VD-BERT: A Unified Vision and Dialog Transformer with BERT

By Yue Wang, Shafiq R. Joty, Michael R. Lyu, Irwin King, Caiming Xiong, Steven C. H. Hoi. 2020

111 citations recorded by the source corpus.

Visual dialog is a challenging vision-language task, where a dialog agent needs to answer a series of questions through reasoning on the image content and dialog history. Prior work has mostly focused on various attention mechanisms to model such intricate interactions. By contrast, in this work, we propose VD-BERT, a simple yet effective framework of unified vision-dialog Transformer that leverages the pretrained BERT language models for Visual Dialog tasks. The model is unified in that (1) it captures all the interactions between the image and the multi-turn dialog using a single-stream Transformer encoder, and (2) it supports both answer ranking and answer generation seamlessly through the same architecture. More crucially, we adapt BERT for the effective fusion of vision and dialog contents via visually grounded training. Without the need of pretraining on external vision-language data, our model yields new state of the art, achieving the top position in both single-model and ensemble settings (74.54 and 75.35 NDCG scores) on the visual dialog leaderboard.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning, Model Architectures

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Citations in the corpus

Large-Scale Adversarial Training for Vision-and-Language Representation Learning (2020, background): “…pre-training has brought leaping advances in vision+language understanding tasks such as VQA and VCR, with great potential in extending to visual captioning [84, 70], visual dialog [47, 68], vision-language navigation [18, 43], as well as video-and-language representation learning [62, 61, 41, 31].”
Flamingo: a Visual Language Model for Few-Shot Learning (2022, background): “[123] Yue Wang, Shafiq Joty, Michael Lyu, Irwin King, Caiming Xiong, and Steven Hoi.”
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (2022, background): “We follow the discriminative setting where the model ranks a pool of answer candidates (Gan et al., 2019; Wang et al., 2020; Murahari et al., 2020).”
Learning to Ground Visual Objects for Visual Dialog (2021, methodology): “, 2020) and VDBERT (Wang et al., 2020) because pre-training models utilize extra large-scale datasets to train the models which are unfair compared with other models.”
Multi-Modal Open-Domain Dialogue (2020, background): “Other recent architectures have explored text generation (Wang et al., 2020; Park et al., 2020) in the context of the Visual Dialog (Das et al., 2017b) task; however, this task is primarily used to measure a model’s ability to answer questions about an image in the ﬂow of a natural conversation,…”
Multi-View Attention Networks for Visual Dialog (2020, methodology): “[18] introduced a fine-tuning method using a pre-trained model, as it is observed that pre-trained language model architectures (e.”
Playing Lottery Tickets with Vision and Language (2021, unknown): “This pretrain-thenfinetune learning paradigm has also been extended to diverse V+L tasks such as novel object captioning [26], visual dialog [51, 71], vision-language navigation [24, 49, 25], OCR-based VQA [73], robust VQA [38], and video-text pre-training [64, 63, 37, 82, 35].”
A Closer Look at the Robustness of Vision-and-Language Pre-trained Models (2020, methodology): “Pre-trained models [39, 75, 59, 35, 37, 24] such as ViLBERT [43], LXMERT [64] and UNITER [13] have demonstrated great generalizability over diverse V+L tasks [80, 23, 47, 70, 4, 22, 46, 9], such as Visual Question Answering (VQA) [6], Visual Commonsense Reasoning (VCR) [78], and Referring Expression”
Recent advances in deep learning based dialogue systems: a systematic survey (2021, methodology): “Wang et al. (2020c) built a quotation model in online conversations and investigated the consistency between quoted sentences and latent dialogue topics.”
Kaleido-BERT: Vision-Language Pre-training on Fashion Domain (2021, methodology): “In contrast to the boom in generic tasks ( e.g ., VCR [9, 43, 78], VQA [32, 38]), other tasks such as visual relationship detection (RVL-BERT [10]), visual navigation ( i.e ., PERVALENT [24] and VLN-BERT [48]), and visual dialog ( e.g ., VisualD [50], VD-BERT [70]) are still in their infancy.”