Temporally-Constrained Video Reasoning Segmentation

• Temporally-Constrained Video Reasoning Segmentation is defined as the task where natural language queries with temporal qualifiers guide both the relevant time interval selection and pixel-level mask generation.
• The approach uses sequential decision-making, reinforcement learning, and temporal token hierarchies to optimize keyframe selection and segmentation accuracy in dynamic video scenes.
• Integrating temporal grounding with spatial prediction, this methodology improves performance on benchmarks by effectively linking temporal events with refined visual segmentation.

Searching arXiv for the primary paper and closely related temporally constrained video segmentation work. Temporally-Constrained Video Reasoning Segmentation is a formulation of video segmentation in which the target object is specified by a natural-language query whose validity depends on temporal context, so the system must infer both when the object is relevant and where it is at the pixel level. In the terminology of "VideoSEG-O3" (Dai et al., 5 Jun 2026), this falls under Reasoning Video Object Segmentation (RVOS): given a video $V$ and a textual query $Q$, predict a temporally localized segment, such as an interval or keyframe, together with a mask sequence for the referred object. The defining feature is that temporal constraints are part of the reasoning process itself rather than a fixed timestamp, requiring the model to decide which temporal portions to inspect, whether further evidence is needed, and when to terminate with a segmentation. Related work frames the same problem under adjacent names such as Video Reasoning Segmentation (VRS), where implicit instructions encode human intent and temporal logic (Wen et al., 8 May 2026), and temporally-constrained video RS, where a temporal constraint function determines whether a query is active at each frame (Shen et al., 22 Jul 2025).

1. Conceptual scope and task formulation

Temporally-Constrained Video Reasoning Segmentation extends conventional video segmentation beyond closed-set category prediction and beyond static referring expressions. The target may be specified through temporal qualifiers such as “when it starts running”, “after he stands up”, or “the car at the front at the beginning”, and the model must both localize the relevant time interval and produce accurate masks in that interval (Dai et al., 5 Jun 2026). In the surgical-domain formulation of temporally-constrained video reasoning segmentation, the query may only be active during a procedural phase, formalized by a temporal constraint function $\tau_Q : \mathbb{N} \rightarrow \{0,1\}$, so that the constrained output becomes $$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$ (Shen et al., 22 Jul 2025).

This setting differs from standard VOS, VIS, and RVOS in the role assigned to time. In standard VOS or VIS, the principal difficulty is maintaining track and mask quality over time. In referring video segmentation, language identifies an object but often assumes the referent remains valid throughout the clip. In temporally-constrained reasoning settings, by contrast, the language itself can determine the relevant temporal slice, so the model must reason over event order, moment salience, or phase-specific relevance before segmentation becomes well-defined (Kao et al., 24 May 2025). This suggests that temporal grounding and segmentation are not separable implementation details but jointly constitutive components of the task.

A recurring formal pattern is the decomposition of the problem into temporal inference followed by spatial prediction. "RCoT-Seg" (Wen et al., 8 May 2026) factorizes VRS into Temporal Video Reasoning (TVR) and Keyframe Target Perception (KTP), while "MomentSeg" (Dai et al., 10 Oct 2025) jointly optimizes Temporal Sentence Grounding (TSG) and RefVOS. "VideoSEG-O3" (Dai et al., 5 Jun 2026) instead casts the entire process as a Markov Decision Process for sequential spatiotemporal exploration, in which the agent explicitly chooses intervals and keyframes via textual actions.

2. Sequential reasoning over time as a decision process

A central development in the topic is the explicit treatment of temporal exploration as a policy rather than as fixed frame sampling. "VideoSEG-O3" formulates RVOS as an MDP with state

$s_t = (\mathcal{V}_t, Q, \mathcal{H}_{t-1}),$

where $Q$ is the query, $\mathcal{H}_{t-1}$ is the interaction history, and $\mathcal{V}_t$ is the current visual observation (Dai et al., 5 Jun 2026). At $t=0$, $\mathcal{V}_t$ contains globally sampled frames, while later states incorporate a selected temporal interval and a high-resolution keyframe from the previous step. The action space contains two action types emitted as text tokens: exploration via a special <select> token plus a time range and keyframe index, and termination via <answer> followed by [SEG] (Dai et al., 5 Jun 2026).

