Search Results for author:
Found 14 papers, 2 papers with code
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks
We propose novel approaches for simultaneously identifying important weights of a convolutional neural network (ConvNet) and providing more attention to the important weights during training.
Towards Efficient Coarse-to-Fine Networks for Action and Gesture Recognition
State-of-the-art approaches to video-based action and gesture recognition often employ two key concepts: First, they employ multistream processing; second, they use an ensemble of convolutional networks.
Ranked #1 on
Action Classification
on Jester test
Generative Human Motion Stylization in Latent Space
Building upon this, we present a novel generative model that produces diverse stylization results of a single motion (latent) code.
CLIPPING: Distilling CLIP-Based Models With a Student Base for Video-Language Retrieval
Pre-training a vison-language model and then fine-tuning it on downstream tasks have become a popular paradigm.
HiVLP: Hierarchical Interactive Video-Language Pre-Training
However, compared to image-language pre-training, VLP has lagged far behind due to the lack of large amounts of video-text pairs.
Error-Aware Spatial Ensembles for Video Frame Interpolation
However, none of the published VFI works considers the spatially non-uniform characteristics of the interpolation error (IE).
Decompose the Sounds and Pixels, Recompose the Events
In this paper, we propose a framework centering around a novel architecture called the Event Decomposition Recomposition Network (EDRNet) to tackle the Audio-Visual Event (AVE) localization problem in the supervised and weakly supervised settings.
Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity
Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e. g. speed, temporal order, etc.
Class Semantics-based Attention for Action Detection
Our attention mechanism outperforms prior self-attention modules such as the squeeze-and-excitation in action detection task.
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder
State of the art (SOTA) few-shot learning (FSL) methods suffer significant performance drop in the presence of domain differences between source and target datasets.
Improving Hierarchical Adversarial Robustness of Deep Neural Networks
Do all adversarial examples have the same consequences?
Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions
In this work, we take a step towards training robust models for cross-domain pose estimation task, which brings together ideas from causal representation learning and generative adversarial networks.
Detection Booster Training: A detection booster training method for improving the accuracy of classifiers.
In this paper, we probe this direction by deriving a relationship between the estimation of unknown parameters of the probability density function (pdf) of input data and classification accuracy.
All at Once: Temporally Adaptive Multi-Frame Interpolation with Advanced Motion Modeling
Recent advances in high refresh rate displays as well as the increased interest in high rate of slow motion and frame up-conversion fuel the demand for efficient and cost-effective multi-frame video interpolation solutions.
