Search Results for author: Faisal Ahmed

Found 7 papers, 3 papers with code

SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning

1 code implementation25 Nov 2021 Kevin Lin, Linjie Li, Chung-Ching Lin, Faisal Ahmed, Zhe Gan, Zicheng Liu, Yumao Lu, Lijuan Wang

Based on this model architecture, we show that video captioning can benefit significantly from more densely sampled video frames as opposed to previous successes with sparsely sampled video frames for video-and-language understanding tasks (e. g., video question answering).

Frame Question Answering +3

Crossing the Format Boundary of Text and Boxes: Towards Unified Vision-Language Modeling

no code implementations23 Nov 2021 Zhengyuan Yang, Zhe Gan, JianFeng Wang, Xiaowei Hu, Faisal Ahmed, Zicheng Liu, Yumao Lu, Lijuan Wang

In this paper, we propose UNICORN, a vision-language (VL) model that unifies text generation and bounding box prediction into a single architecture.

Image Captioning Language Modelling +5

UNITER: Learning UNiversal Image-TExt Representations

no code implementations25 Sep 2019 Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu

Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding.

Language Modelling Masked Language Modeling +7

UNITER: UNiversal Image-TExt Representation Learning

5 code implementations ECCV 2020 Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu

Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i. e., masked language/region modeling is conditioned on full observation of image/text).

Language Modelling Masked Language Modeling +9

BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems

no code implementations15 Nov 2017 Zachary Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng

We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems.

Efficient Exploration Q-Learning +2

Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

1 code implementation ACL 2017 Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng

In this paper, we address this limitation by replacing symbolic queries with an induced "soft" posterior distribution over the KB that indicates which entities the user is interested in.


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