Temporal-Conditional Referring Video Segmentation

• The paper introduces temporal-conditional referring video segmentation, leveraging temporal cues and transformer architectures to produce consistent object masks across video frames.
• It employs innovative techniques like moment-aware gating, frame relevance scoring, and dynamic query allocation to align segmentation with temporal conditions.
• The work emphasizes multimodal conditioning from text and audio to address challenges such as long-range dependencies, semantic ambiguity, and efficiency-accuracy trade-offs.

Searching arXiv for papers on temporal-conditional referring video object segmentation and closely related RVOS methods. Temporal-Conditional Referring Video Object Segmentation is a video-language segmentation problem in which a model must produce masks for a referred object across time while conditioning prediction on temporal evidence rather than on isolated frames. In its broad formulation, the input is a video and a reference signal—most commonly a sentence, but in some work also audio—and the output is a sequence of binary masks for the same referred instance over time. In stricter “moment-aware” formulations, the language may apply only during specific temporal intervals, so segmentation is expected only when the described temporal condition holds. Across the literature, the topic has been developed through end-to-end transformer architectures, moment-aware grounding systems, foundation-model-based decompositions, and training-free agentic pipelines (Yan et al., 2023, Dai et al., 10 Oct 2025, Jin et al., 24 Mar 2026).

1. Task definition and scope

A standard RVOS formulation takes a video $V=\{I_t\}_{t=1}^T$ and a referring sentence $S$ or query $Q$, and predicts masks $\{M_t\}_{t=1}^T$ for the referred object across all frames. MUTR states this in multi-modal form: the inputs are a video clip consisting of $T$ sampled frames $\{I_1,I_2,\ldots,I_T\}$ and a reference in either text or audio, and the outputs are per-frame binary masks $\{M_t\}_{t=1..T}$ for the same referred object instance across the video. AgentRVOS uses the same task structure but emphasizes that queries may contain temporal cues such as actions, events, motion attributes, relative relations, or commonsense, and defines a per-candidate temporal existence set $\mathcal{T}(m_i)=\{t\mid m_i^t\neq\emptyset\}$ for a mask track (Yan et al., 2023, Jin et al., 24 Mar 2026).

The phrase “temporal-conditional” is used in two closely related senses. In the broader sense, RVOS decisions must depend jointly on sentence semantics and temporal context, because appearance changes, motion trajectories, occlusion and re-appearance, and size or pose variations cannot be resolved from a single frame. Tenet states this explicitly and treats temporal prompts as language-associated tracks of boxes spanning the video (Lin et al., 8 Oct 2025). In the stricter moment-centric sense, the condition described by the query is itself temporally sparse. MomentSeg formulates temporal-conditional RefVOS as predicting masks only at frames where a temporal condition holds, by introducing a framewise relevance score $s(t)$ and a temporal gate

$g(t)=\mathbf{1}[s(t)\ge \tau]$

for hard gating or

$S$0

for soft gating (Dai et al., 10 Oct 2025). SAMDWICH expresses the same idea with object-wise moment sets $S$1 and global referred frames $S$2, treating the complement $S$3 as text-irrelevant for text-conditioned supervision (Lee et al., 16 Aug 2025).

The scope of the topic has also widened beyond sentence-only RVOS. MUTR is notable for defining a unified setting in which either text tokens from RoBERTa or audio spectrogram features from VGGish serve as conditioning signals, under a shared DETR-style temporal transformer (Yan et al., 2023). This suggests that temporal-conditional segmentation is increasingly treated as a general conditional video understanding problem rather than as a sentence-only benchmark.

2. Mathematical and algorithmic foundations

A large portion of the literature uses transformer attention, set-based prediction, and dense mask supervision. MUTR adopts the standard attention form

$S$4

and trains with Hungarian assignment to align predicted sequences to ground-truth sequences. Its overall loss is

$S$5

with box loss

$S$6

and mask loss combining Dice and binary focal terms (Yan et al., 2023). TempCD follows the same general pattern, combining segmentation and box regression while maintaining direct per-frame referent selection through a global referent token rather than sequence-level matching across frames (Tang et al., 2023).

Dice- and focal-style mask objectives recur throughout the area. VLP-RVOS trains with

$S$7

with $S$8 and $S$9, while preserving the aligned vision-language feature space of a frozen VLP backbone (Zhou et al., 2024). LTCA adopts Mask2Former loss for frame-wise mask supervision together with VITA-style video-level losses, and summarizes a typical segmentation objective as a combination of Dice and sigmoid focal terms plus video-level consistency losses (Yan et al., 9 Oct 2025).

