no code implementations • 18 Apr 2024 • Md Adnan Arefeen, Biplob Debnath, Md Yusuf Sarwar Uddin, Srimat Chakradhar
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.
1 code implementation • 13 Sep 2023 • Christoph Reich, Biplob Debnath, Deep Patel, Srimat Chakradhar
the input image, the JPEG quality, the quantization tables, and the color conversion parameters.
no code implementations • 2 Sep 2023 • Md Adnan Arefeen, Biplob Debnath, Srimat Chakradhar
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 $24. 61\%$.
no code implementations • 30 Aug 2023 • Christoph Reich, Biplob Debnath, Deep Patel, Tim Prangemeier, Daniel Cremers, Srimat Chakradhar
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.
no code implementations • 15 Nov 2022 • Sibendu Paul, Kunal Rao, Giuseppe Coviello, Murugan Sankaradas, Oliver Po, Y. Charlie Hu, Srimat Chakradhar
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.
no code implementations • 23 Aug 2022 • Sibendu Paul, Kunal Rao, Giuseppe Coviello, Murugan Sankaradas, Oliver Po, Y. Charlie Hu, Srimat Chakradhar
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.
no code implementations • 8 Feb 2022 • Murugan Sankaradas, Kunal Rao, Ravi Rajendran, Amit Redkar, Srimat Chakradhar
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.
no code implementations • 3 Sep 2021 • Kunal Rao, Giuseppe Coviello, Min Feng, Biplob Debnath, Wang-Pin Hsiung, Murugan Sankaradas, Yi Yang, Oliver Po, Utsav Drolia, Srimat Chakradhar
Identification of people with elevated body temperature can reduce or dramatically slow down the spread of infectious diseases like COVID-19.
no code implementations • 12 Oct 2016 • Chao Li, Yi Yang, Min Feng, Srimat Chakradhar, Huiyang Zhou
Leveraging large data sets, deep Convolutional Neural Networks (CNNs) achieve state-of-the-art recognition accuracy.
no code implementations • 17 Mar 2016 • Linnan Wang, Yi Yang, Martin Renqiang Min, Srimat Chakradhar
Then we present the study of ISGD batch size to the learning rate, parallelism, synchronization cost, system saturation and scalability.
no code implementations • NeurIPS 2008 • Hans P. Graf, Srihari Cadambi, Venkata Jakkula, Murugan Sankaradass, Eric Cosatto, Srimat Chakradhar, Igor Dourdanovic
In this way memory bandwidth scales with the number of VPE, and the main data flows are local, keeping power dissipation low.