The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training

Author:

Kang Gi-Cheon1,Kim Sungdong2,Kim Jin-Hwa2,Kwak Donghyun3,Zhang Byoung-Tak1

Affiliation:

1. Seoul National University,IPAI

2. NAVER AI Lab

3. NAVER Cloud CLOVA

Funder

Institute of Information & Communications Technology Planning & Evaluation

Publisher

IEEE

Reference86 articles.

1. Guess-what?! visual object discovery through multi-modal dialogue;de vries;CVPR,0

2. Multimodal Differential Network for Visual Question Generation

3. Visual Dialog

4. Video Captioning with Transferred Semantic Attributes

5. An image is worth 16x16 words: Transformers for image recognition at scale;dosovitskiy;ICLRE,0

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1. From text to multimodal: a survey of adversarial example generation in question answering systems;Knowledge and Information Systems;2024-08-09

2. PROGrasp: Pragmatic Human-Robot Communication for Object Grasping;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. VD-GR: Boosting Visual Dialog with Cascaded Spatial-Temporal Multi-Modal GRaphs;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

4. Modeling Intra- and Inter-Modal Alignment with Optimal Transport for Visual Dialog;2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI);2023-11-06

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