Moment-aware and track-based systems alter the optimization target by shifting supervision from dense end-to-end sequence decoding toward temporal prompt quality or track reasoning. Tenet trains a Prompt Preference Learning classifier with binary cross-entropy:

$Q$0

where $Q$1 if a candidate track’s average box mIoU exceeds that of the reference proposal (Lin et al., 8 Oct 2025). AgentRVOS does not define an explicit numeric scoring function; instead it performs iterative classification of candidate mask tracks into Accepted, Rejected, or Uncertain, updates

$Q$2

carries forward

$Q$3

and narrows temporal scope by

$Q$4

(Jin et al., 24 Mar 2026).

Several works also redefine the semantic representation being reasoned over. EventRR converts the referring expression into a Referential Event Graph (REG), a single-rooted directed acyclic graph with concept embeddings $Q$5, role embeddings $Q$6, and edges $Q$7, then accumulates referring scores from leaves to root with Object-Concept Align and Temporal Referent-Context Align terms (Xu et al., 10 Aug 2025). This is a categorical shift away from treating the sentence as an unstructured token sequence.

3. End-to-end temporal modeling architectures

Early end-to-end RVOS architectures already treated temporal conditioning as an architectural rather than purely loss-driven problem. The “Deeply Interleaved Two-Stream Encoder” inserts Vision-Language Mutual Guidance modules repeatedly into a CNN visual stream and a transformer linguistic stream, then applies a Language-guided Multi-scale Dynamic Filtering module that uses reference-frame and current-frame features under language guidance to generate position-specific dynamic filters for the current frame (Feng et al., 2022). In this formulation, temporal conditioning is local and causal: the current frame is explicitly updated using the previous frame.

A second family centers temporal modeling on query organization. TempCD maintains both a global referent token $Q$8 and per-frame object queries $Q$9, and alternates temporal collection and temporal distribution. Motion-consistent information is collected from frame-level queries to update the global token, then distributed back into a referent sequence across frames for cross-frame reasoning (Tang et al., 2023). MUTR likewise separates low-level and high-level temporal interaction: Multi-modal Temporal Aggregation concatenates multi-scale features across all sampled frames and injects them into text or audio tokens before DETR decoding, while Multi-modal Temporal Interaction performs object-wise inter-frame communication after frame-wise decoding through an MTI encoder and decoder, with

$\{M_t\}_{t=1}^T$0

(Yan et al., 2023).

A third line treats the main difficulty as consistent query identity over time. MTCM states that transformer-based RVOS suffers from query inconsistency and limited consideration of context, and introduces an Aligner and a Multi-Context Enhancer. The Aligner temporally reorders queries with Hungarian matching,

$\{M_t\}_{t=1}^T$1

then denoises them with previous-frame context and language; the Multi-Context Enhancer combines time-axis self-attention, temporal 1D convolution, instance-axis self-attention, and cross-attention with text (Choi et al., 9 Jan 2025). ReferDINO reaches a similar objective from a grounding-foundation starting point. It derives frame-conditioned text features from GroundingDINO, uses a memory-augmented tracker with momentum update

$\{M_t\}_{t=1}^T$2

and then decodes masks with a grounding-guided deformable mask decoder (Liang et al., 24 Jan 2025).

Another major trend is to reuse pretrained representation spaces rather than learn text-video alignment from scratch. VLP-RVOS keeps CLIP or VLMo frozen and adds temporal-aware prompt-tuning, Parameter-Reusing Temporal Capture, cube-frame attention, and multi-stage vision-language relation modeling (Zhou et al., 2024). VD-IT instead exploits a fixed pretrained text-to-video diffusion model, arguing that its latent representation encapsulates rich semantics and coherent temporal correspondences; it adds Text-Guided Image Projection and video-specific noise prediction on top of the frozen T2V U-Net (Zhu et al., 2024). A later diffusion-based variant removes the traditional noise prediction module entirely, uses a deterministic text-conditioned feature extractor, and emphasizes the design of the segmentation head through Hybrid CondDot and Temporal Context Mask Refinement (Zhang et al., 19 Aug 2025).

Temporal consistency has also been addressed through memory and sparse long-range attention. HTR builds a hybrid memory from automatically generated high-quality reference masks, combining pixel-level local memory and instance-level global foreground and background tokens, and evaluates consistency with the Mask Consistency Score

$\{M_t\}_{t=1}^T$3

(Miao et al., 2024). LTCA replaces dense full-video attention with dilated window attention, random global key selection, and explicit global queries, yielding linear-in-video-length complexity and direct long-range temporal context aggregation (Yan et al., 9 Oct 2025). SOC, from an earlier perspective, performs video-level object clustering and multimodal contrastive supervision so that frame-level object embeddings and language tokens occupy a better aligned video-level joint space (Luo et al., 2023).

