7 papers
NDAI-NeuroMAP: A Neuroscience-Specific Embedding Model for Domain-Specific Retrieval
Devendra Patel, Aaditya Jain, Jayant Verma +4
We present NDAI-NeuroMAP, the first neuroscience-domain-specific dense vector embedding model engineered for high-precision information retrieval tasks. Our methodology encompasses…
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.…
Beyond Few-shot Object Detection: A Detailed Survey
Vishal Chudasama, Hiran Sarkar, Pankaj Wasnik +2
Object detection is a critical field in computer vision focusing on accurately identifying and locating specific objects in images or videos. Traditional methods for object detecti…
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…
S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning
Jayateja Kalla, Soma Biswas
Few-shot class-incremental learning (FSCIL) aims to learn progressively about new classes with very few labeled samples, without forgetting the knowledge of already learnt classes.…