HiTeA: Hierarchical Temporal Alignment for Training-Free Long-Video Temporal Grounding
Authors: Xinyi Xu
Hongsong Wang
Guo-Sen Xie
Caifeng Shan
Fang Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: video temporal grounding;training-free;Long-video Understanding;vision-language models
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Temporal grounding in long, untrimmed videos is critical for real-world video understanding, yet it remains a challenging task owing to complex temporal structures and pervasive visual redundancy. Existing methods rely heavily on supervised training with task-specific annotations, which inherently limits their scalability and adaptability due to the substantial cost of data collection and model retraining. Although a few recent works have explored training-free or zero-shot grounding, they seldom address the unique challenges posed by long videos. In this paper, we propose HiTeA (Hierarchical Temporal Alignment), a novel, training-free framework explicitly designed for long-video temporal grounding. HiTeA introduces a hierarchical temporal decomposition mechanism that structures videos into events, scenes, and actions, thereby aligning natural language queries with the most appropriate temporal granularity. Candidate segments are then matched with queries by leveraging pre-trained vision–language models (VLMs) to directly compute segment–text similarity, thereby obviating the need for any task-specific training or fine-tuning. Extensive experiments on both short- and long-video benchmarks show that HiTeA not only substantially outperforms all existing training-free methods (e.g., achieving 44.94% R\@0.1 on TACoS, representing an absolute gain of 12.4%) but also achieves competitive performance against state-of-the-art supervised baselines under stricter metrics. The code is available at https://anonymous.4open.science/r/HiTeA_code.

Published as a conference paper at ICLR 2026
HITEA: HIERARCHICAL TEMPORAL ALIGNMENT FOR TRAINING-FRES LONG-VILE VIDEO TEMPORAL GROUND- ING
Xinyi Xu', Hongsong Wang", Guo-Sen Xie Caifeng Shan', Fang Zhao
Nanjing University, "Southeast University, NNajjing University of Science and Technology om, efshanenju. edu, en, fzhao@nju. edu.en
jjj. edu. en, hongsongwange edu. en, gsxi exmiehm@eee gmail.
ABSTRACT
Temporal grounding in long, untrimmed videos is critical for real-world video understanding, yet remains a challenging task owing to complex temporal struc- tures and pervasive visual redundancy. Existing methods rely heavily super- vised training with task-specific annotations, which inherently limits their scal- ability and adaptability due the substantial cost of data collection and model retraining. Although few recent works have explored training-free or zero-shot grounding, they seldom address the unique challenges posed by long videos. In this paper. we propose HiTeA (Hierarchical Temporal Alignment). a novel, training-free free framework explicitly designed for long-video temporal grounding. HiTeA introduces hierarchical temporal decomposition mechanism that struc- tures videos into events, scenes, and actions, thereby aligning natural language queries with the most appropriate temporal granularity. Candidate segments are then matched with queries by leveraging pre-trained vision-language models (VLMs) to directly compute segment-text similarity, thereby obviating the need for any task-specific training or fine- tuning. Extensive experiments on both short- and long-vides benchmarks show that HiTcA not only substantially outperforms all existing vaining-free methods (e.g., achieving 44.94% R@0.1 on TACoS, rep- resenting absolute gain of 12.4% but also achieves competitive performance against state-of-the-art supervised baselines under stricter metrics. The code is battps://githu /ttp://///ttttttu/ com/ cat.:/aaealaall/TTeeeea
available https::
INTRODUCTION
Video Temporal Grounding (VTG) (Gao et al. 2017) aims to locate the start and end timestamps of a video segment that semantically corresponds to natural language query. This task has broad applications in video surveillance (Tellex & Roy 2009), sports analytics for event and tactic local- ization (Kong et al., 2022), and egocentric video understanding (Grauman et al., (022).. However, in contrast to the short, trimmed clips that are predominantly studied in prior work, real-world videos are typically long and untrimmed, characterized by complex temporal structures and significant redundant content. These characteristics render uniform sampling strategies, mainstay of many existing methods, ineffective at capturing salient moments, thereby constraining their practical scal- ability.
Current nigh-performing VTG methods usually rely on fully supervised training with dense temporal labels (Lei et al. 2021 [Lin al.) 2023 Huang et al. 2024 Ren al. 2024). However, the collection of dense temporal annotations not only costly and subjective but also scales poorly with increasing video length, while the training large models computationally intensive. addition, supervised models are prone to learning dataset-specific biases, leading to limited generalization capability across diverse datasets (Guo et al. 2023). These limitations collectively motivate exploration of training- free approaches under the zero-shot video grounding (ZSVG) setting.
"Corresponding author.

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Published as a conference paper at ICLR 2026
Existing ZSVG methods are primarily designed and evaluated for short video clips. Some rely
on pseudo-labels to train lightweight models (Nam et al.) 2021 Lu et al. 2024 Li et al.
2025 )
whereas others directly apply pre-trained models without task task-specifi training (heng al.
2024
Luo et a 2024), as illustrated in Fig. Ta). However, such approaches often fail to explicitly model long-range temporal structure, consequently struggling to capture complex temporal dependencies or brief yet critical events in long, untrimmed videos. A promising alternative is to leverage the pow- erful general-purpose capabilities of large pre-trained video-language models (VLMs) (Bai et al. 2023 Wang et al., 2024b), which excel at video-text alignment. Although VLMs lack the inherent capability to temporally localize events (i.e., when they occur), their strength in recognizing seman- tic content (i.e., what happens) provides crucial prior for grounding, thereby enabling our method to operate in a fully training-free manner.
(a) Training- free Methods
[1201000 (180866)
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9.21 Vil
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Query:bul
dear oper?
Postprocess
Prediction
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Lim
Query"
Pewtprocess
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(b) Our Method
(c) Hierarchical Temporal Decomposition
Quary
Video
Query: Did leove the exit door open?
Module
Event-awarg
(0.147)
(147 480)
Canaltatte (47.160) [3A7 (001
Scene-aware
(0.70)
(70.47)
(147 189)
(302 400)
2B Mim
Action-aware
(0:70)
(70,22)
(62.177
(47.122) 2M
N152 (89)
(185 2023
(322
CR Maruuee
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Figure 1: Comparison of different paradigms for zero-shot video temporal grounding. (a) Training- free methods directly use pre-trained models on uniformly sampled frames. (b-c) Our HiTeA frame- work pipeline and Hicrarchical Temporal Decomposition (HTD) module.
These observations motivate central research question: How can we equip lightweight, off-the- shelf VLMs with explicit temporal structure to achieve accurate grounding in long videos, while entirely foregoing any task-specific training? The core challenge lies in bridging the gap between the VLM's s powerful semantic understanding and the need for nuanced temporal reasoning in long- video grounding.
To address this challenge, propose HiTeA, fully training- free framework inspired by the hi- erarchical search strategies employed by humans, as shown in Fig. [bb-c. Instead of processing videos uniformly, HiTeA introduces Hierarchical Temporal Decomposition (HTD) module, which adaptively partitions a video into multi-scale units, ranging from coarse events to fine-grained ac- tions, using off-the-shelf feature extractors. This constructs a temporal scaffold that emulates hu- man search strategies, making the framework first establish broad contextual understanding before focusing on more informative segments. To enable the efficient application of a frozen VLM long videos, pre-filtering step identifies a concise set of candidate segments for subsequent detailed evaluation. Finally, unlike common practices that employ off-the-shelf post-processing like Non- Maximum Suppression (NMS), we design dedicated Candidate Refinement module that adaptively integrates multi-scale cues from the scored candidates to produce the final, optimal prediction.
Crucially, HiTeA is entirely training-free, eliminating the necessity for any task-specific data collec- tion or model fine-tuning, Experiments show that HiTeA consistently outperforms existing training- free methods both short- and long-video benchmarks, underscoring that the explicit temporal structure is the key to unlocking the potential of VLMs for precise temporal grounding.
Our main contributions are threefold:
We propose HiTeA, pioneering, fully training-free framework that effectively ad- dresses temporal grounding long videos.
HiTeA introduces a Hierarchical Temporal Decomposition (HTD) strategy that equips off-the-shelf VL with explicit temporal reasoning without any task-specifie training.
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Extensive experiments both short- and long-video benchmarks demonstrate that ap- proach achieves state-of-the-ar performance under the zero-shot setting, with strong gen- eralization.
RELATED WORK
2.1
TRAINING-BANE BASED TEMPORAL GROUNDING IN VIDEOS
The development of video temporal grounding (VTG) has long been dominated by training-based paradigms. Early fully-supervised methods (Zhang al. 2020 Lin al., 2023) achieve strong performance but rely on expensive, fine Line-grinned temporal annotations. To reduce labeling costs, subsequent weakly-supervine
(Zheng al.] 2022b, Huang et al. 2023) and unsupervised ap- proaches (Wang et al. 2022, Zheng et al. 2023) attempt to learn from weaker signals. Despite alle- viating annotation burdens, they still require in-domain training, making them vulnerable dataset bias and imposing heavy computational costs, particularly when scaling to long-form videos.
Efficiency-oriented designs such as hierarchical or coarse-to-fine architectures (Hou et al. 2022 Pan et al.) (023) have been proposed to mitigate computation, but they remain tied to task-specific training pipelines, limiting their generalizability.
2.2 ZERO-SHOT TEMPORAL GROUNDING IN VIDEOS
To overcome annotation and retraining requirements, recent work has explored zero-shot temporal grounding. Existing approaches can be grouped into two streams.
Pseudo-supervised zero-shot methods generate automatic pseudo-labed to train grounding mod- els. For example, PSVL (Lu et al., 2024) synthesizes supervisory signals from text corpora and object detectors, attaining accuracy comparable to supervised baselines. However, because they still involve model training, these methods inherit the computational overhead and biases of training- based paradigms.
Training-fre free methods eliminate training altogether, instead leveraging frozen pre-trained models with clever inference strategies, such query decomposition (Zheng et al., 2024) boundary- aware proposals (Luo et al., 2024). While computationally lighter, they often lack explicit temporal modeling. Most rely uniform sampling, which struggles capture long-range dependencies and can easily miss short but salient events. This leads to inefficiency and performance degradation on complex, long-duration videos.
