Search Results for author:
Found 17 papers, 14 papers with code
CorrNet+: Sign Language Recognition and Translation via Spatial-Temporal Correlation
In specific, CorrNet+ employs a correlation module and an identification module to build human body trajectories.
Improving Continuous Sign Language Recognition with Adapted Image Models
Besides, fully fine-tuning the model easily forgets the generic essential knowledge acquired in the pretraining stage and overfits the downstream data.
Dynamic Spatial-Temporal Aggregation for Skeleton-Aware Sign Language Recognition
Current methods utilize spatial graph modules and temporal modules to capture spatial and temporal features, respectively.
COMMA: Co-Articulated Multi-Modal Learning
First, the prompts of the vision and language branches in these methods are usually separated or uni-directionally correlated.
Hamming Encoder: Mining Discriminative k-mers for Discrete Sequence Classification
Existing pattern-based methods measure the discriminative power of each feature individually during the mining process, leading to the result of missing some combinations of features with discriminative power.
Interpretable Sequence Clustering
Categorical sequence clustering plays a crucial role in various fields, but the lack of interpretability in cluster assignments poses significant challenges.
AdaBrowse: Adaptive Video Browser for Efficient Continuous Sign Language Recognition
Then these features are fed into a policy network to intelligently select a subsequence to process.
Ranked #6 on
Sign Language Recognition
on CSL-Daily
A testing-based approach to assess the clusterability of categorical data
The objective of clusterability evaluation is to check whether a clustering structure exists within the data set.
Continuous Sign Language Recognition with Correlation Network
Visualizations demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames.
Personalized Interpretable Classification
In addition to the problem formulation on this new issue, we present a greedy algorithm called PIC (Personalized Interpretable Classifier) to identify a personalized rule for each individual test sample.
Self-Emphasizing Network for Continuous Sign Language Recognition
To relieve this problem, we propose a self-emphasizing network (SEN) to emphasize informative spatial regions in a self-motivated way, with few extra computations and without additional expensive supervision.
Ranked #7 on
Sign Language Recognition
on CSL-Daily
Significance-Based Categorical Data Clustering
As a by-product, we can further calculate an empirical $p$-value to assess the statistical significance of a set of clusters and develop an improved gap statistic for estimating the cluster number.
Spatial Temporal Graph Attention Network for Skeleton-Based Action Recognition
In such conditions, short-term dependencies are few formally considered, which are critical for classifying similar actions.
Ranked #1 on
Skeleton Based Action Recognition
on Kinetics-400
Temporal Lift Pooling for Continuous Sign Language Recognition
In this paper, we derive temporal lift pooling (TLP) from the Lifting Scheme in signal processing to intelligently downsample features of different temporal hierarchies.
Efficient Two-Step Networks for Temporal Action Segmentation
Due to boundary ambiguity and over-segmentation issues, identifying all the frames in long untrimmed videos is still challenging.
Ranked #12 on
Action Segmentation
on GTEA
FSD-10: A Dataset for Competitive Sports Content Analysis
To promote the research on action recognition from competitive sports video clips, we introduce a Figure Skating Dataset (FSD-10) for finegrained sports content analysis.
An Internal Validity Index Based on Density-Involved Distance
One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters.
Ranked #1 on
Clustering Algorithms Evaluation
on pathbased