This formulation makes temporal constraints explicit. The system is not forced to process the video uniformly. Instead, it decides which interval and which keyframe to inspect next, receives updated observations through the transition function, and is rewarded according to temporal localization and segmentation quality (Dai et al., 5 Jun 2026). The same paper reports that after reinforcement learning, average rounds drop from 2.79 to 2.23 while accuracy improves, indicating more efficient temporal decisions (Dai et al., 5 Jun 2026). On easy benchmarks such as Ref-DAVIS the model tends to terminate early, whereas on longer or more complex datasets such as Ref-SAV and Long-RVOS it performs more turns (Dai et al., 5 Jun 2026).

Agentic keyframe refinement appears in related form in "RCoT-Seg" (Wen et al., 8 May 2026). There, an initial keyframe is proposed by a keyframe generation stage and then verified by Agentic Keyframe Selection (AKS), which reasons whether the target is fully visible and either accepts the frame or triggers reselection. The process iterates up to $Q$0 times and improves the ratio of target area in the selected keyframe relative to the maximum across the video, for example from 61.0% to 64.1% on ReVOS and from 43.0% to 65.9% on DAVIS (Wen et al., 8 May 2026). A plausible implication is that temporally-constrained reasoning benefits from explicit self-evaluation loops whenever target visibility and target identity do not coincide at the first salient moment.

Training-free approaches also operationalize temporal reasoning through discrete selection. "ThinkVideo" uses a multi-agent pipeline in which an MLLM analyzes ordered keyframe candidates with zero-shot CoT, outputs instance-specific keyframe indices and descriptions, then passes these to Seg-Zero and SAM2 (Kao et al., 24 May 2025). On T-ReasonVOS, which isolates temporally sensitive queries, ThinkVideo-GPT-4o reaches $Q$1 versus 37.4 for VideoLISA (Kao et al., 24 May 2025). This suggests that even without end-to-end training, frame-wise temporal reasoning can substantially improve segmentation when temporal logic is the main source of ambiguity.

3. Chain-of-thought, temporal tokens, and hierarchical temporal representations

A major design axis in the literature concerns how temporal reasoning is represented. "VideoSEG-O3" introduces a Decoupled Thinking Trace with three stages: Temporal Understanding, Spatial Detail Capturing, and Expression Parsing (Dai et al., 5 Jun 2026). Temporal understanding operates on many low-resolution frames to infer when the described event likely happens; spatial detail capturing refines the instance using high-resolution keyframes and mid-resolution frames; expression parsing interprets causal, sequential, comparative, or motion-based descriptions (Dai et al., 5 Jun 2026). These stages are executed as a multi-turn CoT in which each turn emits a reasoning block followed by either <select> or <answer> (Dai et al., 5 Jun 2026).

"RCoT-Seg" similarly treats CoT as an operational intermediate state rather than as auxiliary explanation. Video Description Generation produces a temporally aware description of the clip, Keyframe Generation proposes a timestamp, AKS verifies it, and Keyframe Target Grounding outputs boxes and points for SAM2 (Wen et al., 8 May 2026). The model is first initialized on a curated CoT-SFT corpus and then refined with GRPO under task-aligned rewards (Wen et al., 8 May 2026). Explicit CoT is shown to improve J&F by +2.2 on DAVIS, +2.7 on Ref-YouTube-VOS, +3.1 on ReasonVOS, and +2.9 on ReVOS relative to an answer-only baseline (Wen et al., 8 May 2026).