2.3 VIDEO UNDERSTANDING WITH VLMS
The rapid progress of large vision-language models (VLMs) BBai et al. 2023; Wang et al. 2024b) has further expanded video-text research. VLMs excel at high-level semantic understanding, pow- ering applications like video captioning and question answering. However, they are notably weak fine-grained temporal localization: VLMs can often tell what happens but struggle determine when. To address this gap, recent work augments VLMs with temporal awareness via positional encodings (Guo al. 2025), timestamp markers (Meinardus et al. 2024b] Chen et al. 2024). or temporal embeddings (Zeng et al.] 20244; Ren et al.) (024).. These strategies improve short-clip grounding scale poorly to long Videos due limited context length and rising computation.
3 METHODS
3.1
OVERVIEW
We propose HiTeA, training-free framework for temporal grounding in long videos that oper- ates through hierarchical temporal decomposition and query-conditioned matching. As illustrated in Fig. 2 our approach begins with the extraction of multi-scale visual features using pre-trained models such as ViT (Dosovitskiy et al.] 2020). DINO (Caron et al. 202I), and RAFT (Teed & capture complementary cues at different levels of abstraction. These features fed
Deng 2020
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into Hierarchical Temporal Decomposition (HTD) module construct set of candidate seg- ments spanning various temporal granularities. The resulting candidates are subsequently filtered and scored against the text query using frozen, pre-trained vision lasion-language model (VLM). Finally, a Candidate Refinement module merges and ranks these candidates to produce the final temporal predictions, preserving the fully training-free nature of the entire framework.
Candidate Refinement Module
Prediction: From 1475 to 1525.
Temporal Signal Construction
Progressive Merging
Rask
V
Seynent vorkat ion
Candidate Lon 110
0:47
U8
2.8
Franes variation
EM0 2013
6:11
Input Video
Villo
Flow Waynituts
VLM t
Sconing Condidates
Hierarchical Temporal Decomposition Module
0:99
9:28
Hierarch ical Alerging
Segment Points
Query:Did Did leave the exit door open?
Filter
Eali- Godd Syne Scorsioeel
Detection
Figure 2: Overview of the HiTeA framework. The Hierarchical Temporal Decomposition(HT)) module decomposes the input video into multi-scale temporal units. Candidate segments are filtered and scored by a pre-trained VLM in a query-conditioned manner. Final moments are generated through Candidate Refinement module without any training.
3.2
TEMPORAL SIGNAL CONSTRUCTION
3.2.1
FEATURE EXTRACTION.
Given untrimmed video V {fi..... ........ fr). which consists of T frames, extract multi-level features from various encoders: VIT for semantic context, DINO for structural transitions, and RAFT for motion dynamics. For frame ft, the extracted features are:
ΦvvI (fi),
v dino @DiNO(ft), vt flow = ØRAFT (St; fe+1),
(I)
where dvIT, ØINOO, and ØRAFT frozen, pre-trained encoders, and v/it. vdino, vflow their corresponding event-aware, scene-aweed and action-awar features at time t.
3.2.2 SIMILARITY CURVES.
To detect potential segment boundaries from extracted multi-level features, construct three com- plementary temporal signals, each tailored to capture transitions at specific temporal granularity.
Event-level Similarity (seveut)) Aimed mitigating the potential instability of frame-wise ViT features while capturing long-range event transitions, this signal is computed as the cosine similarity between the current frame's 's feature vi and the average feature representation of the most recent video segment. Let S be the set of frames from the start of the current segment to frame / 1.
Scene-level Similarity (sacene): This signal is designed to extract the shot-level structure of the video and is computed as the cosine similarity between consecutive frames' DINO features.
Action-level Similarity (saction): Since the optical flow feature flow inherently represents the mo- tion magnitude between frames fe and fe+1, we derive motion-aware similarity measure by com- puting the negative L2 norm of the flow feature. This formulation encourages lower similarity values during high-motion periods, which often correspond to action boundaries.
We define three complementary similarity metrics to capture event, scene, and action-level cues:
Vyit vit
dino
dino
eventt
86
scone
dino
paction
-I
flow
l|2,
where vyit B._T Ejes, vith is the averaged VIT feature over the current segment S,-1. Each signal is smoothed with Gaussian kernel suppress noises.
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3.3 HIERARCHICAL TEMPORAL DECOMPOSITION MODULE
For each similarity curve, candidate boundaries are extracted using granularity-specffi strategies; at the event level, boundaries identified local minima below threshold 7k, whereas the scene and action levels, the PELT (Killick et al. 2012) change point detection algorithm is employed capture nonlinear distributional shifts and complex temporal patterns. These candidate points indicate potential temporal transitions.
To enforce the hierarchical containment relation (event D scene C action), we introduce a merg- ing function MM-) that integrates higher-level boundaries into lower-level ones. As illustrated in Fig. Bia). for each higher-level boundary (blue), the nearest lower-level point (red) is replaced it lies within temporal tolerance Otherwise, the higher-level point is inserted. The resulting merged boundaries are highlighted green. This hierarchical merging process operates sequen- tially through function MM.), first combining event-level boundaries into scene-level, then inte- grating the resulting scene-level boundaries into action-level segments, ultimately yielding the final multi-granularity set pfinnl
This hierarchical design guarantees that fine-grained action segments remain aligned with broader structures. However, strict structural enforcement primarily benefits long videos with complex nest- ing. For short videos lacking deep hierarchies, such constraints may filter valid candidates. Thus, adopt adaptive strategy: applying M(-) long videos for coherence, while bypassing it for short videos to preserve boundary diversity (See in Appendix A.4.2).
(o) Merging
(b) Candidate
(47.252)
(147.48)
(8,147)
(70,47)
(158118))
(100 300
Figure 3: Key stages of our approach. (a) Hierarchical merging to form candidate segments. (b) Candidate refinement to achieve accurate localization.
3.4 SCORING CANDIDATES
Directly applying VLM all candidate segments is computationally expensive, particularly for long videos with dense proposals. To strike balance between accuracy and efficiency, we adopt two-stage scoring strategy that progressively prioritizes high-quality candidates.
MideoCLIP-based Filtering. We first compute coarse similarity score Selip between each can- didate segment (I,, f.) and the query Q using a VideoCLIP (Xu et al.) 2021 Wang et al. (022a)), which is designed for contrastive learning between video clips and text. To reduce Tragmentation and enhance semantic continuity, we merge adjacent segments with similar scores. Specifically, two neighboring segments and are merged if is selipl < B, where 3 is threshold. From the merged set, we retain the top-k segments per hierarchy based on Selip- This step significantly prunes the candidate pool while preserving segments with a high likelihood matching the query.
VLM--based Scoring. Then, the retained candidates evaluated using frozen, off the-hhe- VLM to obtain fine-grained similarity scores Sylm E [0, 1]. Although absolute scores may vary with prompt design, the relative rankings across candidates remain stable, enabling reliable candidate selection. This stage leverages the strong semantic alignment of VLMs in a fully training-free manner, combining the efficiency of VideoCLIP- based pre-filtering
3.5 CANDIDATE REFINEMENT MODULE
We observe that the reliability of VLM-bbseed scoring is sensitive both segment duration and prompt formulation. Extremely long or short segments tend to yield unreliable scores due to frame sampling limitations uneven content density, while prompt semanties introduce systematic
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biases. To mitigate these issues and explicitly adapt to the HTD DrD--eeerated candidates, propose refinement module that, distinct from conventional proseprocsssing, integrates cross-modee similar- ity with intro consistency, followed by progressive merging and ranking.
Score Fusion. Each candidate segment e; is assigned unified relevance score by combining the VLM-based similarity and the VideoCLIP-basse temporal consistency. While the VLM provides perior semantic reasoning. its output confidence scores often exhibit discretization, resulting in tied rankings among candidate segments. To mitigate this, we leverage the continuous nature of Selip to provide fine-grained discrimination. This fusion strategy ensures that the VLM's semantic align- ment remains dominant, while Selip serves as critical tie-breaker, resolving scoring ambiguities without compromising the primary semantic order.
Before fusion, the Selip is normalized to the range [0, 1] ensure scale compatibility. The final score is computed as:
Shnll À Sylm + (1- A) Selip
(3)
where À balances the two terms. This fusion mitigates the impact of outlier scores and reinforces segments that are both semantically relevant and structurally coherent.
Progressive Merging. To exploit complementary information across hierarchical levels (e.g., an action within a relevant scene), we introduce query-aware, progressive merging strategy that en- ables cross-level interaction, in Fig. Bb. Segments at different granularities- events-are are considered jointly during merging, guided by their semantic relevance to the query. Specifically. two candidates from the same adjacent levels merged if they temporally close and have high cuury-cooditioned similarity. The merging decision accounts for both boundary proximity and consistency of their VLM scores, ensuring semantic coherence in the merged seg- ment. This process is applied iteratively, allowing, for example, a high-scoring action segment to merge with an overlapping scene segment to form richer candidate that integrates multi-level cues. The merged segment inherits a refined confidence score computed from its constituents, reinforcing segments that benefit from hoos-hhirarchhcal fusion. Further details in Appendix A.2.2
scenes, and
Ranking and Output.
The refined set of temporal segments Stnal = {c:} is ranked according to
(i) their updated scores 8fiall and the top-ranked segment selected as the final prediction:
(f, fe) = arg max 4:ESmmal
(4)
final
This refinement stage effectively complements HTD module by leveraging cross-level information and the scores from the previous stage to generate temporal predictions.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
evaluate HiTeA on two categories of benchmarks. For short-videe temporal ground- ing, we use Charades-STA (Sigurdsson al. 2016). originally introduced for videoQA. comprising 1.3K test videos with 3.7K queries of houschold activities, QVHighlights (Lei et al. 2021 con- taining over 1.5K validation and 1.5K test videos with diverse topics that require models to detect all relevant temporal spans and their saliency scores, and ActivityNet--aptions (Krishna et al. 2017), repurposed from dense video captioning, containing 4.9K open-domain videos with 17.0K annotated descriptions. For long-video temporal grounding, we use Ego4D-NLQ (Grauman et al. 2022) with 415 egocentric validation videos, and TACoS (Regneri et al. 2013). a compact, densely- yaanottted cooking dataset with 25 test videos.
Datasets.
Evaluation Metries. Following established practices in temporal grounding, we report Recall @ at multiple IoU thresholds and the mean Intersection-over- Uvin-nnoon (mloU). The evaluation thresholds are set to 0.3, 0.5, 0.7 for short videos and 0. O.3, 0.5 for long videos, reflecting the higher com- plexity of long videos and the difficulty of accurately retrieving very short segments with current zero-shot methods. The mloU metric complements these by providing an overall accuracy measure, calculated by averaging the loU across all predictions.

