Title: Uncertainty-quantified Rollout Policy Adaptation for Unlabelled Cross-domain Video Temporal Grounding
Venue: NeurIPS 2025
Accepted: yes
Keywords: Knowledge Transfer; Video Understanding; Temporal Grounding
Abstract: Video Temporal Grounding (TG) aims to temporally locate video segments matching a natural language description (a query) in a long video. While Vision-Language Models (VLMs) are effective at holistic semantic matching, they often struggle with fine-grained temporal
localisation. Recently, Group Relative Policy Optimisation (GRPO) reformulates the inference process as a reinforcement learning task, enabling fine-grained grounding and achieving strong in-domain performance. However, GRPO relies on labelled data, making it unsuitable in unlabelled domains. Moreover, because videos are large and expensive to store and process, performing full-scale adaptation introduces prohibitive latency and computational overhead, making it impractical for real-time deployment. To overcome both problems, we introduce a Data-Efficient Unlabelled Cross-domain Temporal Grounding method, from which a model is first trained on a labelled source domain, then adapted to a target domain using only a small number of {\em unlabelled videos from the target domain}. This approach eliminates the need for target annotation and keeps both computational and storage overhead low enough to run in real time. Specifically, we introduce \textbf{U}ncertainty-quantified \textbf{R}ollout \textbf{P}olicy \textbf{A}daptation (\textbf{URPA}) for cross-domain knowledge transfer in learning video temporal grounding without target labels. URPA generates multiple candidate predictions using GRPO rollouts, averages them to form a pseudo label, and estimates confidence from the variance across these rollouts. This confidence then weights the training rewards, guiding the model to focus on reliable supervision. Experiments on three datasets across six cross-domain settings show that URPA generalises well using only a few unlabelled target videos. Codes are given in supplemental materials.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nUncertainty-quantified Rollout Policy Adaptation for Unlabelled Cross-domain Temporal Grounding introduces URPA, a data-efficient and annotation-free approach for adapting video temporal grounding models across domains. The method begins with training on a labeled source domain using GRPO, a reinforcement learning framework, and then adapts the model to an unlabeled target domain using only 100–200 videos. By averaging multiple rollout predictions, URPA generates pseudo labels and estimates their uncertainty through variance. These uncertainty scores are used to weight the adaptation reward, enabling the model to focus on more reliable supervision. Experiments across six cross-domain benchmarks demonstrate that URPA enhances generalization and supports efficient, real-time adaptation without requiring ground-truth annotations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. URPA achieves high data efficiency, requiring only 100–200 unlabelled target-domain videos for adaptation, making it practical for real-world scenarios where annotated data is limited or costly to obtain.\n2. Theoretical soundness is well-established, with a rigorous analysis demonstrating that rollout variance approximates Bayesian predictive variance, thereby validating the use of uncertainty for reward weighting during adaptation.\n3. The method enables fully unsupervised adaptation, eliminating the need for ground-truth labels in the target domain by leveraging pseudo labels combined with uncertainty estimation for reliable self-supervision.\n4. The framework supports lightweight, test-time adaptation without the need for full retraining, making it highly suitable for real-time and on-device applications in resource-constrained environments.\nWeaknesses:\n1. The adaptation performance does not consistently improve with more unlabelled target samples. As shown in the paper, excessive adaptation can lead to overfitting on noisy pseudo labels, which limits the method’s scalability in large-scale unsupervised settings.\n2. The method focuses on temporal grounding optimization but does not explicitly address semantic misalignment between language queries and video content, which may limit its effectiveness in scenarios with ambiguous language or complex video semantics.\n\nQUESTIONS:\nHow can models handle ambiguous or vague language descriptions?