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Found 6 papers, 0 papers with code
iRAG: An Incremental Retrieval Augmented Generation System for Videos
Use of RAG for combined understanding of multimodal data such as text, images and videos is appealing but two critical limitations exist: one-time, upfront capture of all content in large multimodal data as text descriptions entails high processing times, and not all information in the rich multimodal data is typically in the text descriptions.
LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMs
Additionally, if free pretrained LLM-based summarizers are used to reduce context (into human consumable summaries), LeanContext can further modify the reduced context to enhance the accuracy (ROUGE-1 score) by $13. 22\%$ to $24. 61\%$.
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning
To achieve this, we propose a channel squeeze-excitation based feature metamorphosis module, Cross-SEC, to achieve distinct attention of all tasks and a de-correlation loss function with differential-privacy to train a deep learning model that produces distinct privacy-aware features as an output for the respective tasks.
FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics
Detection-driven real-time video analytics require continuous detection of objects contained in the video frames using deep learning models like YOLOV3, EfficientDet.
EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference
Collaborative inference enables resource-constrained edge devices to make inferences by uploading inputs (e. g., images) to a server (i. e., cloud) where the heavy deep learning models run.
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification
In this paper, we propose a transfer-learning based model construction technique for the aerial scene classification problem.
Ranked #5 on
Aerial Scene Classification
on UCM (50% as trainset)