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Published as a conference paper at ICLR 2026
Table 1: Performance comparison on long-video temporal grounding benchmarks. 'Sup' denotes the supervision type: FS (Fully-Supervise)) ZS (Zero-Shot) Method requires training.
Ego4D-NLQ
TACoS
Method
Sup
0.1
0.3
0.5
mloU
0.1
0.3
0.5
mloU
2D-TAN Zhang 2020
FS
4.33
1.83
3.39
47.59
37.29
25.32
Moment-DEi (Lei el al.
2021
FS
4.34
1.81
3,53
24.67
11.97
25,49
UniVTG (Lin et al.
2023
FS
7.28
3,95
4.91
51.44
34.97
33.60
UniVTG Lin ct al.
2023
ZS
6,48
3.48
4.63
5.17
1.27
4.40
Mr.BLIP Mcinardus
2024a
ZS
6,49
3.20
5.37
24.59
14.32
17.94
TimeSuite Zeng ct al.
2024)
ZS
0.88
0.43
0.94
6.75
2.50
5.71
UniTime- -ero
(L
2025
ZS
14.67
7.38
10.18
50.06
31.54
33.38
Luo et al. 2024
ZS
27.49
11.20
5.57
HiTeA (Qwen2.5-VL)
7S
20.12 10.39
6.04
8.12
44.94
29.08
16.10 19.79
Table 2: Performance comparison on short-video temporal grounding benchmarks. Supervision
(Sup): FS, WS Weakly-Superrisee US (Unsupervised), ZS. Method requires training
Charades-STD
ANet-Caption
Method
Sup
0.3
0.5
0.7
mloU
0.3
0.5
0.7
mloU
2D-TAN (Zhang al. 2020
FS
57.3
45.8
27.9
41.0
60.4
43.4
25.0
42.5
Moment-Dre DEIK (Lei et al. 2021
FS
62.1
48.2
25.3
42.3
52.6
32.5
15.3
37.8
VTimeLLM (Huang et al.
2024
FS
55.3
34.3
14.7
34.6
44.8
29.5
14.2
31.4
CPL iZeng et al. 2022b
WS
66.40
49.24
22.39
55.73
31.37
CNM (heng el al 2022a
WS
60.39
35.43
15.45
55.68
33.33
Huang al 2023
WS
69.16
52.18
23.94
45.20
58.07
36.91
41.02
PSVL Nam ct al. 202
46.47
31.29
14.17
31.24
44.74
30.08
14.74
29.62
PZVMR Wang et al. 2022
US
46.83
33.21
18.51
32.62
45.73
31.26
17.84
30.35
DSCNet Liu el all 2022
US
44.15 28.73
14.67
47.29
28.1
SPL (Zheng et al.
2023)
US
60.73
40.70
19.62
40.47
50.24
27.24
15.03
35.44
UniVTG Lin et al.
2023)
ZS
44.09
25.22
10.03
27.12
11.10
4.06
16.86
VideoChatGPI.7B
Maaz
2023
20.0
1.7
13.7
13.6
6.1
18.9
VTG-GPT Xu ct al.
2024
ZS
59.48
43.68
25.94
39.81
47.13
28.25
12.84
30.49
Lu et al.
2024)
ZS
47.74
34.62
20.16
32.97
49.26
31.45
15.27
33.25
Uni Time Zero
Li ot al.
2025
ZS
59.09
31.88
52.19
22.77
14.14
27.31
Luo 2024)
56.77
42.93
20.13
37.92
48.28
27.90
11.57
32.37
TFVTG (Zheng
2024
67.04
49.97
24.32
44.51
49.34
27.02
13.39
34.10
Xu et af. 2025)
ZS
58.2
38.4
21.6
36.5
48.1
31.1
14.9
30.8
HiTeA (Qwen2.5-VL.)
7S
69.62
48.52
25.59
46.29
54.46
31.1
16.57
37.93
Implementation Details. Our framework is implemented in PyTorch. We use Qwen2.5-VI. 7B (Bai et al. 2023) as the base VLM, and VideoCLIP-XL. (Wang et al. 2024a) for pre-filtering For feature extraction, and RAFT-Large for action-level features. The frame sampling rate is set to FPS for Charades-STI and FPS for all other datasets. Most hyperparameters remain unchanged across all datasets.
employ ViT-B/32 for event-level features, DINO-v2 for scene-level features,
Adaptive Configuration. We adjust the Hierarchical Merging (HM) module based on video du- ration. HM is enabled for long-video datasets enforce structural consistency, and disabled for short-video datasets to maximize candidate diversity from different feature levels. Additional im- plementation details are provided in Appendix A.33 while supplementary experiments, including analyses on model selections and hierarchical merging effects, are in Appendix A.4
4.2 COMPARISON WITH THE STATB-OF-THE-ARTS
HiTeA establishes a new state-of state-of-the-ar for zero-shot temporal grounding, consistently outperform- ing prior methods both long- and short-video benchmarks (Tables T and 2).

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Published as a conference paper at ICLR 2026
Table Performance comparison on QVHighlights temporal grounding benchmarks. Supervision (Sup): FS, WS Wreakly-Supervisety US (Unsupervised). ZS. Method requires training
Test
Val
Method
Sup
RI@00. RI 600.
mAP@0.5 mAP@avg R1600.5
R00
mAP@0.5 5 mAP@avg
Moment-DEII DDTRR (Lei et al.
2021)
52.9
33.0
54.8
30.7
54.2
33.4
55.4
31.1
UniVTG (Lin el al. (2033
58.9
40.9
VTimeLLM THangg
2024
47.2
29.3
47.3
27.4
48.8
29.5
49.3
Mr.BLIP (Memaardus et al.
2024a)
FS
74.8
60.5
76.1
63.4
CNM Zheng 2022a
WSI
14.1
4.0
11.8
CPI. Zheng 20226
WS
30.8
10.8
22.8
CP1
Kong al.
2023
WS
32.3
11.8
23.7
PZVMR Wang al. DSCNet
2022
US
14.2
4.9
15.7
4.6
12.6
5.1
16.2
5.3
[Lu al ZU2Z)
US
12.3
3.5
10.4
2.7
TimeSuite Zeng al. 2024)
ZS
12.3
9.2
V.imeLLM Huang el al. 2024
ZS
26.1
11.2
(Li al. 2025
ZS
41.0
31.5
Diwan et al
2023)
ZS
48.3
31.0
47.3
28.0
Moment-GP
[UU et al. 2025
ZS
58.3
37.7
55.1
35.0
58.9
38.6
55.7
35.9
HiTeA (Qw)n2.5-VL)
ZS
62.3
42.2
60.7
37.0
64.1
43.2
60.2
37.3
Performance Long Videos. HiTeA sets new state-of-the-art for zero-shot temporal ground- ing on long videos. groundbreaking achievement is made on the Ego4D-NLQ benchmark: achieves an mIoU of 8. 12%, surpassing all fully-supervised baselines. To our knowledge, this is the first time fully training-free method has been successfully applied to this dataset, and it deliv- superior performance. On TACoS, HiTeA also dominates, achieving 44.94% at R 1@0.1-neen doubling the best prior training-fiee ffree result. These results demonstrate that our hierarchical temporal decomposition (HTD) strategy effectively captures the complex temporal structures long videos, proving that explicit hierarchical reasoning can outperform data-intensive supervised training.
Generalization Short Videos. Notably, although HiTeA specifically designed for long videos, it also generalizes remarkably well to short-video settings. On Charades-STA, it achieves 69.62% at R@0.1 and 25.59% at R@0.5, surpassing existing zero-shot approaches and even matching several weakly- and fully-supervised methods. On ActivityNet-Captions HiTeA also demonstrates clear superiority, outperforming the strongest zero-shot baseline. Furthermore, in QVHighlights, HiTeA performs significantly well across both test and validation splits. On the Test split, HiTeA achieves 60.7% at mAP@0.5 and 37.0% at mAP@avg, which is a strong result, surpassing many of the sfate-of-the-art zero-shot methods.
The consistent gains across datasets of varying durations and complexity confirm the efficacy of HiTeA's training-free architecture, particularly the synergy between hierarchical candidate gener- ation and query-aware VLM scoring. Our approach effectively bridges the gap between semantic understanding and temporal localization without task-specific training.
4.3
ABLATION STUDIES
To evaluate the contribution of each component, we conduct comprehensive ablation studies on the TACoS and Charades-STA datasets, representing long and short video benchmarks, respectively.
4.3. EFFECTIVENESS OF INDIVIDUAL MODULES
Table 4 reports the ablation results for Hierarchical Temporal Decomposition (HTD) and Candidate Refinement (CR) on TACoS and Charades-STT The baseline without HTD or CR (first row), which applies uniform segmentation, achieves the lowest performance, demonstrating that naive temporal partitioning fails to capture complex temporal dynamics both long and short videos. For configu- rations without CR, the model selects the single candidate with the highest fusion score. Conversely,
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Table 4: Ablation Study on Hierarchical Temporal Decomposition and Candidate Refinement.
HTD
TACoS
Charades-STA
CR
Event
Scene
Action
0.1
0.3
0.5
mloU
0.3
0.5
0.7
mloU
41.18
21.41
8.25
14.87
55.65
33.17
15.11
35.07
39.34
21.29
10.76
15.64
63.92
40.00
20.94
41.28
/
30.21
18.78
10.21
13.01
59.89
38.82
20.75
40.35
v
30.85
19.74
11.90
13.73
60.13
40.03
20.38
39.83
/
34.32
22.11
13.53
15.34
60.81
41.37
22.18
40.76
35.52
22.25
13.07
15.61
62.82
43.04
21.69
41.40
x
34.97
21.49
11.37
14.98
63.31
42.58
23.01
41.85
38.44
24.41
14.35
16.99
62.69
43.31
22.66
41.61
45.03
23.21
10.32
16.67
67.63
39.46
20.00
43.93
44.94
29.08
16.10
19.79
69.62
48.52
25.59
46.29
enabling CR activates the iterative merging process, allowing adjacent segments to be aggregated based on semantic similarity to form the final prediction.
Hierarchical Temporal Decomposition. Examining individual HTD layers reveals their com- plementary strengths. Event-level segmentation achieves high recall at loose thresholds (39.34% R@0.1 on TACoS, 63. 33.92% @@0.3 on Charades-STA), reflecting the fact that coarse segments are more likely to overlap with ground-truth intervals. However, its precision decreases at stricter thresh- olds, indicating limited ability to accurately localize fineigrained boundaries. In contrast, Action- level segmentation achieves the highest R &@0.5 on TACoS (11.90%) highlighting its utility for pre- cise temporal localization. Scene-level segmentation underperforms on both datasets, likely because TACoS and Charades-STI focus on cooking or household activities, where scene transitions are rare and most queries do not correspond to scene changes.