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-15"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents URPA, a method for temporally localizing natural language queries in videos from diverse domains. URPA is first trained on a source domain with temporal annotations, using two losses: one to make the model answer following a specific format, and another to improve temporal grounding quality using extended ground truth intervals. To adapt to a target domain without annotations, URPA selects a small set of target videos, samples timestamps from the model's predictive distribution, and uses the mean as a pseudo-label. These pseudo-labels are weighted by a confidence, which is estimated from the standard deviation of the sampled timestamps. URPA achieves good results on three cross-domain temporal grounding benchmarks, outperforming off-the-shelf and source-only models, and even some UDA methods that use the full target set.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. Despite its simplicity, URPA is a principled approach that boosts temporal grounding performance on the target domain by leveraging pseudo-labels while accounting for their uncertainty.\n2. Unlike other test-time adaptation methods that adapt to each test input, URPA performs a one-time adaptation before inference using only a small subset of videos, which is a more practical scenario.\n3. The paper is generally well-written (with some typos, though), is easy to follow, clearly motivates the problem, and supports its claims with experiments.\n\nWeaknesses\n\n1. Although URPA, implemented on top of Qwen2.5-7B, consistently outperforms its ablated versions on Charades → Activitynet, it is unclear whether these improvements would generalize to other LMMs.\n2. Figure 4 shows that URPA's performance on the target domain starts to decrease after using 300 target samples. It would have been interesting to see how unsupervised domain adaptation baselines (e.g., UDA-TSL) perform when limited to the same randomly sampled target data as URPA.\n3. It would be interesting to evaluate the model on the source dataset after adaptation, to understand whether it retains its ability to perform temporal grounding in the source domain.\n\nQUESTIONS:\n1. Have the authors evaluated URPA with other LMMs than Qwen2.5-7B to understand whether the method's effectiveness generalizes across different models?\n2. Do other unsupervised domain adaptation baselines also benefit from training on a limited subset of the target dataset (possibly the same subset used by URPA) compared to using the full target set?\n3. How does URPA perform on the source domain after adapting to the target domain? \n4. According to Table 2, extending the start and end timestamps of the ground truth boundaries leads to a significant performance improvement. How was the value of $\\alpha = 0.1$ selected?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel approach for Unlabelled Cross-domain Temporal Grounding (UCTG), termed Uncertainty-quantified Rollout Policy Adaptation (URPA). Specifically: (1) The two tailored reward functions, combined with the introduction of GRPO, ensure robust grounding capability in the source domain. (2) A pseudo-labeling mechanism is designed based on GRPO, where label quality is assessed via uncertainty quantification, enabling effective target adaptation. Extensive experiments on multiple benchmarks demonstrate the superiority of URPA over existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The effectiveness of URPA in achieving cross-domain temporal grounding with limited samples is commendable. The judicious application of GRPO to this task provides an economical yet impactful research direction for the field.\n2. The quality assessment design for pseudo-labels is well-justified. Through rigorous theoretical analysis and comprehensive experiments, the authors convincingly demonstrate the validity of their evaluation strategy.\n\nWeaknesses:\n1. Motivation: The necessity of efficiency for UCTG is inadequately justified in lines 44-46. Additionally, the authors lack task-specific analysis - what are the key challenges constraining UCTG development?\n2. Table 1 Results: URPA achieves better performance than full-dataset methods in certain cases (e.g., TACoS→Charades), yet shows significant gaps in most settings. What causes this phenomenon?\n3. Performance at Scale: While URPA's low sample dependency is impressive, how does its performance evolve with increased samples (e.g., 1000-shot, 10000-shot)? A superior method should demonstrate balanced effectiveness and efficiency.\n4. There appears to be an error in the expression/formulation of Equation 3.\n\nQUESTIONS:\nAlthough the method introduced in this paper is reasonable and has been proven effective through experiments, due to doubts about the motivation and insufficient experiments, my current rating is 4.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method for cross-domain temporal grounding, addressing the challenges of domain shifts and the lack of annotations in unlabelled target domains. By leveraging Group Relative Policy Optimization rollouts and uncertainty estimation, the method enables efficient adaptation using only a small number of target domain videos. The authors introduce a pseudo-labelling mechanism, where predictions are averaged across multiple rollouts, and confidence scores are computed based on variance. Experiments across six cross-domain settings on three benchmark datasets demonstrate the effectiveness of URPA, achieving competitive or superior performance compared to full-dataset adaptation methods while maintaining resource efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths:\n\n1. The introduction of URPA as a data-efficient unsupervised domain adaptation method is a novel contribution to the temporal grounding task, particularly for real-world applications where computational and storage resources are limited.