3 citations · 3 across the 5 of their papers we have counts for
5 papers
CoVLM: Leveraging Consensus from Vision-Language Models for Semi-supervised Multi-modal Fake News Detection
Devank, Jayateja Kalla, Soma Biswas
In this work, we address the real-world, challenging task of out-of-context misinformation detection, where a real image is paired with an incorrect caption for creating fake news.…
AggSS: An Aggregated Self-Supervised Approach for Class-Incremental Learning
Jayateja Kalla, Soma Biswas
This paper investigates the impact of self-supervised learning, specifically image rotations, on various class-incremental learning paradigms. Here, each image with a predefined ro…
TACLE: Task and Class-aware Exemplar-free Semi-supervised Class Incremental Learning
Jayateja Kalla, Rohit Kumar, Soma Biswas
We propose a novel TACLE (TAsk and CLass-awarE) framework to address the relatively unexplored and challenging problem of exemplar-free semi-supervised class incremental learning.…
Robust Feature Learning and Global Variance-Driven Classifier Alignment for Long-Tail Class Incremental Learning
Jayateja Kalla, Soma Biswas
This paper introduces a two-stage framework designed to enhance long-tail class incremental learning, enabling the model to progressively learn new classes, while mitigating catast…
Can Out-of-Domain data help to Learn Domain-Specific Prompts for Multimodal Misinformation Detection?
Amartya Bhattacharya, Debarshi Brahma, Suraj Nagaje Mahadev +3
Spread of fake news using out-of-context images and captions has become widespread in this era of information overload. Since fake news can belong to different domains like politic…