Search Results for author:
Found 27 papers, 20 papers with code
A Recipe for Scaling up Text-to-Video Generation with Text-free Videos
Following such a pipeline, we study the effect of doubling the scale of training set (i. e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9. 67 to 8. 19 and FVD from 484 to 441), demonstrating the scalability of our approach.
Ranked #6 on
Text-to-Video Generation
on MSR-VTT
DreamVideo: Composing Your Dream Videos with Customized Subject and Motion
In motion learning, we architect a motion adapter and fine-tune it on the given videos to effectively model the target motion pattern.
Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation
At the structure level, we decompose the T2V task into two steps, including spatial reasoning and temporal reasoning, using a unified denoiser.
Ranked #5 on
Text-to-Video Generation
on MSR-VTT
Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer Learning
When pre-training on the large-scale Kinetics-710, we achieve 89. 7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability of DiST.
HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation
For snippet-level learning, we introduce an online-updated memory to store reliable snippet prototypes for each class.
Ranked #1 on
Weakly Supervised Action Localization
on BEOID
Temporally-Adaptive Models for Efficient Video Understanding
Spatial convolutions are extensively used in numerous deep video models.
Ranked #3 on
Action Recognition
on EPIC-KITCHENS-100
(using extra training data)
MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition
To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder.
Enlarging Instance-specific and Class-specific Information for Open-set Action Recognition
In this paper, we begin with analyzing the feature representation behavior in the open-set action recognition (OSAR) problem based on the information bottleneck (IB) theory, and propose to enlarge the instance-specific (IS) and class-specific (CS) information contained in the feature for better performance.
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition
To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric.
Space-time Prompting for Video Class-incremental Learning
In this paper, we will fill this gap by learning multiple prompts based on a powerful image-language pre-trained model, i. e., CLIP, making it fit for video class-incremental learning (VCIL).
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning
The former is to reduce the memory cost by preserving only one condensed frame instead of the whole video, while the latter aims to compensate the lost spatio-temporal details in the Frame Condensing stage.
MAR: Masked Autoencoders for Efficient Action Recognition
Inspired by this, we propose propose Masked Action Recognition (MAR), which reduces the redundant computation by discarding a proportion of patches and operating only on a part of the videos.
Ranked #12 on
Action Recognition
on Something-Something V2
Hybrid Relation Guided Set Matching for Few-shot Action Recognition
To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric.
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency
In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos.
TAda! Temporally-Adaptive Convolutions for Video Understanding
This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modelling complex temporal dynamics in videos.
Ranked #67 on
Action Recognition
on Something-Something V2
(using extra training data)
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning
The visualizations show that ParamCrop adaptively controls the center distance and the IoU between two augmented views, and the learned change in the disparity along the training process is beneficial to learning a strong representation.
Exploring Stronger Feature for Temporal Action Localization
Temporal action localization aims to localize starting and ending time with action category.
OadTR: Online Action Detection with Transformers
Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure.
Ranked #8 on
Online Action Detection
on THUMOS'14
Proposal Relation Network for Temporal Action Detection
We calculate the detection results by assigning the proposals with corresponding classification results.
Ranked #2 on
Temporal Action Localization
on ActivityNet-1.3
(using extra training data)
Weakly-Supervised Temporal Action Localization Through Local-Global Background Modeling
Then our proposed Local-Global Background Modeling Network (LGBM-Net) is trained to localize instances by using only video-level labels based on Multi-Instance Learning (MIL).
Weakly-supervised Learning
Weakly-supervised Temporal Action Localization
+1
Relation Modeling in Spatio-Temporal Action Localization
This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021.
Ranked #4 on
Spatio-Temporal Action Localization
on AVA-Kinetics
(using extra training data)
A Stronger Baseline for Ego-Centric Action Detection
This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop.
Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition
In this paper, we present empirical results for training a stronger video vision transformer on the EPIC-KITCHENS-100 Action Recognition dataset.
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal
In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action proposal generation.
Ranked #2 on
Semi-Supervised Action Detection
on ActivityNet-1.3
Temporal Context Aggregation Network for Temporal Action Proposal Refinement
In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement.
Ranked #9 on
Temporal Action Localization
on ActivityNet-1.3
CBR-Net: Cascade Boundary Refinement Network for Action Detection: Submission to ActivityNet Challenge 2020 (Task 1)
In this report, we present our solution for the task of temporal action localization (detection) (task 1) in ActivityNet Challenge 2020.
Temporal Fusion Network for Temporal Action Localization:Submission to ActivityNet Challenge 2020 (Task E)
This technical report analyzes a temporal action localization method we used in the HACS competition which is hosted in Activitynet Challenge 2020. The goal of our task is to locate the start time and end time of the action in the untrimmed video, and predict action category. Firstly, we utilize the video-level feature information to train multiple video-level action classification models.