4. Moment-aware, event-structured, and modular reasoning

A distinct branch of the literature argues that the principal difficulty in temporal-conditional RVOS is not dense mask decoding itself, but identifying when and where the language applies over time. MomentSeg unifies Temporal Sentence Grounding and RefVOS in a single LMM-based framework with a dedicated \texttt{[FIND]} token for key moment identification and a \texttt{[SEG]} token for segmentation. At inference, it derives $\{M_t\}_{t=1}^T$4 from frame-\texttt{[FIND]} similarity, smooths it, locates a moment center

$\{M_t\}_{t=1}^T$5

samples densely around that center, and then performs Bidirectional Anchor-updated Propagation from the anchor frame (Dai et al., 10 Oct 2025).

SAMDWICH makes moment supervision explicit at the dataset level. Built on MeViS-M, it annotates object-wise moment labels $\{M_t\}_{t=1}^T$6 and trains with Moment-guided Dual-path Propagation and Object-level Selective Supervision. Query features are gated by

$\{M_t\}_{t=1}^T$7

where $\{M_t\}_{t=1}^T$8 on referred frames and $\{M_t\}_{t=1}^T$9 otherwise, while memory writes are allowed only for text-relevant frames. Supervision is filtered by

$T$0

so that only temporally aligned objects contribute to the text-conditioned loss (Lee et al., 16 Aug 2025). This formulation directly targets the semantic noise produced by supervising all visible objects regardless of whether the expression refers to them.

EventRR pushes structural reasoning further by parsing expressions into a Referential Event Graph and performing Temporal Concept-Role Reasoning over that graph. For a parent concept $T$1, a temporal query $T$2, and child concepts $T$3, it defines Object-Concept Align

$T$4

and Temporal Referent-Context Align

$T$5

then accumulates

$T$6

from leaves to root (Xu et al., 10 Aug 2025). This is a formal treatment of event attributes and event-event temporal relations rather than a pure sequence encoder.

Modular and training-free systems decompose the task even more aggressively. Tenet reframes RVOS as referring, video, and segmentation factors, generates language-conditioned candidate tracks from Grounding DINO and OC-SORT, learns to prefer the sentence-consistent track, and then prompts SAM per frame with the selected boxes (Lin et al., 8 Oct 2025). AgentRVOS uses SAM3 to generate concept-driven mask tracks over the full video, lets an MLLM derive concepts from the query and reason over object-level evidence, and iteratively prunes candidates using temporal existence sets $T$7 (Jin et al., 24 Mar 2026). A plausible implication is that temporal-conditional RVOS is increasingly being treated as a reasoning-and-selection problem layered over strong generic perception modules.

5. Benchmarks, metrics, and empirical trends

The literature evaluates temporal-conditional RVOS on several benchmark families. Ref-YouTube-VOS and Ref-DAVIS17 are the most common text-referred benchmarks; AVSBench adds audio-referred segmentation; A2D-Sentences and JHMDB-Sentences retain importance for sentence-conditioned actor-action segmentation; MeViS, ReVOS, and ReasonVOS emphasize motion, reasoning, and temporal semantics; Charades-STA and ActivityNet-Grounding appear in work that explicitly combines temporal grounding with segmentation (Yan et al., 2023, Dai et al., 10 Oct 2025, Jin et al., 24 Mar 2026).

Most papers report region similarity $T$8, contour accuracy $T$9, and their average $\{I_1,I_2,\ldots,I_T\}$0. Some also report box mIoU, mAP, oIoU, mIoU, Recall@IoU for temporal grounding, or specialized consistency metrics such as HTR’s MCS. SAMDWICH is an exception in presentation: its tables report J&F as the sum rather than the average (Miao et al., 2024, Lee et al., 16 Aug 2025).

Representative quantitative results show that gains often come from better temporal conditioning rather than from stronger backbones alone. MUTR reports 68.4 J&F on Ref-YouTube-VOS with Swin-L versus ReferFormer’s 64.2, a gain of +4.2, and 61.6 versus 52.9 on AVSBench MS3 with ResNet-50, a gain of +8.7 (Yan et al., 2023). VLP-RVOS reports 67.6 J&F on Ref-YouTube-VOS and 70.2 on Ref-DAVIS17 with VLMo-L after Ref-COCO/+/g pretraining (Zhou et al., 2024). VD-IT reaches 67.2 J&F on Ref-YouTube-VOS and 69.4 on Ref-DAVIS17 with RefCOCO/+/g pretraining (Zhu et al., 2024). ReferDINO reports 69.3 J&F on Ref-YouTube-VOS and 68.9 on Ref-DAVIS17 with Swin-B, together with 51 FPS real-time inference speed (Liang et al., 24 Jan 2025).

