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Shreyank N Gowda

7 accepted papers

2026

SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning

CVPR 2026

In open-world semi-supervised learning (OWSSL), a model learns from labeled data and unlabeled data containing both known and novel classes. In practical OWSSL applications, models are expected to perform rigorous classification by directly selecting the most semantically relevant label from a candi

Cited by 0SourcecodeScholar
2025

Principles of Visual Tokens for Efficient Video Understanding

ICCV 2025poster

Video understanding has made huge strides in recent years, relying largely on the power of transformers. As this architecture is notoriously expensive and video data is highly redundant, research into improving efficiency has become particularly relevant. Some creative solutions include token select…

2025

Progressive Data Dropout: An Embarrassingly Simple Approach to Train Faster

NeurIPS 2025poster

The success of the machine learning field has reliably depended on training on large datasets. While effective, this trend comes at an extraordinary cost. This is due to two deeply intertwined factors: the size of models and the size of datasets. While promising research efforts focus on reducing th…

Cited by 0SourcecodeScholar
2025

ZeroDiff: Solidified Visual-semantic Correlation in Zero-Shot Learning

ICLR 2025poster

Zero-shot Learning (ZSL) aims to enable classifiers to identify unseen classes. This is typically achieved by generating visual features for unseen classes based on learned visual-semantic correlations from seen classes. However, most current generative approaches heavily rely on having a sufficient…

2022

CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition

ECCV 2022poster

"Zero-Shot action recognition is the task of recognizing action classes without visual examples. The problem can be seen as learning a representation on seen classes which generalizes well to instances of unseen classes, without losing discriminability between classes. Neural networks are able to mo…

Cited by 41SourcePDFScholar
2022

Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition

ECCV 2022poster

"We address the problem of data augmentation for video action recognition. Standard augmentation strategies in video are hand designed and sample the space of possible augmented data points either at random, without knowing which augmented points will be better, or through heuristics. We propose to…

Cited by 45SourcePDFScholar