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Found 11 papers, 1 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.
Differentiable JPEG: The Devil is in the Details
the input image, the JPEG quality, the quantization tables, and the color conversion parameters.
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 $24. 61\%$.
Deep Video Codec Control for Vision Models
To overcome the deterioration of vision performance, this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance, while adhering to existing standardization.
APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning
This is because the camera parameter settings, though optimal at deployment time, are not the best settings for good-quality video capture as the environmental conditions and scenes around a camera change during operation.
Why is the video analytics accuracy fluctuating, and what can we do about it?
It is a common practice to think of a video as a sequence of images (frames), and re-use deep neural network models that are trained only on images for similar analytics tasks on videos.
Edge-based fever screening system over private 5G
Edge computing and 5G have made it possible to perform analytics closer to the source of data and achieve super-low latency response times, which is not possible with centralized cloud deployment.
F3S: Free Flow Fever Screening
Identification of people with elevated body temperature can reduce or dramatically slow down the spread of infectious diseases like COVID-19.
Optimizing Memory Efficiency for Deep Convolutional Neural Networks on GPUs
Leveraging large data sets, deep Convolutional Neural Networks (CNNs) achieve state-of-the-art recognition accuracy.
Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent
Then we present the study of ISGD batch size to the learning rate, parallelism, synchronization cost, system saturation and scalability.
A Massively Parallel Digital Learning Processor
In this way memory bandwidth scales with the number of VPE, and the main data flows are local, keeping power dissipation low.
