Search Results for author:
Found 23 papers, 8 papers with code
Semantic Video Entity Linking Based on Visual Content and Metadata
In this paper, we propose to exploit video visual content to improve video entity linking.
Deep Multimodal Representation Learning from Temporal Data
In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications.
An Interactive Greedy Approach to Group Sparsity in High Dimensions
Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis.
Human Activity Recognition
Vocal Bursts Intensity Prediction
The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing
We propose a novel deep neural network architecture for the challenging problem of single image dehazing, which aims to recover the clear image from a degraded hazy image.
Two Stream Self-Supervised Learning for Action Recognition
The self-supervised pre-trained weights effectiveness is validated on the action recognition task.
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
We cast visual retrieval as a regression problem by posing triplet loss as a regression loss.
STEP: Spatio-Temporal Progressive Learning for Video Action Detection
In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector---a progressive learning framework for spatio-temporal action detection in videos.
Ranked #7 on
Action Detection
on UCF101-24
Cross-X Learning for Fine-Grained Visual Categorization
Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation.
Ranked #18 on
Fine-Grained Image Classification
on NABirds
(using extra training data)
Fine-Grained Image Classification
Fine-Grained Visual Categorization
A Generic Visualization Approach for Convolutional Neural Networks
Compared to classification networks, attention visualization for retrieval networks is hardly studied.
Hierarchical Contrastive Motion Learning for Video Action Recognition
Thus, the motion features at higher levels are trained to gradually capture semantic dynamics and evolve more discriminative for action recognition.
GTA: Global Temporal Attention for Video Action Understanding
To this end, we introduce Global Temporal Attention (GTA), which performs global temporal attention on top of spatial attention in a decoupled manner.
Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories
The standard way of training video models entails sampling at each iteration a single clip from a video and optimizing the clip prediction with respect to the video-level label.
Semi-Supervised Vision Transformers
Surprisingly, we show Vision Transformers perform significantly worse than Convolutional Neural Networks when only a small set of labeled data is available.
Efficient Video Transformers with Spatial-Temporal Token Selection
Video transformers have achieved impressive results on major video recognition benchmarks, which however suffer from high computational cost.
ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization
Without the boundary information of action segments, existing methods mostly rely on multiple instance learning (MIL), where the predictions of unlabeled instances (i. e., video snippets) are supervised by classifying labeled bags (i. e., untrimmed videos).
Relational Space-Time Query in Long-Form Videos
In this paper, we propose to study these problems in a joint framework for long video understanding.
Vision Transformers Are Good Mask Auto-Labelers
We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations.
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization
Our framework extends CLIP with minimal modifications to model spatial-temporal relationships in videos, making it a specialized video classifier, while striving for generalization.
MINOTAUR: Multi-task Video Grounding From Multimodal Queries
Video understanding tasks take many forms, from action detection to visual query localization and spatio-temporal grounding of sentences.
Towards Scalable Neural Representation for Diverse Videos
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e. g., NeRV, E-NeRV).
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
Despite significant results achieved by Contrastive Language-Image Pretraining (CLIP) in zero-shot image recognition, limited effort has been made exploring its potential for zero-shot video recognition.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
Video ReCap: Recursive Captioning of Hour-Long Videos
We utilize a curriculum learning training scheme to learn the hierarchical structure of videos, starting from clip-level captions describing atomic actions, then focusing on segment-level descriptions, and concluding with generating summaries for hour-long videos.
