Search Results for author:
Found 8 papers, 4 papers with code
VideoLLM: Modeling Video Sequence with Large Language Models
Building upon this insight, we propose a novel framework called VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs from natural language processing (NLP) for video sequence understanding.
MRSN: Multi-Relation Support Network for Video Action Detection
Action detection is a challenging video understanding task, requiring modeling spatio-temporal and interaction relations.
InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
In this report, we present our champion solutions to five tracks at Ego4D challenge.
Ranked #1 on
State Change Object Detection
on Ego4D
Exploring State Change Capture of Heterogeneous Backbones @ Ego4D Hands and Objects Challenge 2022
Our method achieves an accuracy of 0. 796 on OSCC while achieving an absolute temporal localization error of 0. 516 on PNR.
Uncertainty-based Network for Few-shot Image Classification
Specifically, we first data augment and classify the query instance and calculate the mutual information of these classification scores.
BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection
Empirical results demonstrate that our PlusTAD is very efficient and significantly outperforms the previous methods on the datasets of THUMOS14 and FineAction.
Ranked #1 on
Temporal Action Localization
on THUMOS14
DCAN: Improving Temporal Action Detection via Dual Context Aggregation
Specifically, we design the Multi-Path Temporal Context Aggregation (MTCA) to achieve smooth context aggregation on boundary level and precise evaluation of boundaries.
Ranked #19 on
Temporal Action Localization
on ActivityNet-1.3
Dynamic Sampling Networks for Efficient Action Recognition in Videos
The existing action recognition methods are mainly based on clip-level classifiers such as two-stream CNNs or 3D CNNs, which are trained from the randomly selected clips and applied to densely sampled clips during testing.
Ranked #9 on
Action Recognition
on ActivityNet