Another representational strategy is token hierarchization. "The Devil is in Temporal Token" proposes VRS-HQ, which replaces the single-token design of earlier methods with frame-level <SEG> tokens and a temporal-level <TAK> token (Gong et al., 15 Jan 2025). <SEG> captures local frame-specific target semantics, while <TAK> captures global temporal semantics over the video (Gong et al., 15 Jan 2025). Temporal Dynamic Aggregation updates the temporal token by a similarity-weighted sum of frame-level tokens,

$Q$2

with best performance at $Q$3 (Gong et al., 15 Jan 2025). Token-driven Keyframe Selection then combines token similarity and SAM2 occlusion score, and the best score combination uses only token similarity plus occlusion (Gong et al., 15 Jan 2025). On ReVOS, VRS-HQ improves VISA-13B by +5.9, +12.5, and +9.1 in $Q$4 on the referring, reasoning, and overall subsets (Gong et al., 15 Jan 2025).

"MomentSeg" adopts a related but distinct temporal token mechanism through a dedicated [FIND] token for TSG (Dai et al., 10 Oct 2025). Instead of explicit timestamp generation, [FIND] is matched against pooled frame-level temporal features via cosine similarity

$Q$5

with $Q$6, followed by a weighted sigmoid cross-entropy loss (Dai et al., 10 Oct 2025). This converts temporal grounding into metric learning between a language-conditioned temporal token and video frame features. The same temporal signal drives Moment-Centric Sampling, which densely samples informative moments and sparsely samples the remainder of the video (Dai et al., 10 Oct 2025). On Charades-STA, MomentSeg-3B reaches [email protected] = 58.2 and mIoU = 50.2, outperforming larger video-LLM baselines listed in the paper (Dai et al., 10 Oct 2025). A plausible implication is that temporal tokens are most effective when they are used not only to localize moments but also to constrain downstream segmentation compute.

4. Reinforcement learning, rewards, and segmentation-aware optimization

The most explicit reinforcement learning formulation in the literature is "VideoSEG-O3" (Dai et al., 5 Jun 2026). The paper argues that standard GRPO optimizes token log-probabilities and treats [SEG] as just another text token, which prevents mask quality from affecting the policy gradient (Dai et al., 5 Jun 2026). To address this, it introduces SEG-aware Logit Calibration, defining a calibrated joint policy for token $Q$7 and mask $Q$8: $Q$9 and correspondingly augments the GRPO log-ratio with a segmentation contribution (Dai et al., 5 Jun 2026). Ablation shows that plain GRPO degrades performance relative to CoT cold-start, whereas SEG-aware calibration improves MeViS val_u from 59.43 to 60.51 and Ref-SAV from 50.55 to 51.27 (Dai et al., 5 Jun 2026). The paper further adds a hybrid objective

$\tau_Q : \mathbb{N} \rightarrow \{0,1\}$0

with $\tau_Q : \mathbb{N} \rightarrow \{0,1\}$1 chosen around 0.2 by ablation (Dai et al., 5 Jun 2026).

The reward design in VideoSEG-O3 is explicitly composite: $\tau_Q : \mathbb{N} \rightarrow \{0,1\}$2 $\tau_Q : \mathbb{N} \rightarrow \{0,1\}$3 enforces reasoning format; $\tau_Q : \mathbb{N} \rightarrow \{0,1\}$4 rewards keyframe alignment and temporal precision; $\tau_Q : \mathbb{N} \rightarrow \{0,1\}$5 rewards spatial IoU and keyframe superiority; $\tau_Q : \mathbb{N} \rightarrow \{0,1\}$6 rewards monotonic improvement across turns (Dai et al., 5 Jun 2026). Removing temporal reward causes the largest drop, including –2.49 on MeViS, and removing progressive reward increases average rounds and harms performance (Dai et al., 5 Jun 2026). Step-wise rewards are reported to induce degenerate early-termination policies, whereas episodic rewards perform better (Dai et al., 5 Jun 2026).

