Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding

cs.CL cs.AI cs.CV Shun Inadumi, Lee Sangmyeong, Koichiro Yoshino · Oct 14, 2026

What it does

Why it matters

It proposes Candidate Set Visual Grounding (CSVG) and introduces the CandVG benchmark to evaluate how the set of plausible referents evolves across dialogue turns. Evaluating 18 vision-language models shows that stronger single-object...

Main concern

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Plain-language introduction

This paper identifies a gap between standard visual grounding—which assumes enough dialogue context to pinpoint a single object—and real-world interactions where multiple referents remain plausible. It proposes Candidate Set Visual Grounding (CSVG) and introduces the CandVG benchmark to evaluate how the set of plausible referents evolves across dialogue turns. Evaluating 18 vision-language models shows that stronger single-object grounding does not translate to better set estimation, with models often failing to generate full candidate sets or committing prematurely to one candidate.

Critical review

Verdict

Bottom line

The paper makes a timely and clearly scoped contribution by defining CSVG and providing a careful diagnostic analysis of 18 VLMs. Its central empirical finding—that model scale helps CVG accuracy but not consistently CSVG $F_1$—is solid. However, the paper stops at benchmarking and light prompt engineering, lacking any learned mechanism or training intervention to address the identified failure modes.

“Our evaluation of vision-language models shows that stronger visual grounding capability does not necessarily yield better CSVG performance.”

paper · Abstract

“For the CSVG task on CandVG, Table 2 shows that larger open-weight generalist VLMs do not consistently achieve higher F1 scores.”

paper · Section 5.2

What holds up

The task formulation is well motivated, and the dataset construction is rigorous: inter-annotator $F_1$ reaches 0.943 and 0.935 for the two pairs, supporting benchmark reliability. The paper’s cross-turn tracking analysis is particularly strong, diagnosing distinct failure modes—premature commitment versus failure to narrow—that are grounded in quantitative turn-level statistics and qualitative examples.

“Table 1 shows high agreement for both annotator pairs across the CVG and CSVG metrics, supporting the reliability of the CandVG annotations.”

paper · Section 4.2, Table 1

“Qwen3.5 27B tends to commit prematurely to a narrow candidate set, whereas Qwen3 VL 4B fails to narrow it sufficiently as context accumulates.”

paper · Section 6.1

Main concerns

The paper is limited to zero-shot inference and simple prompt variations, offering no trained model or architectural insight to remedy the low recall problem. Its conclusions about embodied post-training are drawn from only two small model families at two scales each, which is insufficient to generalize. The task-aware instruction experiments improve recall for some large models but degrade precision or small-model performance, yet the paper still presents them as a partial mitigation without statistical significance testing or error analysis. Finally, the benchmark inherits the narrow dialogue structure of its source data, where candidate sets almost always monotonically shrink; sudden shifts and persistent ambiguity are underrepresented.

“Our investigation of performance improvement is limited to task-aware instructions applied at inference time. We have not systematically evaluated more sophisticated prompting or decoding strategies, such as self-reflection.”

paper · Limitations

“In most dialogues, candidate sets gradually shrink as additional context becomes available, often converging to a single object. Although a small number of dialogues involve sudden shifts in the intended object, resulting in candidate addition, such transition patterns are underrepresented in our benchmark.”

paper · Limitations

Evidence and comparison

The evidence supports the headline claim that CandVG is more challenging than standard multi-object grounding: every open-weight model scores lower $F_1$ on CandVG than on gRefCOCO, and the precision–recall gap is larger. The paper fairly distinguishes CSVG from prior CVG and ambiguous VQA work. One gap is the lack of domain analysis: because CandVG is derived from Open Images while RefCOCOg and gRefCOCO use COCO, some performance difference could stem from domain shift rather than dialogue ambiguity alone.

“VLMs achieve higher F1 scores on gRefCOCO than on CandVG in the CSVG setting. Figure 3 further shows that, although both benchmarks exhibit a precision–recall gap, the gap is consistently larger on CandVG.”

paper · Section 5.3

“Unlike existing CVG datasets, which assume contexts sufficient to identify a single referent, CandVG also includes contexts in which multiple referents remain plausible.”

paper · Section 2, Footnote 2

Reproducibility

Reproducibility is strong for an empirical benchmarking paper. The authors release annotations, code, prompts, and model-specific output formats via GitHub and Hugging Face. Experimental details are thorough: greedy decoding, a maximum of 512 tokens for open models, and exact API settings for GPT-5.6 Luna/Terra (reasoning effort medium, max 2,048 tokens). The main blockers are the proprietary GPT-5.6 models and the absence of candidate generation randomness statistics or temperature sweeps; relying on greedy decoding is reported, but no variance estimates are given. Hardware requirements (four RTX 3090 GPUs) and runtimes (~six hours per model) are clearly stated.

“We ran all experiments with the open-weight models on four NVIDIA RTX 3090 GPUs, and the longest inference run for a single model took about six hours.”

paper · Appendix C

“For GPT-5.6, we set the maximum generation length to 2,048 tokens and the reasoning effort to medium.”

paper · Appendix C

Abstract

Interpreting an ambiguous utterance in real-world interactions often requires context beyond a single utterance. When the utterance refers to objects in a visual scene, it is necessary to maintain and update the set of plausible referents as context accumulates, without assuming that the available information identifies a unique object. However, prior work on interactive visual grounding, which localizes objects referred to by linguistic expressions, has focused on identifying a unique object once sufficient dialogue context becomes available. To address this gap, we propose Candidate Set Visual Grounding (CSVG), a task for estimating the set of objects consistent with the given dialogue context. We further introduce CandVG, a benchmark that evaluates how the candidate set changes across dialogue turns. Our evaluation of vision-language models shows that stronger visual grounding capability does not necessarily yield better CSVG performance. In particular, models have difficulty predicting the complete candidate set, owing to challenges in candidate generation and premature commitment to a small set of candidates in early turns. Nevertheless, we find that this limitation can be partially mitigated in the evaluated large-scale models using instructions designed for CSVG.

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