Combining multiple layers consistently improves performance, illustrating the complementarity of different temporal granularities. Two-level combinations, such Scene+Action, consistently out- perform any single layer by capturing both coarse coverage and fine fine-grained details. Extending the decomposition to three levels yields further gains on TACoS (mloU: 16.99%), whereas the improve- ment on Charades-STA is marginal (mloU:41.6 %). This indicates that multi-level decomposition is most beneficial in long videos, where temporal structures are more complex, while short videos are often simple enough that a single or two-level decomposition suffices.
Candidate Refinement. The CR module, designed to adaptively refine the candidates produced by HTD, brings consistent and substantial gains across benchmarks. It boosts mloU from 16.999 to 19.79% on TACoS and from 41.61% % to 46.29% on Charades-STA, with gains observed at all IoU thresholds. Comparing uniform w/o CR and uniform CR, observe that CR leads to significant gains. The uniform + w/o CR baseline performs worse due to poor segmentation, while uniform CR improves mloU by refining boundaries. The full all w/CR configuration further enhances performance, confirming CR's s role in handling ambiguities and providing more accurate segmentation. These results demonstrate that CR effectively filters noisy segments and sharpens temporal boundaries, confirming its role as crucial complement to HTD by exploiting cross-level interactions and query-aware merging for more robust and precise localization.
4.3.2 EFFICIENCY OF VIDEOCLIP-BASED PRE-FILTERING
We quantitatively evaluate the efficiency of our pre-filtering stage, designed to avoid the computa- tional cost of exhaustively scoring all candidate segments with VLMssshown in Table The reduc- tion ratio correlates with video length and structural complexity. On Ego4D-NLQ. featuring long egocentric videos with dense content, filtering reduces the average number of segments from 146.68 to 9.0 per video, corresponding to a 93.9% reduction. Similarly, TACoS and ActivityNet-Caption reductions of 76.6% and 79.4%, respectively.
These results confirm that our filtering preserves high-quality candidates while pruning irrelevant ones, reducing the number of VLM score computations from hundreds to single digits per video.

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Table 5: Efficiency Improvement of VideoCL.P-based Filtering
Measure
Charades-STA
ANet-Captions
TACoS
Ego4D-NLQ
Segments (w/ Filter)
9.75
22.26
38.19
146.68
Segments (w/ Filter)
8.17
4.59
8.92
9.0
Reduction (%)
16.2
79.4
76.6
93.9
Effect
Effect of
0.45
0.40
0.33
0.30
- mbb
0.2
0.4
0.6
0.2
0.0
Figure 4:
Parameter sensitivity analysis. Left: the effect of 3 on VideoCI.P-basee pre-filtering Right: the effect of X on the final scoring function.
This makes training-free temporal grounding computationally tractable and scalable, particularly for long and complex videos.
4.4 PARAMETER SENSITIVITY
We analyze the sensitivity of our method on the TACoS dataset with respect to two key hyperpa- rameters: the merging threshold 3 in the VideoCI.IP- pre-filterigg stage and the weighting parameter À in the CR module. Figure Recall @ K and mloU in different parameter values.
For 3 (Figure left), performance remains relatively stable in the range [0.2, 0.6). with the best mloU observed at 3 0.5. This indicates that the pre-filtering threshold is not highly sensitive, since the hierarchical decomposition already provides diverse proposals. However, when 3 exceeds 0.6, semantically distinct moments tend to be merged, which harms localization accuracy.
For À (Figure right), the highest mloU is achieved at À 0.99. This validates that the VLM score Sylm is the dominant factor for accurate localization. However, as the VLM tends to output discrete confidence values, the VideoCLIP score Selip provides essential complementary cues. By assigning minimal weight (1%) to Sclip. we effectively break ties among candidates with equal VLM scores without disrupting the high-quality semantic alignment provided by the VLM.
4.5 INFERENCE EFFICIENCY
To evaluate scalability, we benchmarked HíTcA against representative baselines on both long-video and short-video datasets. In our analysis, HiTeA achieves stable online inference latency (~7-8s) that is largely insensitive to video length. This significantly outperforms dense scanning or proposal- based baselines, which often suffer from linear or quadratic complexity scaling on long inputs. The detailed runtime analysis and full comparison table are provided in Appendix A.6
CONCLUSION
We have presented HiTeA, a fully training-free framework for temporal grounding in long videos, designed to tackle the challenges of complex temporal structures and high computational costs. Cen- tral to approach is the Hierarchical Temporal Decomposition (HTD) strategy, which structures videos into a coarse-to-fine hierarchy. This allows off-the-shelf VLMs to localize queries with high precision, completely without supervision. Extensive experiments validate our approach, showing state-of-thee thetthh--rttttt zero-shot performance. HiTeA's strong results on long-vide benchmarks highlight its scalability and robustness, paving the way for practical, efficient video understanding systems.
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REPRODUCIBILITY STATEMENT
To ensure reproducibility, we have taken several measures. A complete description of the pro- posed framework, and further implementation details, including feature extraction, hyperparameter settings, and dataset-specific preprocessing, are documented in Appendix. The full source code available the link: httts:://ithuk: com/ came wwwil11//iiiiiii.
While pre-trained models and raw datasets must be obtained from their official sources due to size constraints, we provide examples of processed data formats and will release all intermediate files generated during preprocessing to make replication straightforward. In addition, all pre-trained models used in our work are publicly available, with their versions and usage explicitly specified the appendix. These resources intended to facilitate faithful reproduction of our results and foster future research in zero-shot temporal grounding.
ACKNOWLEDGGENNT
This work was supported by the National Natural Science Foundation of China (No. 62476124, 62276134, 62172090), Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China JYB2005XDXM902), Natural Science Foundation of Jiangsu Province (No. BK20242015, BK20230833), and the Gusu Innovation and Entrepreneur Leading Talents (No. ZXL2025322).
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A APPENDIX
A.1 QUALITATIVE RESULTS.
A.1.1 LONG-VIDEO EXAMPLE
Query: large plate is removed for the mango.
TACoS
Length
6T:(238, 248)
Event
7
43
57
165
238
321
372
Scene
62
111
154159
187
248
309
333
353
Action
30
172
(57. 165) S 1344
0.
(248. (248.309)
5.4:0.1462
(62,103)
Say 1350
Spa 5op00.1344 0.1
Score
(238,321) Sup 0..1633
00.2
(238. 248)
Ra,10, 1430 5pm,
(187,236)
(165,238) Rup 00.191
Rymn
(62,111)
0.
(103,111)
Rep 0.132 4pm 0.1
Refine
(238,248) 0.794 II (248,309) 0.199 II (187,321) 1..00.
Prediction:(238,244
Figure 5:
A qualitative example of the proposed hierarchical temporal decomposition and matching process for the query.
Figure E illustrates the hierarchical reasoning of our framework for the query "A large plate is removed for the mango." Multi-scale candidate boundaries are first generated by the HTD codulee-venttie (blue), scene-level (green), and action-level (yellow) splits. Irrelevant can- didates are filtered by VideoCI.IP, and the remaining segments are scored by frozen VLM (Qwen2.5-VL), where Selip provides coarse semantic alignment and Sqwen reflects more precise query-conditionn cond-ccond relevance. The CR module then integrates and re-ranks these candidates to pro- duce the final prediction.
In this example, the short but critical interval [238, [48]_correppodiin to the instantancous plate removal receives the highest confidence score (s = 0.79) after refinement, whereas longer and more ambiguous segments (e.g., /248, 309/) assigned lower scores. This demonstrates that our framework effectively localizes fleeting yet semantically important events in long videos by combining hierarchical temporal reasoning with coarse-to-fine semantic matching from a general- purpose VLM, all without any task-specific training.
A.1.2 SHORT-VIDER EXAMPLES
Figure presents two qualitative examples from short-video datasets (Charades-STAN, Since short videos typically lack significant scene-level changes, we selected two illustrative clips to show- case different decomposition strategies: single-level action decomposition (top) and two-level ac- tion+event decomposition (bottom).
The top example demonstrates that action-level decomposition alone can successfully capture fine- grained temporal transitions. Despite only three action segments and relatively low VidcoCLIP scores, the relevant candidates are retained, and the frozen Qwen VLM accurately matches the textual query the corresponding video segments.
In the bottom example, combining event-level and action-level decompositions captures semantic variations comprehensively. resulting in precise segment boundaries. Along with Figure 5 these examples highlight that our framework can flexibly adapt to videos of different lengths, demon- strating robust localization capability and strong generalizability across both short and long videos.
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Qhery: person looks at picture on the desk,
Charades STA
Lenggh
6T:(8.1, 21,2)
>ctiin
944
20.4
31.97
Score
(9.4, 20.4)
9.49.4) 11p Sque
Ray117.33
Reve-0.0
(9.4 s:0.7937
31,97) 5:0.5960 5960
Prediction:(9) (9,21)
Refine
(20.4, 31,97) 19p-19.90 9..
(0.9.4) s:0.1005
Queryyperson starts sneezing.
Charades- STA
Length
(2: 35.0)
Event
23
Action
18.6
Score
Scoring
(15, 23)
(23, 35)
0.124 00
1oy10.1362 5pmx
(18.6, 23)
(23, 25.8)
0.134
5a, :4442
1334 5p..1.22
5pm
[0, B)
As,00.1280 Sgm. 0.2
(15. 18.6)
0c.00.1166 002
(23, 35)
s:0 --0.9233
Refine
Refining
(15, 23)
$:0.2983
Prediction: 35)
(18.6,23)
s:0,1993
Figure 6: Qualitative examples on short-video datasets. The top case illustrates the result obtained using only the action-level decomposition, while the bottom case shows comparison between event-level and action-level results.
A.2
MAIN ALGORITHMS.
A.2.1 HIERARCHICAL MERGING
Algorithm. We enforce hierarchical consistency among event, scene, and action boundaries through a top-down merging strategy. The procedure summarized in Algorithm
Algorithm Hierarchical merging temporal boundaries
1: Input: Feature similarity curves gwit Sflow gdino, video duration T, tolerance a
2: Output: Final boundary set plinal
3: Extract candidate boundaries:
Cevent + find.minima(Srt)) (serne + pelt (Sffon)
Caction <- (-pet(Sddinn)
4: Merge nearby points: Pk <- merge numbers(C*. T), k E fevent, scene, action]
5: Hierarchical merging: pacene t- M(Pevent, paoone (.)
paction + M(pscone paction a.)
