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Temporal Prompting Matters: Rethinking Referring Video Object Segmentation

Ci-Siang Lin, Min-Hung Chen, I-Jieh Liu, Chien-Yi Wang, Sifei Liu, Yu-Chiang Frank Wang

arXiv:2510.07319v1cs.CV

TL;DR

RVOS aims to segment a text-referred object throughout a video, but existing methods often require end-to-end training and dense mask annotations. Tenet generates and selects temporal prompts for foundation segmentation models, and reports J&F scores of 65.5% on Ref-YouTube-VOS and 71.0% on Ref-DAVIS17. The formulation treats box-level annotations as weak supervision for segmentation.

  • Problem

    RVOS must segment a text-referred object across video frames, while many existing methods require costly end-to-end training and dense mask annotations.

    Source: §1 · pp. 1–2

  • Method

    Tenet decomposes RVOS into referring, video, and segmentation, using detectors and trackers to generate temporal prompts and Prompt Preference Learning to select prompts for foundation segmentation models.

    Source: §1 · pp. 2–3

  • Results

    65.5% J&F on Ref-YouTube-VOS and 71.0% J&F on Ref-DAVIS17 are reported for Tenet prompting SAM with the selected track.

    Source: §4.2 · p. 8

  • Takeaways & Limitations

    The results support using selected temporal prompts to adapt image-based foundation segmentation models for referring video object segmentation.

    Source: §5 · p. 10

    The formulation uses box-level target annotations, treated as weak supervision for segmentation.

    Source: §3.1 · p. 4

Abstract

from arXiv · show

Referring Video Object Segmentation (RVOS) aims to segment the object referred to by the query sentence in the video. Most existing methods require end-to-end training with dense mask annotations, which could be computation-consuming and less scalable. In this work, we rethink the RVOS problem and aim to investigate the key to this task. Based on existing foundation segmentation models, we decompose the RVOS task into referring, video, and segmentation factors, and propose a Temporal Prompt Generation and Selection (Tenet) framework to address the referring and video factors while leaving the segmentation problem to foundation models. To efficiently adapt image-based foundation segmentation models to referring video object segmentation, we leverage off-the-shelf object detectors and trackers to produce temporal prompts associated with the referring sentence. While high-quality temporal prompts could be produced, they can not be easily identified from confidence scores. To tackle this issue, we propose Prompt Preference Learning to evaluate the quality of the produced temporal prompts. By taking such prompts to instruct image-based foundation segmentation models, we would be able to produce high-quality masks for the referred object, enabling efficient model adaptation to referring video object segmentation. Experiments on RVOS benchmarks demonstrate the effectiveness of the Tenet framework.

1 INTRODUCTION

RVOS must follow a language-described object through changing video frames, while common approaches require costly end-to-end training and dense masks. Tenet uses selected temporal prompts to adapt foundation segmentation models to this task.

1 INTRODUCTION

  • RVOS must track and segment a text-referred object across frames despite changes in position, size, pose, occlusion, and scene.Long-term motion or action in a referring sentence may not be recognizable from a single frame.Source: §1 · p. 1
  • Most existing RVOS methods require computationally expensive end-to-end training and dense mask annotations, limiting scalability.Source: §1 · p. 2
  • Image-based SAM produces masks from visual prompts but cannot properly handle natural-language descriptions or video; naive combinations of detectors and trackers are suboptimal.Those off-the-shelf models are trained for different tasks.Source: §1 · p. 2
  • Tenet decomposes RVOS into referring, video, and segmentation factors, using off-the-shelf detectors and trackers to generate reference proposals and candidate tracks as temporal prompts.The framework addresses referring and video while leaving segmentation to foundation models.Source: §1 · p. 2
  • Prompt Preference Learning compares candidate-track quality with the reference proposal because confidence scores do not readily identify high-quality tracks.The selected prompt instructs an image-based foundation segmentation model; experiments on Refer-Youtube-VOS and Refer-DAVIS17 demonstrate Tenet’s effectiveness.Source: §1 · p. 3

2 RELATED WORKS

Prior RVOS work addresses language-conditioned segmentation across video, while foundation segmentation models offer visual-prompt capabilities but need adaptation for referring video tasks.

2.1 REFERRING IMAGE/VIDEO SEGMENTATION

  • RIS segments a text-specified object in one image, but its methods may be limited by RVOS challenges such as object motion, deformation, and occlusion.RVOS methods include offline ReferFormer and online OnlineRefer; many require costly end-to-end training and dense mask annotations.Source: §2.1 · p. 3

2.2 FOUNDATION SEGMENTATION MODELS

  • SAM supports visual prompts such as points and boxes, while SegGPT and SEEM offer generalized segmentation capabilities but lack specific video-domain adaptation or enhanced tracking for RVOS.SAM-PT focuses on point-prompt enhancement for classic video segmentation, whereas the paper proposes temporal-aware prompting for referring video segmentation.Source: §2.2 · pp. 3–4

3 PROPOSED FRAMEWORK: TENET

Tenet decomposes RVOS into referring, video, and segmentation factors, using temporal prompts for the first two and foundation models for segmentation. It generates reference proposals and candidate tracks, then learns to select prompts because track quality is not reliably reflected by confidence scores.

