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Found 17 papers, 8 papers with code
An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022
This technical report describes the CONE approach for Ego4D Natural Language Queries (NLQ) Challenge in ECCV 2022.
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding
This paper tackles an emerging and challenging problem of long video temporal grounding~(VTG) that localizes video moments related to a natural language (NL) query.
Searching for Two-Stream Models in Multivariate Space for Video Recognition
We design a multivariate search space, including 6 search variables to capture a wide variety of choices in designing two-stream models.
SF-Net: Single-Frame Supervision for Temporal Action Localization
To obtain the single-frame supervision, the annotators are asked to identify only a single frame within the temporal window of an action.
Ranked #5 on
Weakly Supervised Action Localization
on BEOID
Towards Train-Test Consistency for Semi-supervised Temporal Action Localization
The inconsistent strategy makes it hard to explicitly supervise the action localization model with temporal boundary annotations at training time.
CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation
In order to jointly capture the self-attention across multiple dimensions, including time, location and the sensor measurements, while maintain low computational complexity, we propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to process each dimension sequentially, yet in an order-independent manner.
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition
Motion has shown to be useful for video understanding, where motion is typically represented by optical flow.
Ranked #1 on
Action Recognition
on UCF-101
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data.
AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.
Ranked #16 on
Weakly Supervised Action Localization
on ActivityNet-1.2
(mAP@0.5 metric)
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
AutoLoc: Weakly-supervised Temporal Action Localization
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Online Detection of Action Start in Untrimmed, Streaming Videos
We aim to tackle a novel task in action detection - Online Detection of Action Start (ODAS) in untrimmed, streaming videos.
Single Shot Temporal Action Detection
The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step.
ConvNet Architecture Search for Spatiotemporal Feature Learning
Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation, and image captioning.
Ranked #71 on
Action Recognition
on HMDB-51
Temporal Convolution Based Action Proposal: Submission to ActivityNet 2017
Our approach achieves the state-of-the-art performances on both temporal action proposal task and temporal action localization task.
Ranked #11 on
Temporal Action Proposal Generation
on ActivityNet-1.3
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos
Temporal action localization is an important yet challenging problem.
Ranked #27 on
Temporal Action Localization
on THUMOS’14
(mAP IOU@0.6 metric)
EventNet Version 1.1 Technical Report
Finally, EventNet version 1. 1 contains 67, 641 videos, 500 events, and 5, 028 event-specific concepts.
Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
To address this challenging issue, we exploit the effectiveness of deep networks in temporal action localization via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes on the learned classification network to localize each action instance.
Ranked #1 on
Temporal Action Localization
on MEXaction2
