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Found 18 papers, 5 papers with code
Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques
This paper offers a comprehensive analysis of recent advancements in video inpainting techniques, a critical subset of computer vision and artificial intelligence.
Adversarial Augmentation Training Makes Action Recognition Models More Robust to Realistic Video Distribution Shifts
More precisely, we created dataset splits of HMDB-51 or UCF-101 for training, and Kinetics-400 for testing, using the subset of the classes that are overlapping in both train and test datasets.
Watt For What: Rethinking Deep Learning's Energy-Performance Relationship
Deep learning models have revolutionized various fields, from image recognition to natural language processing, by achieving unprecedented levels of accuracy.
Telling Stories for Common Sense Zero-Shot Action Recognition
The textual narratives forge connections between seen and unseen classes, overcoming the bottleneck of labeled data that has long impeded advancements in this exciting domain.
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning
Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training.
From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications
This paper presents a comprehensive survey of state-of-the-art methods for talking head generation.
Optimizing ViViT Training: Time and Memory Reduction for Action Recognition
In this paper, we address the challenges posed by the substantial training time and memory consumption associated with video transformers, focusing on the ViViT (Video Vision Transformer) model, in particular the Factorised Encoder version, as our baseline for action recognition tasks.
Synthetic Sample Selection for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) has emerged as a pivotal research domain in computer vision, owing to its capability to recognize objects that have not been seen during training.
Ranked #1 on
Generalized Zero-Shot Learning
on CUB-200-2011
A Closer Look at Temporal Ordering in the Segmentation of Instructional Videos
We refer to this task as Procedure Segmentation and Summarization (PSS).
Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition
We propose to learn what makes a good video for action recognition and select only high-quality samples for augmentation.
Ranked #2 on
Few Shot Action Recognition
on HMDB51
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition
We address the problem of capturing temporal information for video classification in 2D networks, without increasing their computational cost.
A New Split for Evaluating True Zero-Shot Action Recognition
We benchmark several recent approaches on the proposed True Zero-Shot(TruZe) Split for UCF101 and HMDB51, with zero-shot and generalized zero-shot evaluation.
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition
Theproblem can be seen as learning a function which general-izes well to instances of unseen classes without losing dis-crimination between classes.
Ranked #2 on
Zero-Shot Action Recognition
on Olympics
SMART Frame Selection for Action Recognition
In this work, however, we focus on the more standard short, trimmed action recognition problem.
Ranked #4 on
Action Recognition
on UCF101
ALBA : Reinforcement Learning for Video Object Segmentation
We treat this as a grouping problem by exploiting object proposals and making a joint inference about grouping over both space and time.
Using an ensemble color space model to tackle adversarial examples
Minute pixel changes in an image drastically change the prediction that the deep learning model makes.
StegColNet: Steganalysis based on an ensemble colorspace approach
Image steganography refers to the process of hiding information inside images.
ColorNet: Investigating the importance of color spaces for image classification
These color images are taken as input in the form of RGB images and classification is done without modifying them.
Ranked #4 on
Image Classification
on SVHN
