Papers › Referring Transformer: A One-step Approach to Multi-task Visual Grounding

Referring Transformer: A One-step Approach to Multi-task Visual Grounding

6 Jun 2021NeurIPS 2021 12arXiv:2106.03089archive 2025-07-28

Muchen Li, Leonid Sigal

As an important step towards visual reasoning, visual grounding (e.g., phrase localization, referring expression comprehension/segmentation) has been widely explored Previous approaches to referring expression comprehension (REC) or segmentation (RES) either suffer from limited performance, due to a two-stage setup, or require the designing of complex task-specific one-stage architectures. In this paper, we propose a simple one-stage multi-task framework for visual grounding tasks. Specifically, we leverage a transformer architecture, where two modalities are fused in a visual-lingual encoder. In the decoder, the model learns to generate contextualized lingual queries which are then decoded and used to directly regress the bounding box and produce a segmentation mask for the corresponding referred regions. With this simple but highly contextualized model, we outperform state-of-the-arts methods by a large margin on both REC and RES tasks. We also show that a simple pre-training schedule (on an external dataset) further improves the performance. Extensive experiments and ablations illustrate that our model benefits greatly from contextualized information and multi-task training.

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convert_examples_to_features ubc-vision/RefTR/datasets/lang_utils.py official repository unverified MIT (permissive) · db595e458994d7d0 · report
mlp_mapping ubc-vision/RefTR/models/reftr_transformer.py official repository unverified MIT (permissive) · 7a5bae282fd6c7e4 · report
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Tasks

DecoderReferring ExpressionReferring Expression ComprehensionReferring Expression SegmentationSegmentationVisual GroundingVisual Reasoning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Referring Expression Segmentation RefCoCo val RefTR Overall IoU 70.56 #24 of 37 Archive leaderboard report

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