1 citations · 2 across the 7 of their papers we have counts for
7 papers
Multimodal RAG Enhanced Visual Description
Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
Textual descriptions for multimodal inputs entail recurrent refinement of queries to produce relevant output images. Despite efforts to address challenges such as scaling model siz…
Investigating Context Effects in Similarity Judgements in Large Language Models
Sagar Uprety, Amit Kumar Jaiswal, Haiming Liu +1
Large Language Models (LLMs) have revolutionised the capability of AI models in comprehending and generating natural language text. They are increasingly being used to empower and…
Towards Subject Agnostic Affective Emotion Recognition
Amit Kumar Jaiswal, Haiming Liu, Prayag Tiwari
This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in s…
Lightweight Adaptation of Neural Language Models via Subspace Embedding
Amit Kumar Jaiswal, Haiming Liu
Traditional neural word embeddings are usually dependent on a richer diversity of vocabulary. However, the language models recline to cover major vocabularies via the word embeddin…
Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach
Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
Understanding an information forager's actions during interaction is very important for the study of interactive information retrieval. Although information spread in uncertain inf…
Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation
Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
User implicit feedback plays an important role in recommender systems. However, finding implicit features is a tedious task. This paper aims to identify users' preferences through…
Effects of Foraging in Personalized Content-based Image Recommendation
Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the…