6: Return pfinal (pevent pocene paction
Boundary Extraction Functions. The function findmminima selects local minima from sim- ilarity curve to generate candidate boundaries, while pelt implements the PELT change point detection algorithm, which identifies temporal transitions based on curve statistics. The function merge numbers enforces minimum distance between boundary points by merging those that overly close. The hierarchical merging function M(.) integrates boundaries across levels: for
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each higher-level boundary, the closest lower-level point is replaced if within temporal tolerance a, otherwise the higher-level point is inserted.
Rationale for Multi-Granular Boundary Detection. Different granularity levels correspond to distinct temporal dynamics. Event-level boundaries reflect long-term semantic shifts and can be reliably captured using local minima in VideoCLIP-based similarity curves. Scene- and action- level boundaries represent shorter and more frequent transitions; for these, PELT efficiently detects multiple change points by modeling curve statistics rather than relying solely on local extrema. This strategy balances computational efficiency with boundary accuracy.
Hierarchical Merging and Adaptation. Candidate boundaries are initially extracted indepen- dently from each feature space (CLIP-ViT, RAFT flow, DINOv2). The hierarchical merging func- tion .M(-) then integrates higher-level boundaries into lower levels, ensuring fine- grained action segments align with broader temporal structures. In long videos, this merging is essential to main- tain global coherence and prevent error accumulation. In short videos, however, hicrarchical merg- ing is disabled: the smaller number of segments and sufficient diversity across feature spaces make independent candidates preferable. This design choice, empirically validated in Table allows the framework to adapt to different video lengths while preserving both precision and robustness in temporal localization.
A.2.2 CANDIDATE REFINEMENT
Algorithm 2 Candidate refinement via Progressive Merging
Input: Initial candidate segments {ci} with scores {s;}, score threshold
2: Output: Refined candidate set Stina with updated scores
3: while merging possible do
4: for each pair (c,, ey) not yet merged do
5:
|;; šq < and Cj are temporally proximate then
6:
Compute weights
s:/(s; $j). b $3/(5; +
7:
Update boundaries of merged segment by weighte averaging Assign merged score Snew 8; + b b.8j Replace (Ci,C)) with new merged segment
8:
9:
10: end if
11: end for
end while
13:
Rank refined candidates Sänal by updated scores
return Sünal
The candidate refinement stage addresses two key challenges arising from hierarchical temporal decomposition (HTD) and the limitations of off-the-shelf VLMs. First, HTDD-generated segments may not align perfectly with every video's inherent structure, and query-relevant content often spans multiple hierarchical levels. The initial overlapping candidates from different hierarchical levels may require consolidation to produce temporally coherent predictions. Second, short segments may lack sufficient visual context for reliable VLM assessment, leading to under-scoring, while long segments may dilute salient information with irrelevant content-2 known weakness of VLMs in long-video understanding.
To mitigate these issues, we introduce merging algorithm, merge segments all, which con- solidates candidates based on score similarity (within threshold #)) and temporal overlap. The pro- cess operates similarly to agglomerative clustering: segments satisfying these criteria are merged, with new boundaries computed as confidence-weighted average. This design ensures that seg- ments with higher VLM scores exert greater influence on the final localization, thereby correcting for sampling bias and reconciling over-segmentation across hierarchical levels. The final candidate set, Stnat- is ranked by a unified score $tial X Sylm + (1 A) Selip. balancing cross modal relevance and temporal coherence in the tap-ranked predictions.
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A.2.3 DISTINCTION BETWEEN MERGING STRATEGIES
Our framework incorporates distinct merging processes: the Hierarchical Merging in the Tem- poral Decompasition (HTD) stage and the Progressive Merging in the Candidate Refinement (CR) stage. The apparent complexity of having two merging steps is necessary because they serve fun- damentally different purposes-one is video-centric and structural, the other is query-centric and semantic.
HTD Merging (Structural and Video-Centricc. The merging performed in the HTD module (as detailed in Algorithm I and Appendix (A.2.1) is query-agnostic and operates offline. Its primary goal is to decompose the video based on its intrinsic visual features (derived from VIT. DINO, RAFT) to mine the inherent temporal hierarchy (actions, scenes, events). The merging process here ensures that all generated candidate segments are structurally and physically coherent. These segments act as high-quality, pre-computed building blocks for the entire framework. Since HTD ineepednde,,,n,tt, it cannot predict the final semantic boundary requested by the user.
CR Merging (Semantic and Query-Centrie). In contrast, the Progressive Merging in the CR module (in Algorithm ( and Appendix(A.2.2) is query-centrie and operates online. This step relies on the VLM and VideoCLIP similarity scores. representing a deep semantic interaction between the query and the video content. A single natural language query often corresponds to a semantic event that spans multiple adjacent HTD structural units (e.g., a "cooking" query may cover HTD's separate "chopping" and "frying" units). The CR merging step dynamically re-aggregates these HTD units that share high semantic relevance to the specific query, ensuring the output segment captures the complete semantic scope of the user's intent.
Rationale for Two-Stage Merging, The necessity of the two-stage approach lies in efficiency and accuracy: HTD provides the highly refined, structurally sound components, which reduces the search space significantly. CR then utilizes the powerful VLM to ensure that these structural components are correctly and fully assembled into the semantically accurate boundary required by the query. Both stages are indispensable for achieving robust temporal grounding performance.
A.3 IMPLEMENTATION DETAILS.
This section provides a comprehensive account of the experimental setup, covering feature extrac- tion. model configurations, hyperparameter settings, and dataset-specific strategies. We further clude dataset statistics and analysis to contextualize the evaluation and ensure transparency and reproducibility of our results.
A.3.1 FEATURE EXTRACTION AND SIGNAL CONSTRUCTION
Frame Sampling: Videos are subsampled at rate of 5 FPS for Charades-STi and FPS for all other datasets to balance computational efficiency and temporal resolution.
Feature Encoders: We use frozen, off-the-shelf models:
VIT (CLIP): ViT-B/32 to extract frame-wise semantic features (vtlt E R513). Its language-aligee aligned embeddings provide high-level event context when aggregated over several seconds.
DINOv2: DINOv2-base (ViT-B/14) extract structural features that are sensitive to scene layout and viewpoint changes, serving as scene-level signal. Each frame is represented as 768-dimensional vector (villo E TR766).
RAFT: Optical flow between consecutive frames is extracted using a pre-trained RAFT model (large variant). The magnitude of the flow field is spatially averaged to produce a frame-level motion intensity score (yflow E IR).
Rationale for backbone selection: We deliberately choose three widely used encoders that emphasize complementary cues different temporal scales.
For event-level semanties, aligned with natural language and has strong zero-shot transfer, making the aggregated features several seconds natural descriptor of high-level activity context.
CLIP ViT-B/32 as it produces image embeddings
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For scene-level structure, adopt DINO-v2, sensitive to changes in layout, background, and viewpoint, thus providing robust signal for shot- and like transitions.
whose self-supervised ViT features
For action-level motion, we employ RAFT-Large. high-accuracy optical-flow model whose dense flow fields summarize instantaneous motion; the magnitude of the flow directly reflects short, local movements that correspond action boundaries. Our HTD module only relies on these semantic/srrucuura/mttooo properties, so other en- coders providing similar cues could be substituted without modifying the overall al- gorithm.
To empirically justify this selection, we conducted an ablation study in Section A.4.3 The results demonstrate that these models are chosen as established representatives with proven generalization capabilities, thereby maximizing the model's performance.
This three-stream design makes HiTeA naturally backbone-agnostic: any encoders that provide comparable event-, scene-, and action-level signals can be plugged into our HTD module without changing the rest of the framework.
A.3.2 HIERARCHICAL TEMPORAL DECOMPOSITION
Boundary Detection:
Event-level (Cevent): The video is first partitioned into coarse segments of uniform duration. Within each segment, local minima in the gevent curve are identified as potential event boundaries, capturing major semantic shifts.
Scene-level (Cscene) & Action-level (Caction): The PELT change point detection al- gorithm is applied to the gaeeue and 5 gaction curves, using a radial basis function (RBF) kernel. The penalty term empirically set for both feature types balance sen- sitivity and robustness in transition detection.
Parameters in Hierarchical Merging:
Merge numbers: Ensures minimum distance of 2 seconds between consecutive boundaries within the same level.
Temporal tolerance: In function M,, the temporal tolerance is set to seconds. higher-level boundary replaces the nearest lower-level one if their distance is < a, otherwise it is inserted.
Activation: The Hierarchical Merging (HM) process is enabled for long-video datasets (Ego4D-NLO. TACoS) and disabled for short-video datasets (Charades- STA, ActivityNet-Caption))) This based on the empirical analysis in Ap- pendix A.4.2
A.3.3 CANDIDATE PROPOSAL AND SCORING
VideoCLIP-baseed Pre-filtering:
Model: We use a VideoCLIP model (VideoCLIP-XL (Wang et al. 2024a) to compute the coarse similarity Selip between a candidate video segment and the text query.
Segment Merging: Adjacent segments are merged if their Selip scores differ by less than 3 = 0.1. This threshold was empirically set and applied consistently across all datasets. detailed analysis the merging behavior and its impact on different datasets is provided in the experimental section 4.4
Top-K Selection: The top ko segments from each hierarchy level (event, scene, action) are selected for VLM evaluation, resulting in a maximum of segments per video.
VLM-based Scoring:
Model: We utilize Qwen2.5-VI. our primary frozen VLM for its strong video- language alignment capability.
Scoring: The model computes a semantic similarity score Sylm E [0. 1] for each can- didate segment. The final score for candidate is computed $fnal X Sylm + (1 A) Setip. with À = 0.99. We set À = 0.99 to ensure that the final ranking is dominated by the VLM's semantic score, while still leveraging CLIP lightweight tie-breaker.
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Table 6: Dataset statistics and characteristics. Key attributes include the number of videos, number of language queries, average video length, average moment length, annotation source, and domain.
Benchmark
# VVieeos
#Queries
Video Len. (s)
Moment Len. (s)
Domain
Charades-S STA
1.3k
3.7k
29
7.8
Activity
ANet-Captions
4.9k
17.1k
118
40.2
Activity
TACoS
4.0k
368
31.9
Cooking
Ego4D-NLQ
415
4.6k
472
10.7
Ego-centric
In practice, the VLM often assigns identical or nearly identical scores to multiple can- didate segments, making it difficult to establish clear ranking. Incorporating a small weight from CLIP provides additional discriminative power in such cases, while the near-unit weight on the VLM score prevents CLIP from interfering with the primary semantic alignment.