3.1 PROBLEM DEFINITION AND METHOD OVERVIEW

  • Tenet treats RVOS as referring, video, and segmentation components, using box-level annotations and off-the-shelf detectors and trackers to generate prompts for foundation segmentation models.The reference proposal uses finetuned detector Top-1 boxes; candidate tracks use pretrained detector Top-K boxes plus the finetuned Top-1 box, with OC-SORT tracking and Top-1 boxes filling missed frames.Source: §3.1 · p. 4, §3.2 · p. 5

3.2 TEMPORAL PROMPT GENERATION AND ANALYSIS

  • Prompting SAM with ground-truth boxes yields 83.6% J&F, 15.6% higher than state-of-the-art RVOS method MUTR (Yan et al., 2024b), supporting prompt-based segmentation as a promising RVOS direction.Source: §3.2 · p. 5
  • Finetuned Grounding DINO Top-1 proposals surpass pretrained Top-1 proposals by 4.9% in J&F.Grounding DINO is finetuned using RVOS box annotations; the method selects the highest-confidence proposal at each frame as the reference proposal.Source: §3.2 · p. 5
  • 5.6% higher J&F than the reference proposal (b) came from the candidate track with the highest averaged box mIoU, showing that at least one candidate track is high quality.The comparison is between the best-produced candidate track (d) and the finetuned detector's Top-1 reference proposal (b).Source: §3.2 · p. 5
  • 7.3% lower J&F for the highest-confidence track than for the highest-box-mIoU track shows frame-level confidence scores are unreliable for ranking video-level tracks.The comparison concerns candidate tracks produced from pretrained Grounding DINO Top-K proposals plus the finetuned model's Top-1 proposal.Source: §3.2 · pp. 5–6

3.3 PROMPT PREFERENCE LEARNING

  • Prompt Preference Learning uses a Transformer-based binary classifier to compare each candidate track with the reference proposal, selecting the highest-scoring candidate above 0.5 or otherwise the reference.The training label is positive when a candidate track has higher mIoU than the reference proposal.Source: §3.3 · pp. 6–7

4 EXPERIMENTS

Experiments evaluate Tenet on Ref-YouTube-VOS and Ref-DAVIS17, reporting benchmark results alongside analyses of prompt choices and efficiency.

4.1 DATASETS AND IMPLEMENTATION DETAILS

  • Evaluation uses the Ref-YouTube-VOS official server because validation annotations are unavailable, and official-code evaluation for Ref-DAVIS17.The prompt scorer uses pretrained CLIP image and text encoders, two-layer MLPs, and a one-layer Transformer; only MLP and Transformer parameters are trained.Source: §4.1 · p. 7

4.2 QUANTITATIVE AND QUALITATIVE RESULTS

  • 65.5% J &F on Ref-YouTube-VOS and 71.0% J &F on Ref-DAVIS17; Tenet uses Prompt Preference Learning to select tracks for SAM.The results are evaluated on the validation splits.Source: Table 2 · p. 8, §4.2 · p. 8

4.3 ABLATION STUDIES

  • Using only Top-5 tracks from finetuned Grounding DINO drops J &F by 5.5%, attributed to repetitive proposals and lost diversity.Table 3 compares candidate-track variants; ⋆ marks tracks produced from the finetuned detector’s Top-5 proposals.Source: §4.3 · p. 8, Table 3 · p. 10
  • Greedily merging candidate tracks by mIoU yields only 1.2% higher J &F than selecting one candidate, so candidates are considered individually.Source: §4.3 · p. 10
  • J &F saturates when the number of pretrained Grounding DINO proposals reaches K≥5, so K is set to 5 for efficiency.The analysis uses the best candidate with the highest mIoU.Source: §4.3 · p. 10, Table 4 · p. 10
  • Tenet has over 2 times fewer trainable parameters than DEVA, attributed to prompting foundation models rather than end-to-end vision-language training.The comparison also reports J &F scores on Ref-YouTube-VOS and Ref-DAVIS17.Source: Table 5 · p. 10, §4.3 · p. 10

5 CONCLUSION

The conclusion presents Tenet as a framework that separates referring and video from segmentation in RVOS. It uses temporal prompts and Prompt Preference Learning to adapt image-based foundation segmentation models.

5 CONCLUSION

  • Tenet addresses RVOS referring and video factors with temporal prompts selected by Prompt Preference Learning, while foundation models handle segmentation.The authors report effectiveness on standard RVOS benchmarks.Source: §5 · p. 10

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