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of 44
pro vyhledávání: '"Kumar, Sateesh"'
We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g., CLIPScore) via integrating the recent advances in MLMs. We design fou
Externí odkaz:
http://arxiv.org/abs/2403.02677
The quality of pre-training data plays a critical role in the performance of foundation models. Popular foundation models often design their own recipe for data filtering, which makes it hard to analyze and compare different data filtering approaches
Externí odkaz:
http://arxiv.org/abs/2309.15954
Research on Inverse Reinforcement Learning (IRL) from third-person videos has shown encouraging results on removing the need for manual reward design for robotic tasks. However, most prior works are still limited by training from a relatively restric
Externí odkaz:
http://arxiv.org/abs/2207.14299
Autor:
Ojha, Shailendra Singh, Sharma, Jai Kumar, Dhakad, Bhupendra, Kumar, Sateesh, Sharma, Neeraj, Pandey, Anuj Kumar, Hasnain, S.M. Mozammil, Kumar, Sandeep, Kumar, Rahul
Publikováno v:
In Results in Engineering June 2024 22
We present a novel approach for unsupervised activity segmentation which uses video frame clustering as a pretext task and simultaneously performs representation learning and online clustering. This is in contrast with prior works where representatio
Externí odkaz:
http://arxiv.org/abs/2105.13353
Autor:
Haresh, Sanjay, Kumar, Sateesh, Coskun, Huseyin, Syed, Shahram Najam, Konin, Andrey, Zia, Muhammad Zeeshan, Tran, Quoc-Huy
We present a self-supervised approach for learning video representations using temporal video alignment as a pretext task, while exploiting both frame-level and video-level information. We leverage a novel combination of temporal alignment loss and t
Externí odkaz:
http://arxiv.org/abs/2103.17260
Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms focused on lane departures or safe following distances are finding their
Externí odkaz:
http://arxiv.org/abs/2004.05261
Akademický článek
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Akademický článek
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Autor:
Usmani, Maaha, Amin, Faridah, Saeed, Rabeeya, Durrani, Noureen, Zaheer, Muhammad K., Mateen, Areeba, Shakeel, Fatima, Kumar, Sateesh
Publikováno v:
Journal of Family Medicine & Primary Care; Jan2024, Vol. 13 Issue 1, p271-277, 7p