"RCoT-Seg" uses GRPO differently but with a comparable alignment objective (Wen et al., 8 May 2026). For AKS, the answer accuracy reward is

$\tau_Q : \mathbb{N} \rightarrow \{0,1\}$7

combined with a format reward in

$\tau_Q : \mathbb{N} \rightarrow \{0,1\}$8

with $\tau_Q : \mathbb{N} \rightarrow \{0,1\}$9 and $$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$0 (Wen et al., 8 May 2026). The GRPO objective includes clipped policy improvement and a KL penalty to the CoT-SFT reference model (Wen et al., 8 May 2026). For KTG, the reward uses Hungarian matching over predicted and ground-truth box-point sets, with terms for IoU, box L1, and point L1 correctness (Wen et al., 8 May 2026). This indicates a broader trend: RL is being used not merely to optimize free-form reasoning fluency but to align temporally grounded choices with downstream segmentation utility.

5. Temporal consistency beyond reasoning: memory, propagation, and evaluation

Although temporally-constrained reasoning segmentation emphasizes semantic-temporal inference, it remains dependent on stable temporal propagation and consistency mechanisms. Several strands of work contribute these foundations.

"ThinkVideo", "VRS-HQ", "MomentSeg", and "RCoT-Seg" all rely on SAM2-based propagation after keyframe or anchor-frame segmentation (Kao et al., 24 May 2025, Gong et al., 15 Jan 2025, Dai et al., 10 Oct 2025, Wen et al., 8 May 2026). In "MomentSeg", Bidirectional Anchor-updated Propagation begins from the moment center $$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$1 and propagates both forward and backward, with memory cleaning triggered when cumulative tracking confidence falls below $$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$2 with $$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$3 (Dai et al., 10 Oct 2025). Adding full BAP raises MeViS val$$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$4 from 58.9 to 62.0 and ReasonVOS from 57.4 to 61.7 in the reported ablation (Dai et al., 10 Oct 2025). This suggests that temporally-constrained reasoning systems benefit from propagation schemes whose anchor is semantically selected rather than fixed at the first frame.

In referring segmentation, "Temporally Consistent Referring Video Object Segmentation with Hybrid Memory" introduces a hybrid memory that stores local dense spatiotemporal features and global foreground/background tokens (Miao et al., 2024). Local propagation computes

$$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$5

while global tokens are aggregated with confidence-thresholded weighting (Miao et al., 2024). On Ref-YouTube-VOS, HTR with Swin-L achieves 67.1% and on Ref-DAVIS17 65.6% (Miao et al., 2024). The paper further proposes Mask Consistency Score,

$$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$6

which measures the fraction of videos whose IoU stays above threshold $$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$7 for all frames (Miao et al., 2024). HTR improves MCS@0.1 from 53.2 to 64.0, [email protected] from 44.7 to 54.4, and [email protected] from 7.6 to 11.6 relative to ReferFormer* in the reported comparison (Miao et al., 2024). MCS is not a reasoning metric per se, but it captures whether temporally constrained decisions persist without catastrophic drift.

For broader VIS, several methods formalize temporal consistency as identity-consistent sequence optimization. TCOVIS replaces local matching with Global Instance Assignment over the whole clip and adds a Spatio-Temporal Enhancement module (Li et al., 2023). On YouTube-VIS 2021, TCOVIS reaches 61.3 AP with Swin-L, and on YouTube-VIS 2022 it achieves 51.0 AP with Swin-L (Li et al., 2023). LOMM introduces Latest Object Memory and Decoupled Object Association, reaching 54.0 AP on YouTube-VIS 2022 in offline mode with ViT-L (Lee et al., 26 Jul 2025). IFR-based consistent VIS models use recurrent query updates and cross-frame mask prediction losses, reaching 62.1% on YouTubeVIS-2019 and 54.7% on YouTubeVIS-2021 (You et al., 2022). Although these methods are not language-grounded, they provide the temporal identity and memory mechanisms on which reasoning segmentation pipelines often depend.