Methods emphasizing moment or reasoning structure produce particularly strong results on temporally difficult benchmarks. MomentSeg-3B reports 72.0 on Ref-YTVOS and 76.4 on Ref-DAVIS17, while its 7B version improves Ref-DAVIS17 to 77.4 and ReVOS Overall to 65.1 (Dai et al., 10 Oct 2025). LTCA reports 69.1 on Ref-YouTube-VOS and 68.6 on Ref-DAVIS17 with Swin-L, and records improvements of 11.3% and 8.3% on the MeViS val$\{I_1,I_2,\ldots,I_T\}$1 and val datasets respectively (Yan et al., 9 Oct 2025). EventRR reports 66.9 J&F on Refer-Youtube-VOS and 65.4 on DAVIS17-RVOS with Video-Swin-B, while improving A2D-Sentences to 81.4 oIoU and 73.1 mIoU (Xu et al., 10 Aug 2025).

Weakly supervised, modular, and training-free systems also reach competitive levels. Tenet reports 65.5 J&F on Ref-YouTube-VOS and 71.0 on Ref-DAVIS17 while training only the detector adaptation and Prompt Preference Learning under box supervision, with about 45M trainable parameters versus about 112M for ReferFormer and DEVA and about 221M for OnlineRefer (Lin et al., 8 Oct 2025). AgentRVOS reports 61.9 J&F on MeViS, 59.8 on ReVOS Overall, and 68.6 on ReasonVOS with Qwen3-VL-8B-Thinking, and scales up to 73.1 on MeViS and 75.5 on ReasonVOS with GPT-5 (Jin et al., 24 Mar 2026). These results indicate that strong temporal prompts or object tracks can substitute for dense end-to-end mask training when the upstream perception module is sufficiently capable.

6. Limitations, failure modes, and open directions

The literature converges on several recurring limitations. One is the difficulty of long-range temporal dependency. MUTR notes that the temporal window is fixed to sampled frames and that long-range dependencies beyond the clip length might be under-exploited (Yan et al., 2023). VLP-RVOS similarly remarks that clip-local Parameter-Reusing Temporal Capture and cube-frame attention may miss very long events or rare reappearances (Zhou et al., 2024). LTCA addresses this partially with sparse long-range attention, but still identifies extremely long videos and sparse target presence as challenging (Yan et al., 9 Oct 2025).

A second limitation is dependence on upstream proposal, detector, tracker, or foundation-model quality. Tenet relies on Grounding DINO and OC-SORT to produce sufficiently accurate and diverse proposals (Lin et al., 8 Oct 2025). AgentRVOS states that if SAM3 misses the target, subsequent reasoning cannot recover it (Jin et al., 24 Mar 2026). HTR likewise depends on automatically generated high-quality reference masks: if selective segmentation fails to produce reliable references, memory propagation cannot recover (Miao et al., 2024).

A third issue is semantic ambiguity in language and temporally sparse supervision. SAMDWICH argues that indiscriminate frame sampling and supervision of all visible objects induce semantic misalignment, especially when expressions refer to objects only during specific moments (Lee et al., 16 Aug 2025). EventRR shows that treating expressions as unstructured token sequences neglects event attributes and event-event temporal relations that are central in video-referring expressions (Xu et al., 10 Aug 2025). MomentSeg observes that lack of explicit absolute-time encoding can challenge queries such as “at the end of the video” (Dai et al., 10 Oct 2025).

Efficiency–accuracy trade-offs remain explicit. MUTR reports that adding MTA and MTI increases parameters from 168.1M to 177.6M and slightly reduces FPS from 19.64 to 19.37, while improving J&F from 60.2 to 61.9 on Ref-YouTube-VOS with ResNet-50 (Yan et al., 2023). ReferDINO introduces confidence-aware query pruning precisely because direct processing of 900 GroundingDINO queries is expensive; with a 50% query drop it reduces FLOPs by about 33.7% and memory by about 36.6% with negligible performance change (Liang et al., 24 Jan 2025). LTCA explicitly targets linear complexity in video length to avoid the quadratic cost of dense temporal attention (Yan et al., 9 Oct 2025).

Open directions stated across the papers include stronger multimodal pretraining, memory-efficient temporal modules for longer sequences, dynamic temporal windows, adaptive query allocation, improved motion and tracking signals, enhanced audio encoders, explicit event detectors, better temporal alignment and memory, and richer structured reasoning over multi-object and relation-aware temporal prompts (Yan et al., 2023, Lin et al., 8 Oct 2025, Jin et al., 24 Mar 2026, Dai et al., 10 Oct 2025, Xu et al., 10 Aug 2025). Taken together, these directions suggest that future temporal-conditional RVOS systems will likely combine three ingredients: stronger generic perception, more explicit temporal semantics, and more selective supervision over when language should influence segmentation.