Papers › Unifying Vision-and-Language Tasks via Text Generation

Unifying Vision-and-Language Tasks via Text Generation

4 Feb 2021arXiv:2102.02779archive 2025-07-28

Jaemin Cho, Jie Lei, Hao Tan, Mohit Bansal

Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring expression comprehension, and a language decoder for image captioning, etc. To alleviate these hassles, in this work, we propose a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation, where our models learn to generate labels in text based on the visual and textual inputs. On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models. Moreover, our generative approach shows better generalization ability on questions that have rare answers. Also, we show that our framework allows multi-task learning in a single architecture with a single set of parameters, achieving similar performance to separately optimized single-task models. Our code is publicly available at: https://github.com/j-min/VL-T5

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VLSeq2SeqLMOutput j-min/VL-T5/VL-T5/src/multitask_model.py official repository ran MIT (permissive) · b5a6c04b009cca88 · report
VisualEmbedding j-min/VL-T5/VL-T5/src/multitask_model.py official repository ran MIT (permissive) · e44fd46c867cf8be · report
JointEncoder j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · 9ae4551ab4057d4f · report
VLT5 j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · 5d69b18e68c1e195 · report
VLT5COCOCaption j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · 943c42cc626cc9a5 · report
VLT5GQA j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · c6c8a88fa5a29395 · report
VLT5MMT j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · 8f8e3dcb1533f514 · report
VLT5MultiTask j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · 615b17a71b757c72 · report
VLT5NLVR j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · a991fbc2a12b6068 · report
VLT5RefCOCO j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · 99a4eac0e06e3904 · report
VLT5VCR j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · b4349d2ccfd035b9 · report
VLT5VQA j-min/VL-T5/VL-T5/src/multitask_model.py official repository unverified MIT (permissive) · 79580be4d7e75e1a · report

Tasks

Conditional Text GenerationDecoderImage CaptioningLanguage ModelingLanguage ModellingMulti-Task LearningQuestion AnsweringReferring ExpressionReferring Expression ComprehensionText GenerationVisual Commonsense ReasoningVisual Question AnsweringVisual Question Answering (VQA)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Captioning Flickr30k Captions test VL-T5 CIDEr 2.6 #7 of 7 Archive leaderboard report
Image Captioning Flickr30k Captions test VL-T5 SPICE 2.0 #7 of 7 Archive leaderboard report
Image Captioning nocaps val VL-T5 CIDEr 4.4 #3 of 3 Archive leaderboard report
Image Captioning nocaps val VL-T5 SPICE 5.3 #3 of 3 Archive leaderboard report
Visual Question Answering (VQA) VCR (Q-A) test VL-T5 Accuracy 75.3 #8 of 11 Archive leaderboard report
Visual Question Answering (VQA) VCR (Q-AR) test VL-T5 Accuracy 58.9 #6 of 7 Archive leaderboard report
Visual Question Answering (VQA) VCR (QA-R) test VL-T5 Accuracy 77.8 #7 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: VL-T5

VL-T5

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