activity
20242026
most citedTrafficLens: Multi-Camera Traffic Video Analysis Using LLMs

4 citations · 4 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2026

DamageScope: Vision-Language Retrieval at Scale for Disaster Damage Assessment from Satellite Imagery

Ravi K. Rajendran, Biplob Debnath, Murugan Sankaradas +1

Timely and accurate assessment of property damage is critical following natural disasters. Traditional on-site inspections are labor-intensive, costly, and often pose safety risks.…

cs.CV2026

Open-SAT: LLM-Guided Query Embedding Refinement for Open-Vocabulary Object Retrieval in Satellite Imagery

Md Adnan Arefeen, Biplob Debnath, Ravi K. Rajendran +2

In satellite applications, user queries often take the form of open-ended natural language, extending beyond a fixed set of predefined categories. This open-vocabulary nature poses…

cs.CV2026

Visual Alignment of Medical Vision-Language Models for Grounded Radiology Report Generation

Sarosij Bose, Ravi K. Rajendran, Biplob Debnath +3

Radiology Report Generation (RRG) is a critical step toward automating healthcare workflows, facilitating accurate patient assessments, and reducing the workload of medical profess…

cs.CV20254 cited

TrafficLens: Multi-Camera Traffic Video Analysis Using LLMs

Md Adnan Arefeen, Biplob Debnath, Srimat Chakradhar

Traffic cameras are essential in urban areas, playing a crucial role in intelligent transportation systems. Multiple cameras at intersections enhance law enforcement capabilities,…

cs.CV2025

StreamingRAG: Real-time Contextual Retrieval and Generation Framework

Murugan Sankaradas, Ravi K. Rajendran, Srimat T. Chakradhar

Extracting real-time insights from multi-modal data streams from various domains such as healthcare, intelligent transportation, and satellite remote sensing remains a challenge. H…

cs.CV2024

iRAG: Advancing RAG for Videos with an Incremental Approach

Md Adnan Arefeen, Biplob Debnath, Md Yusuf Sarwar Uddin +1

Retrieval-augmented generation (RAG) systems combine the strengths of language generation and information retrieval to power many real-world applications like chatbots. Use of RAG…