Zobrazeno 1 - 10
of 535
pro vyhledávání: '"Shah Hardik"'
Autor:
Kamuni, Navin, Shah, Hardik, Chintala, Sathishkumar, Kunchakuri, Naveen, Dominion, Sujatha Alla Old
End-to-end multi-task dialogue systems are usually designed with separate modules for the dialogue pipeline. Among these, the policy module is essential for deciding what to do in response to user input. This policy is trained by reinforcement learni
Externí odkaz:
http://arxiv.org/abs/2401.18040
Autor:
Shah, Hardik
This research focuses on the critical process of upgrading a Design System from Web Content Accessibility Guidelines (WCAG) 2.0 to WCAG 2.1, which is an essential step in enhancing web accessibility. It emphasizes the importance of staying up to date
Externí odkaz:
http://arxiv.org/abs/2312.02992
Autor:
Sinha, Animesh, Sun, Bo, Kalia, Anmol, Casanova, Arantxa, Blanchard, Elliot, Yan, David, Zhang, Winnie, Nelli, Tony, Chen, Jiahui, Shah, Hardik, Yu, Licheng, Singh, Mitesh Kumar, Ramchandani, Ankit, Sanjabi, Maziar, Gupta, Sonal, Bearman, Amy, Mahajan, Dhruv
We introduce Style Tailoring, a recipe to finetune Latent Diffusion Models (LDMs) in a distinct domain with high visual quality, prompt alignment and scene diversity. We choose sticker image generation as the target domain, as the images significantl
Externí odkaz:
http://arxiv.org/abs/2311.10794
Autor:
Pramanick, Shraman, Song, Yale, Nag, Sayan, Lin, Kevin Qinghong, Shah, Hardik, Shou, Mike Zheng, Chellappa, Rama, Zhang, Pengchuan
Video-language pre-training (VLP) has become increasingly important due to its ability to generalize to various vision and language tasks. However, existing egocentric VLP frameworks utilize separate video and language encoders and learn task-specifi
Externí odkaz:
http://arxiv.org/abs/2307.05463
Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture se
Externí odkaz:
http://arxiv.org/abs/2306.05785
Large Vision-Language Foundation Models (VLFM), such as CLIP, ALIGN and Florence, are trained on large-scale datasets of image-caption pairs and achieve superior transferability and robustness on downstream tasks, but they are difficult to use in man
Externí odkaz:
http://arxiv.org/abs/2303.18232
Autor:
Kottur, Satwik, Moon, Seungwhan, Markosyan, Aram H., Shah, Hardik, Damavandi, Babak, Geramifard, Alborz
People capture photos and videos to relive and share memories of personal significance. Recently, media montages (stories) have become a popular mode of sharing these memories due to their intuitive and powerful storytelling capabilities. However, cr
Externí odkaz:
http://arxiv.org/abs/2211.03940
Autor:
Rodriguez, Pedro, Azab, Mahmoud, Silvert, Becka, Sanchez, Renato, Labson, Linzy, Shah, Hardik, Moon, Seungwhan
Searching troves of videos with textual descriptions is a core multimodal retrieval task. Owing to the lack of a purpose-built dataset for text-to-video retrieval, video captioning datasets have been re-purposed to evaluate models by (1) treating cap
Externí odkaz:
http://arxiv.org/abs/2210.05038
Autor:
Pramanick, Shraman, Jing, Li, Nag, Sayan, Zhu, Jiachen, Shah, Hardik, LeCun, Yann, Chellappa, Rama
Vision-language pre-training (VLP) has recently proven highly effective for various uni- and multi-modal downstream applications. However, most existing end-to-end VLP methods use high-resolution image-text box data to perform well on fine-grained re
Externí odkaz:
http://arxiv.org/abs/2210.04135
Autor:
Korlipara, Madhuri, Shah, Hardik
Publikováno v:
European Journal of Training and Development, 2022, Vol. 48, Issue 1/2, pp. 90-111.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/EJTD-03-2022-0030