Zobrazeno 1 - 10
of 387
pro vyhledávání: '"Oliva, Aude"'
Autor:
Huang, Irene, Lin, Wei, Mirza, M. Jehanzeb, Hansen, Jacob A., Doveh, Sivan, Butoi, Victor Ion, Herzig, Roei, Arbelle, Assaf, Kuhene, Hilde, Darrel, Trevor, Gan, Chuang, Oliva, Aude, Feris, Rogerio, Karlinsky, Leonid
Compositional Reasoning (CR) entails grasping the significance of attributes, relations, and word order. Recent Vision-Language Models (VLMs), comprising a visual encoder and a Large Language Model (LLM) decoder, have demonstrated remarkable proficie
Externí odkaz:
http://arxiv.org/abs/2406.08164
Autor:
Wang, Runqian, Ghosh, Soumya, Cox, David, Antognini, Diego, Oliva, Aude, Feris, Rogerio, Karlinsky, Leonid
Low-rank adapters (LoRA) and their variants are popular parameter-efficient fine-tuning (PEFT) techniques that closely match full model fine-tune performance while requiring only a small number of additional parameters. These additional LoRA paramete
Externí odkaz:
http://arxiv.org/abs/2405.17258
Autor:
Pan, Bowen, Shen, Yikang, Liu, Haokun, Mishra, Mayank, Zhang, Gaoyuan, Oliva, Aude, Raffel, Colin, Panda, Rameswar
Mixture-of-Experts (MoE) language models can reduce computational costs by 2-4$\times$ compared to dense models without sacrificing performance, making them more efficient in computation-bounded scenarios. However, MoE models generally require 2-4$\t
Externí odkaz:
http://arxiv.org/abs/2404.05567
Autor:
Zhong, Howard, Mishra, Samarth, Kim, Donghyun, Jin, SouYoung, Panda, Rameswar, Kuehne, Hilde, Karlinsky, Leonid, Saligrama, Venkatesh, Oliva, Aude, Feris, Rogerio
Pre-training on massive video datasets has become essential to achieve high action recognition performance on smaller downstream datasets. However, most large-scale video datasets contain images of people and hence are accompanied with issues related
Externí odkaz:
http://arxiv.org/abs/2311.06231
Autor:
Pan, Bowen, Panda, Rameswar, Jin, SouYoung, Feris, Rogerio, Oliva, Aude, Isola, Phillip, Kim, Yoon
We explore the use of language as a perceptual representation for vision-and-language navigation (VLN), with a focus on low-data settings. Our approach uses off-the-shelf vision systems for image captioning and object detection to convert an agent's
Externí odkaz:
http://arxiv.org/abs/2310.07889
The development of technologies for easily and automatically falsifying video has raised practical questions about people's ability to detect false information online. How vulnerable are people to deepfake videos? What technologies can be applied to
Externí odkaz:
http://arxiv.org/abs/2304.04733
Autor:
Cascante-Bonilla, Paola, Shehada, Khaled, Smith, James Seale, Doveh, Sivan, Kim, Donghyun, Panda, Rameswar, Varol, Gül, Oliva, Aude, Ordonez, Vicente, Feris, Rogerio, Karlinsky, Leonid
Large-scale pre-trained Vision & Language (VL) models have shown remarkable performance in many applications, enabling replacing a fixed set of supported classes with zero-shot open vocabulary reasoning over (almost arbitrary) natural language prompt
Externí odkaz:
http://arxiv.org/abs/2303.17590
Deepfakes pose a serious threat to digital well-being by fueling misinformation. As deepfakes get harder to recognize with the naked eye, human users become increasingly reliant on deepfake detection models to decide if a video is real or fake. Curre
Externí odkaz:
http://arxiv.org/abs/2206.00535
Autor:
Grauman, Kristen, Westbury, Andrew, Byrne, Eugene, Chavis, Zachary, Furnari, Antonino, Girdhar, Rohit, Hamburger, Jackson, Jiang, Hao, Liu, Miao, Liu, Xingyu, Martin, Miguel, Nagarajan, Tushar, Radosavovic, Ilija, Ramakrishnan, Santhosh Kumar, Ryan, Fiona, Sharma, Jayant, Wray, Michael, Xu, Mengmeng, Xu, Eric Zhongcong, Zhao, Chen, Bansal, Siddhant, Batra, Dhruv, Cartillier, Vincent, Crane, Sean, Do, Tien, Doulaty, Morrie, Erapalli, Akshay, Feichtenhofer, Christoph, Fragomeni, Adriano, Fu, Qichen, Gebreselasie, Abrham, Gonzalez, Cristina, Hillis, James, Huang, Xuhua, Huang, Yifei, Jia, Wenqi, Khoo, Weslie, Kolar, Jachym, Kottur, Satwik, Kumar, Anurag, Landini, Federico, Li, Chao, Li, Yanghao, Li, Zhenqiang, Mangalam, Karttikeya, Modhugu, Raghava, Munro, Jonathan, Murrell, Tullie, Nishiyasu, Takumi, Price, Will, Puentes, Paola Ruiz, Ramazanova, Merey, Sari, Leda, Somasundaram, Kiran, Southerland, Audrey, Sugano, Yusuke, Tao, Ruijie, Vo, Minh, Wang, Yuchen, Wu, Xindi, Yagi, Takuma, Zhao, Ziwei, Zhu, Yunyi, Arbelaez, Pablo, Crandall, David, Damen, Dima, Farinella, Giovanni Maria, Fuegen, Christian, Ghanem, Bernard, Ithapu, Vamsi Krishna, Jawahar, C. V., Joo, Hanbyul, Kitani, Kris, Li, Haizhou, Newcombe, Richard, Oliva, Aude, Park, Hyun Soo, Rehg, James M., Sato, Yoichi, Shi, Jianbo, Shou, Mike Zheng, Torralba, Antonio, Torresani, Lorenzo, Yan, Mingfei, Malik, Jitendra
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers f
Externí odkaz:
http://arxiv.org/abs/2110.07058
Deep convolutional networks have recently achieved great success in video recognition, yet their practical realization remains a challenge due to the large amount of computational resources required to achieve robust recognition. Motivated by the eff
Externí odkaz:
http://arxiv.org/abs/2108.10394