\n\n2. A rigorous theoretical analysis connects GRPO rollouts to Bayesian predictive variance, providing strong justification for the use of rollout variance as a measure of epistemic uncertainty.\n\n3. The method performs well with only a small number of unlabelled target videos, which is significantly more practical than full-dataset adaptation methods.\n\n4. Results are provided on six cross-domain settings across three datasets, demonstrating the generalizability and robustness of URPA. The ablation studies clearly highlight the contributions of uncertainty quantification and soft labeling.\n\n# Weaknesses:\n\n1. While the paper demonstrates the importance of URPA components, it does not compare GRPO rollouts with other reinforcement learning approaches or variants. This could provide further insight into the unique strengths of GRPO in the temporal grounding context.\n\n2. Although uncertainty quantification is used to mitigate noisy pseudo-labels, the method still assumes that pseudo-labels with low variance are reliable. This assumption may not hold in cases where low variance predictions are consistently incorrect due to systematic bias in the source model.\n\n3. The experiments suggest diminishing returns when increasing the number of rollouts (G>8). However, the computational trade-offs of using fewer rollouts versus the impact on uncertainty estimation are not fully explored.\n\nQUESTIONS:\n1. How does the choice of the exponential decay function in Eq. (9) affect the confidence score? Have alternative formulations for scaling uncertainty been tested?\n\n2. In scenarios where the target domain exhibits significant visual or semantic differences from the source domain, how robust is the method? Does the variance metric still reliably estimate epistemic uncertainty?\n\n3. The results show that performance peaks at G = 8 rollouts. Could you provide more insight into why increasing rollouts beyond this threshold does not yield better results? Is it due to computational inefficiency, or does it introduce noise?\n\n4. Although the method is unsupervised in the target domain, how does it perform when compared to fully supervised target training (beyond the 200-shot scenario)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes URPA, a data-efficient method for unlabelled cross-domain temporal grounding. It adapts a GRPO-trained model using a small number of unlabelled target videos via rollout-based pseudo-labels and confidence-weighted rewards. The method is theoretically grounded and achieves strong results on six benchmark transfer settings.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Clear problem motivation: The paper addresses a practical and under-explored setting, data-efficient unsupervised cross-domain temporal grounding, where only a small number (100–200) of unlabeled target videos are available.\n- Rich experimental results: Extensive evaluations are conducted on three datasets across six cross-domain settings, including detailed ablation studies and visualizations.\n- Theoretical support: The authors prove that rollout variance converges to Bayesian predictive uncertainty under mild assumptions, offering a principled justification for using rollout standard deviation as confidence.\n\nWeaknesses:\n- Comparative methods are dated: Most baseline methods are from 2020 or earlier, and a few others are recent. This limits the completeness of the comparisons.\n- Limited methodological novelty: The approach combines existing techniques, i.e. GRPO, MC Dropout-style uncertainty estimation [11], and pseudo-labeling with rollout averaging. While the combination is elegant and practical, it lacks fundamentally new learning formulations.\n- No adaptation to strong modern VLMs beyond Qwen2.5, and it is unclear whether it generalizes to other foundation models.\n\nQUESTIONS:\n1. Comparative scope: Most baselines are from 2020 or earlier. Could the authors include comparisons with more recent methods from 2024-2025?\n2. Model generality: The current experiments are based solely on Qwen2.5-7B. Can the authors clarify whether URPA generalizes to other VLMs or LLM-based temporal grounding models?\n3. Novelty clarifications: Since URPA builds on existing techniques (GRPO, MC Dropout, pseudo-labeling), can the authors better articulate what constitutes the core methodological novelty beyond this integration?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}