Temporal consistency has also been studied as an evaluation or regularization principle outside reasoning-centric systems. Perceptual consistency measures agreement between segmentation and perceptual correspondences across nearby frames, yielding 0.985 on DriveSeg and 0.9985 on DAVIS for ground-truth segmentation temporal consistency in the reported experiments (Zhang et al., 2021). AuxAdapt shows that test-time adaptation can improve temporal consistency without optical flow at inference, for example raising HRNet-w48-s4 on Cityscapes from TC = 72.1 to 75.8 while maintaining mIoU = 81.0 (Zhang et al., 2021). TCNN uses latent temporal consistency in a learned shape space for semi-supervised surgical video segmentation, improving Endoscapes mean F1 from 70.91 for the Video Predictor to 73.06 for full TCNN (Alapatt et al., 2021). These works indicate that temporal reasoning segmentation inherits a larger technical problem: segmentation decisions must remain coherent under appearance changes, occlusion, and ambiguous local evidence.

6. Benchmarks, automated dataset construction, and empirical landscape

The empirical study of temporally-constrained reasoning segmentation depends strongly on benchmark design. Existing reasoning datasets such as ReVOS, ReasonVOS, MeViS, Ref-DAVIS17, Ref-YTVOS, Ref-SAV, and Long-RVOS stress different aspects of temporal logic, motion-centric description, and long-horizon reasoning (Dai et al., 5 Jun 2026, Kao et al., 24 May 2025, Gong et al., 15 Jan 2025, Wen et al., 8 May 2026, Dai et al., 10 Oct 2025).

Several papers report strongest gains exactly where temporal reasoning is hardest. "VideoSEG-O3-4B" reports state-of-the-art gains on Long-RVOS (+6.1% over previous SOTA), Ref-SAV (+15.5% over Sa2VA-8B), and MeViS with $$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$8 versus 55.8 for UniPixel-7B (Dai et al., 5 Jun 2026). On ReVOS it reaches 67.7 overall versus 61.3 for Veason-R1, and on ReasonVOS 62.9 versus 59.9 (Dai et al., 5 Jun 2026). "ThinkVideo" shows especially large gains on T-ReasonVOS, a curated subset containing temporally sensitive queries, where ThinkVideo-Gemma3-12B reaches 50.2 and ThinkVideo-GPT-4o 55.5 compared with 37.4 for VideoLISA (Kao et al., 24 May 2025). "MomentSeg" improves ReVOS overall to 62.6 and ReasonVOS to 61.7, outperforming VRS-HQ-7B and ViLLa-6B in the reported tables (Dai et al., 10 Oct 2025). "RCoT-Seg-3B" reaches 61.2 on ReVOS and 58.2 on ReasonVOS, outperforming Veason-R1 by +1.3 on ReVOS and +3.0 on ReasonVOS according to the paper’s comparison (Wen et al., 8 May 2026).

Benchmark construction itself has become a research topic. "Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction" formalizes a temporal constraint function $$\mathcal{M}_{\text{constrained}} = \{ M^{(t)} \cdot \tau_Q(t) \mid t = 1, 2, \ldots, T \}$$9 and proposes an automated pipeline for dataset construction using a digital twin representation $s_t = (\mathcal{V}_t, Q, \mathcal{H}_{t-1}),$0, integrating phase decomposition, SAM2 instance tracking, depth estimation, motion features, action descriptions, and semantic descriptions (Shen et al., 22 Jul 2025). Candidate object-phase pairs are selected by ensemble LLM voting

$s_t = (\mathcal{V}_t, Q, \mathcal{H}_{t-1}),$1

then verified through temporal alignment and converted into queries by an LLM generator (Shen et al., 22 Jul 2025). The resulting TCVideoRSBenchmark contains 52 samples from MVOR videos (Shen et al., 22 Jul 2025). This suggests that large-scale temporally constrained reasoning datasets may increasingly be constructed through semi-automated pipelines rather than fully manual annotation, especially in domain-specific settings such as surgical video.

"ViLLa" provides another important dataset contribution through VideoReasonSeg, designed to expose long durations, multiple objects, rapid motion, and heavy occlusions (Zheng et al., 2024). The paper attributes its improvements to a context synthesizer, a hierarchical temporal synchronizer, and a key segment sampler (Zheng et al., 2024). While the detailed quantitative table is not fully reproduced in the supplied data, the work is presented as a benchmark-building and architectural response to an underexplored regime of video reasoning segmentation (Zheng et al., 2024).

