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GREC: Generalized Referring Expression Comprehension

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arxiv 2308.16182 v2 pith:BYXZK5L6 submitted 2023-08-30 cs.CV

Shuting He , Henghui Ding , Chang Liu , Xudong Jiang This is my paper
classification cs.CV
keywords expressionsgrecclassicexpressiongrefcocoreferringtargetcomprehension
verification ladder T0 review T1 audit T2 compute T3 formal
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The objective of Classic Referring Expression Comprehension (REC) is to produce a bounding box corresponding to the object mentioned in a given textual description. Commonly, existing datasets and techniques in classic REC are tailored for expressions that pertain to a single target, meaning a sole expression is linked to one specific object. Expressions that refer to multiple targets or involve no specific target have not been taken into account. This constraint hinders the practical applicability of REC. This study introduces a new benchmark termed as Generalized Referring Expression Comprehension (GREC). This benchmark extends the classic REC by permitting expressions to describe any number of target objects. To achieve this goal, we have built the first large-scale GREC dataset named gRefCOCO. This dataset encompasses a range of expressions: those referring to multiple targets, expressions with no specific target, and the single-target expressions. The design of GREC and gRefCOCO ensures smooth compatibility with classic REC. The proposed gRefCOCO dataset, a GREC method implementation code, and GREC evaluation code are available at https://github.com/henghuiding/gRefCOCO.

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Cited by 5 Pith papers

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  1. Teaching MLLMs to Say No: Generalized Referring Expression Comprehension via Refusal Calibrated GRPO

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    A recalibrated GRPO reinforcement learning method lets multimodal LLMs say 'None' for nonexistent referring expressions without sacrificing localization accuracy on objects that do exist.

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    RefBench-PRO organizes REC into attribute, position, interaction, relation, commonsense, and reject tasks; no tested MLLM exceeds 72%, and Ref-R1 raises Qwen2.5-VL-7B from 57.6 to 69.4 on it.

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  4. ReMeREC: Relation-aware and Multi-entity Referring Expression Comprehension

    cs.CV 2025-07 conditional novelty 6.0 of 10

    ReMeREC introduces a relation-aware multi-entity referring expression comprehension framework and the ReMeX dataset, reporting state-of-the-art grounding and relation prediction, with some evaluation caveats.

  5. MDC-R: The Minecraft Dialogue Corpus with Reference

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    MDC-R adds expert annotations of anaphoric and deictic reference, with block-level IDs and bounding boxes, to 101 Minecraft building dialogues, and shows that current referring-expression models struggle on this dynam...

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