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Found 12 papers, 6 papers with code
RubiksNet: Learnable 3D-Shift for Efficient Video Action Recognition
We analyze the suitability of our new primitive for video action recognition and explore several novel variations of our approach to enable stronger representational flexibility while maintaining an efficient design.
MoReVQA: Exploring Modular Reasoning Models for Video Question Answering
This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage, modular reasoning framework.
Streaming Dense Video Captioning
An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos, predict rich, detailed textual descriptions, and be able to produce outputs before processing the entire video.
Revisiting the "Video" in Video-Language Understanding
Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks.
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Video Question Answering
on MSR-VTT-MC
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments
We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation.
IGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes
We present iGibson 1. 0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes.
End-to-End Joint Semantic Segmentation of Actors and Actions in Video
Traditional video understanding tasks include human action recognition and actor/object semantic segmentation.
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.
Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos
In this work, we propose to tackle this new task with a weakly-supervised framework for reference-aware visual grounding in instructional videos, where only the temporal alignment between the transcription and the video segment are available for supervision.
ActivityNet Challenge 2017 Summary
The ActivityNet Large Scale Activity Recognition Challenge 2017 Summary: results and challenge participants papers.
SST: Single-Stream Temporal Action Proposals
Our paper presents a new approach for temporal detection of human actions in long, untrimmed video sequences.