7. Limitations, controversies, and open problems

A recurring limitation is the dependence on keyframe quality and propagation reliability. "VideoSEG-O3" still depends on SAM2 as a separate decoder, and propagation errors can accumulate in very long or occluded sequences (Dai et al., 5 Jun 2026). "ThinkVideo" notes difficulty with multiple similar instances and ambiguous queries, and observes that online reasoning benchmarks remain underdeveloped (Kao et al., 24 May 2025). "VRS-HQ" reports failures on subtle dynamic reasoning such as identifying “the boat moving fastest” and on very short temporal presence (Gong et al., 15 Jan 2025). "RCoT-Seg" notes zero-target scenarios as a mismatch point: the reasoning trace may correctly infer absence, but the grounding stage can still output boxes and masks (Wen et al., 8 May 2026). HTR likewise fails when no high-quality reference mask exists in any frame (Miao et al., 2024).

Another open issue is how explicit temporal modeling should be. Some approaches rely on chain-of-thought and active exploration rather than dedicated temporal modules (Dai et al., 5 Jun 2026, Kao et al., 24 May 2025, Wen et al., 8 May 2026). Others argue for hierarchical temporal tokens and explicit temporal aggregators (Gong et al., 15 Jan 2025, Dai et al., 10 Oct 2025). A plausible implication is that the field has not yet converged on whether temporal structure is best represented as policy state, token hierarchy, memory bank, or cross-frame feature affinity.

Evaluation remains contested. Average frame-level metrics such as $s_t = (\mathcal{V}_t, Q, \mathcal{H}_{t-1}),$2 and $s_t = (\mathcal{V}_t, Q, \mathcal{H}_{t-1}),$3 do not fully capture catastrophic temporal failures, motivating measures such as MCS (Miao et al., 2024). For TSG-augmented systems, temporal localization metrics such as [email protected], [email protected], [email protected], and mIoU are informative but do not directly measure whether grounded moments actually improve segmentation (Dai et al., 10 Oct 2025). Conversely, RL-based reasoning traces may optimize temporal choices that are difficult to compare under conventional segmentation benchmarks alone (Dai et al., 5 Jun 2026, Wen et al., 8 May 2026). This suggests that a mature evaluation protocol will likely need joint metrics spanning temporal grounding, mask quality, propagation stability, and perhaps reasoning efficiency.

A broader controversy concerns the role of training-free versus end-to-end learned systems. "ThinkVideo" achieves large gains on temporally sensitive benchmarks without fine-tuning (Kao et al., 24 May 2025), whereas "VideoSEG-O3", "MomentSeg", and "RCoT-Seg" obtain improvements through tightly integrated multi-stage training (Dai et al., 5 Jun 2026, Dai et al., 10 Oct 2025, Wen et al., 8 May 2026). Training-free methods offer closed-source compatibility and modularity, but learned systems can align temporal reasoning, keyframe selection, and segmentation quality more directly. The coexistence of both paradigms indicates that temporally-constrained reasoning segmentation remains an architectural frontier rather than a settled problem class.

In aggregate, the literature defines Temporally-Constrained Video Reasoning Segmentation as the coupling of temporal inference, language grounding, and pixel-level localization under dynamic query validity. The most characteristic technical elements are active interval or keyframe selection, hierarchical or dedicated temporal tokens, segmentation-aware optimization objectives, and memory-based propagation anchored at semantically meaningful moments (Dai et al., 5 Jun 2026, Gong et al., 15 Jan 2025, Dai et al., 10 Oct 2025, Wen et al., 8 May 2026). The empirical trend across ReVOS, ReasonVOS, MeViS, Ref-SAV, Long-RVOS, and T-ReasonVOS is that models improve most when they explicitly model temporal decision-making rather than treating time as a passive sequence of frames (Dai et al., 5 Jun 2026, Kao et al., 24 May 2025). This suggests that the topic is best understood not as a minor variation of referring video segmentation, but as a distinct research area at the intersection of temporal grounding, multimodal reasoning, and temporally coherent video segmentation.