A.3.4 CANDIDATE REFINEMENT
Merging Criteria:
Temporally proximate or overlapping segments are iteratively merged if they satisfy both conditions: (1) their boundaries are within second of each other, and (2) their unified confidence scores 8tnal differ by less than 0.14.
Boundary Update: New boundaries for merged segments are computed as the score- weighted average of the original boundaries.
A.3.5 DATASET ANALYSIS
We conduct experiments on four widely used benchmarks: Charades-STA, ActivityNet--aptions, TACoS, and Ego4DD-NL.. As summarized in Table these datasets vary significantly in scale, video duration, and domain coverage, thereby providing a comprehensive evaluation setting. Charades- STA and ActivityNet-Captions consist of thort-to-medium length activity videos paired with natural language queries, while TACoS focuses on long cooking videos with fine-grained temporal annota- tions. Ego4D-NLQ. in contrast, represents an egocentric perspective with long untrimmed videos and query annotations grounded in first-person interactions. Importantly, all four datasets are an- notated by human annotators, ensuring high-quality language descriptions and reliable temporal grounding labels. This diversity in domain, video duration, and query density makes the set of benchmarks complementary and well-suited for evaluating the scalability and generalization ability of temporal grounding methods.
Figure further illustrates the distribution of normalized moment lengths across the four datasets. Charades- exhibits relatively short moments, mostly concentrated within 0.1-0.3 of the video duration, reflecting its focus on fine mme-grained activity descriptions. Activity.Ne--aatian broader spread, but still biases toward shorter segments, which is consistent with the open-domain nature of its videos and captions. TACoS, in contrast, shows a significant proportion of long mo- ments, often covering more than half of the video duration, highlighting the dense and procedural structure of cooking videos. Ego4D-NLQ presents more balanced distribution, with moments spanning wide range of relative lengths, reflecting the egocentric setting where queries may corre- spond to both brief interactions and extended activities.
displays a
These distinct distributions underscore the heterogeneity of temporal grounding benchmarks. Datasets with predominantly short moments (e.g., Charades-STA) emphasize precise boundary de- tection, while those with longer or more varied segments (e.g., TACoS, Ego4D-NLQ) challenge methods to handle diverse temporal scales. This diversity motivates the need for our hierarchical decomposition and adaptive scoring framework, which is explicitly designed to cope with the vari- ability in moment granularity across benchmarks.
Dataset-Specific Configurations. Most hyperparameters remain fixed across datasets. The dataset-specific adjustments are the frame sampling rate and the activation of the Hierarchical Merg- ing module, as detailed above. This demonstrates the robustness and generalizability our frame- work.
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ActivitVNe- CappitAet-Captioos Captions
Pereeetoge 1963
15.0
23
Purcellage
7.5
5.5
6122
644.
6449
41.4
091.0
6102
02ed
644d
634.5
Relative Moment Longth
Relative Moment Lenoth
EqoDD-NLQ
73
Percentage
Pescantage
984.1
6192
6445
6668
091.
8591
e102
D3ed
0.
8848
0$14
Relative Moment Langth
Relative Mossent Lengza
Figure 7: Distribution of normalized moment lengths across four benchmarks. The x-axis denotes the ratio of the annotated moment length to the corresponding video duration, while the y-axis shows the percentage of queries in each bin.
Table 7: Ablation Study on Visual-Language Backbones.
TACoS
Charades-STT
Model
Size
0.1
0.3
0.5
mloU
0.3
0.5
0.7
mloU
Videoclip-XX XL
0.6B
22.19
12.95
7.38
9.50
55.03
38.23
20.27
37.17
+Intern Video2.5
7B
40.94
24.50
13.24
17.03
69.11
48.39
25.24
45.94
+Qwen2.5
3B
41.85
22.66
11.61
16.36
62.15
42.39
22.20
41.50
+Qwen2.5-VL
7B
44.94
29.08
16.10
19.79
69.62
48.52
25.59
46.29
A.4 SUPPLEMENTARY EXPERIMENTS.
A.4.1 IMPACT OF VISUAL-LANGUAGGE MODEL SELECTION
The results in Table 7 reveal several important insights regarding the role of different VLMMs within the HiTeA framework. When using only Videoclip-XL. without a large-scale VLM, performance on short-video datasets remains reasonable. but results long videos are significantly lower. This suggests that effective long-video grounding requires the strong generalization capabilities provided by large VLMs. Across models, performance generally scales with model capacity: the number of parameters increases from 3B to 7B in Qwen2.5-VL metrics on both TACoS and Charades- STA improve consistently. This trend indicates that larger VLMs offer stronger -isual-language alignment, enabling more accurate evaluation of the semantic relevance between video segments and text queries.
Equally notable is that VLMs of similar scale demonstrate comparable performance. For exam- ple, Qwen2.5-VL. and Intern Video2.,, both with 7B parameters, achieve nearly identical results on Charades-STA. This suggests that, provided the model has sufficiently rich general representations, HiTeA can leverage different backbone VLMs without any task-specific adjustment or fine-tuning. Overall, these observations highlight the framework's robustness and generality, confirming that the combination of hierarchical temporal decomposition and large pre-trained VLMs can consistently support effective long-video temporal grounding in fully training-free setting.
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Table 8: Impact of Hierarchical Merging.
Charades-ST -TAA
ANet-Captions
Method
0.3
0.5
0.7
mloU
0.3
0.5
0.7
mloU
HM
69.65
44.30
22.66
45.09
53.65
30.55
16.24
37.30
w/o HM
69.62
48.52
25.59
46.29
54.46
31.1
16.57
37.93
A.4.2 IMPACT OF HIERARCHICAL MERGING.
The results in Table 8 reveal nuanced but consistent pattern: the application of our Hicrarchical Merging (HM) module leads a slight performance degradation on short-video datasets (Charades- STA and ANet-Captions), while its benefits are pronounced and essential for long videos (as es- tablished in our main results). We argue this not shortcoming but a critical, intentional design feature that demonstrates our framework's adaptive capability.
The core rationale lies in the fundamental structural difference between short and long videos. Short videos (e.g., Charades-STA) typically contain fewer semantic segments and less complex hierar- chical temporal structure. this setting, the candidate boundaries extracted from the three comple- mentary feature spaces (event, scene, action) already sparse and highly informative. Enforcing hicrarchical merging here can be overly restrictive; it risks diluting the valuable diversity of these independent signals by forcing them into single hierarchy. The marginally superior performance without HM (w/o HM) suggests that for short videos, preserving this diversity of boundary hy- potheses more beneficial than imposing rigid top-down structure. The different feature spaces effectively provide an ensemble of perspectives, leading to richer and more accurate localization.
Conversely, long videos exhibit a deeply nested temporal hierarchy with abundant redundant content. Without a mechanism to enforce consistency across different levels of granularity, the sheer number of potential boundaries from various features would lead temporal clutter, error accumulation, and a loss of global coherence. The HM module is indispensable here. It acts as a regularizer, pruning spurious detections and aligning fine- grained action transitions with broader scene and event con- texts. This creates clean, coherent, and multi-scale temporal scaffold that is crucial for efficiently and accurately navigating long durations.
Therefore, our decision to disable HM for short videos and enable it for long videos is principled one, grounded in the data's inherent characteristics. demonstrates that our framework not a one-size-fits- fil solution but intelligently adapts its strategy to the input. This adaptive design is a strength, contributing to robust and state-of-the-art performance across both short and long-form video benchmarks.
A.4.3 IMPACT OF FRATURE EXTRACTION MODEL SELECTION.
To investigate the impact of specific feature extractors and verify the generalizability of our frame- work, we conducted ablation studies by replacing the backbone models at the Action and Event levels.
Specifically, we replaced the RAFT optical flow model with FlowNet (Dosovitskiy al. 2015) for the Action level, replaced DINOv2 with iBOT (Zhou et al. [021] for the Scene level, and replaced the ViT-CLIP model with standard results Charades-STA are reported in Table
(Liu et al. 2021) for the Event level. The
As observed in Table g while the framework maintains competitive performance using alternative backbones, our default configuration yields the highest accuracy.
Using FlowNet results a performance drop 1.27% mloU, This validates our choice of RAFT, its dense, pixel-level flow estimation captures fine-grained motion boundaries more effectively than the coarser features from FlowNet.
Action Level:
Scene Level: Replacing DINOv2 with iBOT leads to 1.33% decrease in mloU. While iBOT utilizes Masked Image Modeling (MIM) to effectively capture local structural dctalls-crucis for detecting scene ransitions-DINNN achieves superior results. This justifies selection of DI-
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Table 9: Impact of Feature Extraction Model Selection.
Charades-STi STA
Model
Level
0.3
0.5
0.7
mloU
Flowoet(Dosovitski) al. iBhu el at. 2021)
2015)
Action
68.92
46.94
23,41
45.02
Scene
69.14
46.37
23.47
44.96
Wrin--rrantormerr Liu et al. 2021
Event
67.45
45.46
23.76
44.47
Ours
69.62
48.52
25.59
46.29
NOv2, as it inherits the structural awareness of iBOT while benefiting from larger-scale curated data and optimized training recipes, resulting more robust generalized features.
Event Level: Replacing VIT with Swin-Transfone Transformer leads to 1.82% drop in mloU. This confirms the necessity of Vision-Language alignment at the top level. ViT explicitly aligns visual concepts with language queries, allowing for more accurate detection of high-level semantic narrative shifts compared to purely visual features.
These empirical results substantiate that our core contribution lies in the hierarchical processing strategy itself EEvent-Scene-Action), rather than being tied to specific architectures. However, our default configuration yields the highest accuracy. This demonstrates that while the hierarchical structure is the primary driver of success, our selection of ViT. DINO, and RAFT- as es- tablished representatives with proven generalization capabilities-is essential for maximizing the model's overall potential, thereby empirically justifying our architectural decisions.
A.5 COMPUTATIONAL COMPLEXITY
HiTeA consists of two major stages: hierarchical candidate generation and VLM-based scoring.
1. Hierarchical Candidate Generation. Given video with N frames, we first extract multi-scale VideoClip features and apply the Hierarchical Temporal Decomposition (HTD) module to generate candidate segments at multiple temporal granularities. The number of frames N is typically very large for long videos. The computation involved this stage primarily includes feature extraction, lightweight aggregation, and candidate filtering, which scale approximately linearly with the video length:
OHTD =
(5)
where H = is the number of hierarchical levels. Since these operations do not involve heavy neural network inference, this stage is computationally efficient even for long videos.
