ActivityNet-1.3
Egocentric Action Recognition by Video Attention and Temporal Context
In this challenge, action recognition is posed as the problem of simultaneously predicting a single `verb' and `noun' class label given an input trimmed video clip.
Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention
Departing from existing alternatives, our W3 module models all three facets of video attention jointly.
Ranked #1 on
Action Recognition
on EgoGesture
Finding Moments in Video Collections Using Natural Language
We evaluate our approach on two recently proposed datasets for temporal localization of moments in video with natural language (DiDeMo and Charades-STA) extended to our video corpus moment retrieval setting.
The ActivityNet Large-Scale Activity Recognition Challenge 2018 Summary
The guest tasks focused on complementary aspects of the activity recognition problem at large scale and involved three challenging and recently compiled datasets: the Kinetics-600 dataset from Google DeepMind, the AVA dataset from Berkeley and Google, and the Moments in Time dataset from MIT and IBM Research.
Diagnosing Error in Temporal Action Detectors
Despite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?)
Guess Where? Actor-Supervision for Spatiotemporal Action Localization
Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable.
ActivityNet Challenge 2017 Summary
The ActivityNet Large Scale Activity Recognition Challenge 2017 Summary: results and challenge participants papers.
SCC: Semantic Context Cascade for Efficient Action Detection
Despite the recent advances in large-scale video analysis, action detection remains as one of the most challenging unsolved problems in computer vision.
SST: Single-Stream Temporal Action Proposals
Our paper presents a new approach for temporal detection of human actions in long, untrimmed video sequences.
ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding
In spite of many dataset efforts for human action recognition, current computer vision algorithms are still severely limited in terms of the variability and complexity of the actions that they can recognize.
On the Relationship Between Visual Attributes and Convolutional Networks
One of the cornerstone principles of deep models is their abstraction capacity, i. e. their ability to learn abstract concepts from `simpler' ones.