VLM-Based Scoring. The computationally expensive component is the scoring of candidate segments using frozen vision- vision-language model (VLM). HiTeA applies a top-K filtering strategy at each hierarchical level, such that only the most promising candidates are evaluated by the VLM. Let K; denote the number of candidates retained at level i, then the total number of VLM evaluations is:
NVLM Σ K
The corresponding complexity is:
OVLM O Σ K; = O(H
where K & N ensures that VLM inference is tractable, for very long videos. This represents substantial reduction compared to a brute-force search over all possible start-end pairs, which would require (N22 VLM evaluations.
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A.6 SCALABILITY AND RUNTIME ANALYSIS
Clarification on Multi-Model Usage. Although HiTeA leverages five pre-trained components to ensure robust performance, most of them are used strictly in an offline preprocessing stage. The pipeline is structurally decoupled as follows:
Offline preprocessing (executed once per video): (I) a video encoder (e.g., ViT) for event-level features; (2) self-supervised backbone (e-g.. DINO) for complementary scene- level cues; (3) motion model (e.g.. RAFT) for optical-flow features. These backhones are only used for feature extraction and do not participate in online reasoning.
Online interaction (executed per query): (4) a lightweight retriever (e.g., VideoCLIP) for candidate selection; (5) a VLM (e.g.. Qwen2.5-VL) for high-level reasoning over a small set of candidates.
During user interaction, only components (4) and (5) are invoked. In typical deployments, video- level features are pre-computed and cached, so the heavy perceptual cost is amortized across queries and does not affect per-query latency. Moreover, the three offline backbones (VTT. DINO, RAFT) are fully parallelizable across GPUs.
Computational Complexity. We compare HiTeA against three representative training- freee base- lines: the dense-scanning TFVTG (Zheng et al., 2024). the compositional proposal method of Luo al. 2024), the BLIP-based zero-shot VMR (Wattasseril et al. (023), and the dense-captioning approach Moment-GPT (Xu et al., 2025). ) To rigorously evaluate scalability, we conduct experi- ments on QVHighlights and TACoS, which are characterized by distinct temporal distributions with
average video lengths of ~ 1550 and 3668,, respectively.
Baselines (O(L3) or OL)). TFVTG requires dense proposal enumeration over all tem- poral pairs, leading to O(L²) complexity in the video length L. Luo et al. (2024) operate on a fixed number of video snippets L, (32 in their implementation) and decompose each query into Lo simple queries; their online cost therefore scales as O Lę) and is effectively insensitive to raw video length, but the original paper does not report wall- clock latency. The BLIP-based zero-shot VMR approach (Wattassrrl et al. (023) performs sparse per-second BLIP/BLIP-2 scoring followed by watershed-style merging, resulting in (L) online cost in L with a lighter per-frame model than full MILMs, but again with- out reported runtime on long-vides benchmarks. Moment-GPP performs frame-by-frame MLLM captioning, which scales linearly with L (O(L)) but with substantial constant factor due to the heavy MLLM.
HiTeA (O(K) online). HiTeA first abstracts each video into small set of structural events via Hierarchical Temporal Decomposition (HTD), and then selects a fixed number of can- didates (Top-K) for VLM reasoning. The online reasoning complexity is thus reduced to (K), where K is the number of selected candidates. We set K-9 by default, making the online cost effectively insensitive to the raw video length L in regime of interest.
Practical Runtime Decomposition. To make the computational cost more transparent, we further decompose HiTeA's runtime into two stages.
Stage 1:
Offline feature extraction (once per video). The first three backbones in HiTeA- -VT, DINO, and RAFT-are are used solely for feature extraction. In practical deployments, video fea- tures (e.g., RGB embeddings, optical flow, CLIIP-tyle embeddings) are typically pre-computed and stored. These three models features for a single vidco takes about 6-7s, dominated by RAFT optical-flow computation. If we disable optical flow and only keep event/scene features, this offline stage reduces to 1-2s, confirming that RAFT is the main contributor to offline cost.
be executed fully in parallel. On TACOS videos, extracting all
Stage 2: Online per-query reasoning (user-perceiver latency). This stage determines the latency observed by the end user. It involves two models (VideoCLIP and the VLM) and two lightweight algorithms (HTD and CR):
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Algorithmic overhead is negligible. HTD operates on cached features and runs in approxi- mately 0.0012s per video on average; CR requires only 0.0001s per query in our measure- ments. Their contribution to total latency is negligible compared to VLM inference.
Cascade-filter design reduces VLM calls. A straightforward zero-shot design would apply a VLM to all (or densely sampled) segments, leading to hundreds of VLM inferences on long videos. HiTeA instead adopts two-stage cascade: (i) very fast VideoCLIP module scores all HTD candidates (e.g.. ~38 on TACOS and 1~14 on Ego4D), and (ii) only the Top-K candidates (K=9) are passed the heavy VLM. In our implementation, a single Qwen2.5-VL. forward pass takes about Is with an optimized runtime, so the overall VLM cost is tightly controlled.
Table 10: Runtime efficiency breakdown. Offline cost is dominated by RAFT (optical flow) but is amortized over queries. Online latency remains approximately constant because the heavy VLM is restricted to Top-K candidates, avoiding the length-dependen scaling observed in dense captioning approaches. Most training-free free works do not report time anaylsis, so we primarily list their online complexity. For MPme we report the QVHighlights latency from (Xu et al. 2025) and
provide a coarse extrapolation on TACOS based on average video duration.
Inference Latency (s/ //querr))
Method
Mechanism
Online Complexity
Short (QVH)
Long (TACOS)
BLIP-VMR Watttsseril al.
2023
Sparse scoring merging
O(L) BLIP
12.7
25 (est.)
TFVIG Zheng
2024
Dense scanning
O(12)
Lan et 2024
Cartesian product
O(T,, T.))
Moment GPT Xu al.
2025
Dense frame captioning
((L) MLLM
16.1
32 (est.)
HITeA (Ours)
Cascade reasoning
K=K
See below
below
HiTeA brewkdoww:: offiiee va. online
1. Offtine cost
DINO RAFT
O(L) O
3.0-4.0
6.0-7.0"
2. Online latency
VideoCLIP filter VLM
7.0-8.0
7.0-8.0
Dominant cost is (~ Without flow, offiine cost drops 1122s HTD overhead is negligible (~00.00125).
For Luo al. 2024,, Le denotes the fixed vumber of video snippets (32 their implementation) and Lę is the number of simple queries. The inference latencies on TACOS for RI.IP.VMR and MomentGGP are estimated hased on avenge video length.
On TACOS, the average per-query scoring time of HiTeA is 7-8s. Empirically, most of the time is spent on the Qwen2.5-VL inferences, while VideoCLIP and the HTD/CR computations contribute only tiny fraction. In other words, HiTeA introduces just enough structure (HTD cascade filter- ing) to eliminate the hundreds of redundant VLM calls that a naive "one- MLLM-over-lll-segment design would incur long videos.
Summary. Table T0 and the above breakdown show that while offline preprocessing naturally scales with video length (e.g., 3-4s + 6-7s from QVHighlights to TACOS), HiTeA's online la- tency remains stable at approximately 7.5s per query. This confirms that our sparse structural rea- soning design effectively shields the user experience from the computational heaviness the five underlying models, and maintains practical afficiency-accuree trade-off even on long videos.
A.7 LIMITATIONS AND FUTURE DIRECTIONS.
The primary limitation of HiTeA stems from its core design: the hierarchical decomposition deliv- ers maximal benefit for long, untrimmed videos with complex temporal structures. On very short clips, the advantages of a strict multi-level hierarchy are naturally diminished. Our ablation studies, however, reveal that comparable performance can be achieved with simplified one- or two-level partitioning. indicating that a fixed three-layer hierarchy is not always necessary.
Furthermore, the decomposition need not be strict in practice. The optimal granularity may vary depending factors such as video domain, length, and even the specific query, leading to organic cross-level overlaps and merges. This inherent complexity is precisely what our Candidate Refine- ment module is designed to handle. The framework's 's adaptability points toward promising future directions. For instance, rather than relying on a fixed hierarchy, a dynamic strategy could be devel- oped to adaptively determine the optimal decomposition depth based video properties. More fun-
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Published as a conference paper at ICLR 2026
damentally, alternative strategies for temporal segmentation diself-beyon the action-scene- paradigm we adopted are worth exploring. The principle of multi-scale temporal analysis could also be generalized to other video understanding tasks, such as dense video captioning summa- rization. This work serves as an initial step in this direction, establishing a foundation for future research into more flexible and general temporal reasoning models.
A.8 THE USE LLM.
We clarify that large language model (LLM) was used solely as a writing assistant in the prepa- ration of this paper. Specifically, the I.LM was employed to polish the text, improve grammar, and refine the clarity and flow of exposition. It was not involved in the design of methods, implemen- tation of algorithms, execution of experiments, or analysis results. All technical contributions, experiments, and conclusions presented in this work entirely the authors' own.
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Reviews:
[{"review_id": "hUbmwmrAID", "reviewer": "Reviewer_KVaa", "summary": "This paper proposes HiTeA, a training-free framework for Video Temporal Grounding (VTG) in long videos. The authors argue that existing methods either rely on expensive supervised annotations or perform poorly when handling the complex temporal structures and content redundancy in long videos. The core contribution of HiTeA is a Hierarchical Temporal Decomposition (HTD) mechanism. This mechanism simulates human search strategies, decomposing the video into three granularities: 'events', 'scenes', and 'actions'. It first uses various pre-trained models (like ViT, DINO, RAFT) to extract multi-level features and generate multi-scale candidate segments. Subsequently, the framework adopts a two-stage strategy: first, it pre-filters candidates using a lightweight VideoCLIP, and then it scores the candidates using a frozen large vision-language model (VLM). Experiments show that HiTeA significantly outperforms existing zero-shot (ZS) methods on multiple short- and long-video benchmarks. For example, on the TACOS dataset, it achieves a 12.4% absolute performance gain. More importantly, on long-video benchmarks like Ego4D-NLQ, the method achieves performance competitive with or even superior to state-of-the-art (SOTA) supervised methods.", "questions": "My main concerns are articulated in the \"Weaknesses\" section and are more about presentation and rigor. Here they are converted into specific questions for the authors' rebuttal:\n\n-   **Regarding $\\lambda=0.99$:** Given the optimal value of $\\lambda$ is 0.99, this suggests the contribution of $s_{clip}$ is minimal. Does this imply that the \"fusion\" motivation proposed in Sec 3.5 is not practically realized, and the true role of $s_{clip}$ (as mentioned in the appendix) is merely for filtering and tie-breaking? Please clarify the exact role of $s_{clip}$ in the final score.\n-  **Regarding the disabling of HM:** You mention in Appendix A.4.2 that the HM module is disabled for short videos because preserving the diversity of independent features is more effective than enforcing a hierarchy. Does this mean the best practice for HiTeA involves using **two different configurations based on video length (long/short)**? Could you more explicitly state this (very reasonable) design choice in the main text?\n-  **Regarding the complexity of CR and HTD:** Your framework appears to have two \"merging\" steps: (1) the hierarchical merging in HTD (Alg 1) and (2) the progressive merging in CR (Alg 2). This makes the method (especially the CR module) seem somewhat complex. Table 3 shows that CR is crucial, but could you provide further intuition on why the merging in HTD is insufficient, necessitating a second round of merging in CR?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "-  **Significance and Impact:** The paper's strongest merit lies in its **groundbreaking results**. As a **training-free** framework, HiTeA's mIoU (8.12%) on Ego4D-NLQ (a highly challenging long-video benchmark) **surpasses** all listed fully-supervised SOTA methods (e.g., UniVTG's 4.91%). This challenges the traditional notion that SOTA performance must rely on dense supervision and holds significant implications for the VTG field.\n- **Originality and Intuitive Method:** The core **HTD (Hierarchical Temporal Decomposition)** idea is highly novel. It does not blindly segment the video; instead, it emulates the human search intuition of progressing from 'event' to 'scene' to 'action' and elegantly maps this intuition to different pre-trained features (ViT for semantics, DINO for structure, RAFT for motion). This is a very elegant design.\n- **High-Quality and Rigorous Experiments:**\n    * **Main experiments** convincingly demonstrate its SOTA performance on both long and short videos.\n    * The **ablation study (Table 3)** is solid. It clearly quantifies the contribution of each layer in HTD (Event, Scene, Action) and the necessity of the CR module, proving the effectiveness of each component in the framework.\n    * The **efficiency analysis (Table 4)** adds significant practical value, showing that VideoCLIP pre-filtering reduces the computational load on Ego4D by 93.9%, addressing the pain point of slow VLM inference on long videos.\n\n-  **Clarity:** As mentioned, the paper is well-written and logically organized. **Figures 1 and 2**, in particular, intuitively illustrate the difference between HiTeA and traditional methods, as well as its complex internal workflow.", "weaknesses": "My criticisms are  primarily focus on the rigor of its claims and the transparency of its details.\n\n-   **Contradiction between the motivation for Score Fusion (Sec 3.5) and the results (Sec 4.4):**\n    * **Issue:** In Sec 3.5, the authors introduce $s_{final}=\\lambda \\cdot s_{vlm}+(1-\\lambda) \\cdot s_{clip}$, motivated by the idea of fusing the VLM's semantic information with VideoCLIP's structural information.\n    * **Contradiction:** However, in the parameter analysis in Sec 4.4 (Fig 4, Right), the optimal value for $\\lambda$ is set to **0.99**. This implies that the contribution of $s_{clip}$ is almost negligible (1%).\n    * **Suggestion:** The authors also admit in the appendix that $s_{clip}$ mainly serves as a \"tie-breaker\". It is suggested that the authors state this more candidly in the main text (Sec 3.5)—i.e., that the VLM is absolutely dominant and CLIP is only used for filtering and tie-breaking—rather than presenting it as a \"balanced fusion.\" This would make the paper's motivation more rigorous.\n\n-   **A key implementation detail is hidden in the appendix:**\n    * **Issue:** Appendix A.4.2 (Table 7) reveals that for short videos (Charades-STA, ANet), the **Hierarchical Merging (HM) module is disabled** because it slightly harms performance.\n    * **Suggestion:** This is a crucial implementation detail that reveals the framework's adaptability (prioritizing structure for long videos, diversity for short videos). This should not be hidden in the appendix. It is suggested that the authors move this detail (i.e., that HM is disabled for short videos) to the main paper's experimental setup (Sec 4.1) or method (Sec 3.3) for discussion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "i0BjuZUaFn", "reviewer": "Reviewer_wbqj", "summary": "This paper introduces HiTeA, a training-free framework for long-video temporal grounding. The method tackles the challenge of locating temporal segments in long, untrimmed videos corresponding to natural language queries—without requiring any supervised training.", "questions": "Please refer to the points listed under *Weaknesses*.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The Hierarchical Temporal Decomposition (HTD) effectively aligns video segments with textual queries at multiple granularities, bridging the gap between semantic understanding and temporal localization.", "weaknesses": "1. **Feature extraction choices** – Why were **ViT**, **DINO**, and **FLOW** specifically chosen for extracting event-level, scene-level, and action-level features, respectively? How do these choices contribute to the complementary strengths of each temporal layer?\n2. **Inference efficiency** – The paper discusses computational complexity analytically, but could you provide a **runtime comparison** with representative baselines on long-video datasets to empirically demonstrate HiTeA’s scalability?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vTnXlbdFs8", "reviewer": "Reviewer_g3Bh", "summary": "This paper presents HiTeA, a training-free framework for temporal grounding in long, untrimmed videos. The key innovation is a Hierarchical Temporal Decomposition (HTD) module that structures videos into multi-scale temporal units (events, scenes, actions) using off-the-shelf feature extractors. Candidate segments are scored using frozen vision-language models (VLMs), and a Candidate Refinement module produces final predictions. The method achieves strong results on both long-video benchmarks (Ego4D-NLQ, TACoS) and short-video datasets (Charades-STA, ActivityNet-Captions).", "questions": "Please refer to the Weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) **Well-motivated problem.** The paper targets a real gap in current approaches—their difficulty scaling to long, temporally complex videos without costly supervised training—and clearly motivates a training-free alternative.\n\n2) **Strong empirical performance.** HiTeA delivers compelling results, especially on long videos: 44.94% R@0.1 on TACoS (a +12.4% absolute gain).\n\n3) **Robust generalization.** The method transfers across diverse domains and durations—from short clips (≈29 s, Charades-STA) to extended egocentric footage (≈472 s, Ego4D-NLQ)—indicating strong robustness to video type and length.", "weaknesses": "1) **Limited technical novelty.**\n   While the system is effective, its building blocks are largely established:\n   - Hierarchical temporal decomposition has prior art.\n   - Zero-shot use of pretrained VLMs is well explored.\n   - Change-point detection (e.g., PELT) is off-the-shelf.\n   - The overall pipeline follows a familiar proposal-generation + scoring pattern.\n\n2) **Method complexity and engineering burden.**\n   The framework stitches together multiple components (three feature extractors, VideoCLIP filtering, VLM scoring, candidate refinement) and exposes many hyperparameters (α, β, λ, k, thresholds). This raises concerns about:\n   - **Generalizability** beyond the tested datasets,\n   - **Sensitivity** to hyperparameters (only β and λ receive analysis), and\n   - Whether the gains stem from **principled design** versus careful tuning and component stacking.\n\n3) **Unclear rationale for hierarchy effects across lengths.**\n   Table 7 shows hierarchical merging helps long videos but harms short ones; the discussion in A.4.2 reads as a post-hoc explanation rather than a principled, testable design hypothesis.\n\n4) **Domain specificity of the three-level hierarchy.**\n   The event–scene–action granularity—especially the scene level—appears tailored to the evaluated domains. It’s unclear how this hierarchy transfers to other video genres (e.g., sports broadcasts, surveillance, instructional videos) without re-defining levels or retuning cues.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BSUUtFZBWN", "reviewer": "Reviewer_RwT5", "summary": "This paper introduces HiTeA, a training-free framework for temporal grounding in untrimmed videos. The core of HiTeA is its Hierarchical Temporal Decomposition (HTD) mechanism, which structures a video into multi-scale temporal units (events, scenes, actions). It leverages pre-trained vision-language models to match text queries with candidate segments without any task-specific training and employs a refinement module to generate final predictions. The authors report extensive experiments on Charades-STA, ActivityNet-Captions, Ego4D-NLQ, and TACoS datasets.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The manuscript is well-written and clearly structured. \n\n2. The supporting figures, particularly the conceptual diagrams, are well-designed and effectively aid in understanding the proposed framework.", "weaknesses": "1. The proposed hierarchical relationship among events, scenes, and actions appears to be somewhat problematical and lacks a strong empirical or theoretical justification. For instance, in real-world videos, the relationship can be more complex and non-hierarchical; a single scene might encompass multiple distinct events. A more detailed rationale or citation for this specific structural choice would strengthen the methodological foundation.\n\n2. The experimental evaluation would benefit from a direct comparison with other recent, state-of-the-art training-free methods for temporal grounding or related video-language tasks. [a] Universal Video Temporal Grounding with Generative Multi-modal Large Language Models, arXiv 2025.  [b] Zero-Shot Video Grounding With Pseudo Query Lookup and Verification, TIP 2021.\n\n3. In the ablation study of Table 3, the performance improvements from the individual components of the Hierarchical Temporal Decomposition (HTD) on the TACoS dataset seem relatively marginal, and in some configurations, a component even appears to degrade performance. The manuscript should provide an in-depth analysis discussing the potential reasons for this observed instability or limited impact. Furthermore, the ablation study appears incomplete as it does not present the results of using the Candidate Refinement module in isolation (e.g., on uniformly sampled segments). Showing this baseline is critical to disentangle and quantify its specific contribution to the overall performance.\n\n4. The generalization and robustness of the method could be more convincingly demonstrated by including evaluations on the QVHighlights dataset. A key challenge of this dataset is that a single query can correspond to multiple moments. Testing on QVHighlights would provide valuable insights into the model's capability to handle such complex, multi-instance grounding scenarios.\n\n5. The justification for the specific choice of pre-trained models for feature extraction (ViT-B/32 for events, DINO-v2 for scenes, RAFT-Large for actions) is insufficient. The authors should clarify the specific properties or prior evidence that make these particular models representative of their respective hierarchical levels. A discussion on whether alternative models were considered and why these were chosen would strengthen the methodological design.\n\n6. The inference cost of utilizing five different pre-trained models is a significant practical concern, especially for processing long videos. The manuscript should include a discussion, and ideally a quantitative analysis, of the computational efficiency and inference speed. This is a critical factor for real-world applicability and should be transparently reported.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)