TimeExpert: Expert-Guided Video Temporal Grounding

• TimeExpert is a VTG model that decomposes video tasks into distinct outputs: timestamps, saliency scores, and captions.
• It employs a Mixture-of-Experts decoder with dynamic routing and adaptive expert management to specialize token processing.
• Empirical evaluations on datasets like YouCook2, Charades-STA, and QVHighlights show improved performance with lower activated parameter counts.

Searching arXiv for the primary paper and adjacent work on temporal experts and video temporal grounding. {"^^^^^^^^8query8 OR abs:\8^^^^^^^^"Video Temporal Grounding\" OR ^^^^^^^^8ti:\8 OR ^^^^^^^^8ti:\8 OR ^^^^^^^^8ti:\8 OR ^^^^^^^^8ti:\8 Tracker\" OR ^^^^^^^^8ti:\8 Experts Averaging\"","max_results":^^^^8ti:\8query8^^^^,"sort_by":"relevance"} I found relevant arXiv entries matching the topic, including the primary "TimeExpert" paper and adjacent work on temporal specialization, video understanding, time-series experts, and temporal generalization. TimeExpert is an expert-guided Video-LLM for Video Temporal Grounding (VTG) that treats temporal localization, saliency assessment, and textual generation as distinct subtasks within a unified structured-generation framework. In this formulation, VTG outputs are sequences of events, each event containing a timestamp, a saliency score, and a caption, and the model uses a Mixture-of-Experts (MoE) decoder to route task-specific tokens to specialized experts rather than sending all outputs through a single static decoding pathway (&&&^^^^8query8^^^^&&&).

^^^^8ti:\8^^^^. Problem formulation and task scope

TimeExpert is built for VTG, a setting in which a model must identify when an event occurs in a video, often also estimate how salient it is, and in some tasks generate a textual description. The paper unifies Dense Video Captioning (DVC), Moment Retrieval (MR), and Video Highlight Detection (VHD) by representing output as an event sequence

PRESERVED_PLACEHOLDER_^^^^8query8^^^^

where each event is

PRESERVED_PLACEHOLDER_^^^^8ti:\8^^^^

This yields a causal event-modeling objective

PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8^^^^

with PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8^^^^ denoting the instruction or textual ^^^^^^^^8query8^^^^^^^^ and PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8^^^^ the video-frame sequence (&&&^^^^8query8^^^^&&&).

Within this formulation, DVC corresponds to predicting multiple temporally ordered events with timestamps and captions, MR emphasizes timestamp prediction for a ^^^^^^^^8query8 segment, and VHD emphasizes saliency prediction. A central claim of the model is that these outputs should not be treated as homogeneous text tokens. The paper identifies a limitation in prior Video-LLMs: they process all task tokens through identical and static pathways even though timestamp prediction, saliency scoring, and caption generation are materially different computations (&&&^^^^8query8^^^^&&&).

Task	Output emphasis	Reported metrics
DVC	timestamps + captions	SODAPRESERVED_PLACEHOLDER_^^^^8 OR ti:\8^^^^, CIDEr, F^^^^8ti:\8^^^^ Score, METEOR
MR	temporal segment localization	PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8^^^^, PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8^^^^, mIoU
VHD	saliency/highlight prediction	mAP, HIT@^^^^8ti:\8^^^^

This event-centric view makes TimeExpert a structured VTG model rather than a generic video-captioning system. A common misconception is that it is merely a sparse decoder added to a Video-LLM; the paper’s actual thesis is narrower and more specific, namely that VTG requires specialization at the level of task-token types (&&&^^^^8query8^^^^&&&).

^^^^8 OR abs:\8^^^^. Architectural decomposition

TimeExpert adopts ARIA as its base MoE Video-LLM and replaces the single shared decoder pathway with a fine-grained MoE decoder. Its architecture contains a lightweight visual encoder with ^^^^8 OR ti:\8 OR ti:\88^^^^M parameters, built from a Vision Transformer plus a projection module, and each video frame is encoded into ^^^^8ti:\8 OR abs:\88^^^^ or ^^^^8 OR abs:\8 OR ti:\8 OR ti:\8^^^^ visual tokens before slot-based token compression reduces them to 8 visual tokens per frame (&&&^^^^8query8^^^^&&&).

The model also introduces task-specific encoders and heads. Time tokens and score tokens are processed by separate encoders, and the paper states that the time encoder and score encoder share the same architecture. These modules are initialized with a tokenizer containing ^^^^8ti:\8ti:\8^^^^ number tokens, a separator token $\langle sep \rangle$, and a switching token $\langle sync \rangle$. The full system then uses independent decoding heads for time, score, and text outputs, with generation following the structured order time tokens PRESERVED_PLACEHOLDER_^^^^8ti:\8query8^^^^ score tokens PRESERVED_PLACEHOLDER_^^^^8ti:\8ti:\8^^^^ text tokens for each event (&&&^^^^8query8^^^^&&&).

This decomposition is not a manually hard-coded “timestamp expert” versus “caption expert” mapping inside the MoE. Rather, specialization is induced by routing dynamics, activation statistics, adaptive expert addition and pruning, and an auxiliary loss designed for VTG token types. The paper reports that removing separate encoders and decoding heads causes the model to fail to follow instructions, which it treats as strong evidence that timestamps and scores should not be folded into an undifferentiated text-generation interface (&&&^^^^8query8^^^^&&&).

^^^^8 OR ti:\8^^^^. Expert routing, specialization, and auxiliary objectives

The routing mechanism starts from a standard sparse MoE baseline. For input token embedding PRESERVED_PLACEHOLDER_^^^^8ti:\8 OR abs:\8^^^^, vanilla gating is written as

PRESERVED_PLACEHOLDER_^^^^8ti:\8 OR ti:\8^^^^

and the MoE output is

PRESERVED_PLACEHOLDER_^^^^8ti:\8 OR ti:\8^^^^

TimeExpert argues that fixed top-PRESERVED_PLACEHOLDER_^^^^8ti:\8 OR ti:\8^^^^ routing is inadequate for VTG because it uses one fixed activation budget for all token types and ignores task-token importance (&&&^^^^8query8^^^^&&&).

Its task-aware dynamic gating first computes expert similarity

PRESERVED_PLACEHOLDER_^^^^8ti:\8 OR ti:\8^^^^

then activates experts with

PRESERVED_PLACEHOLDER_^^^^8ti:\8 OR ti:\8^^^^

where PRESERVED_PLACEHOLDER_^^^^8ti:\88^^^^ is the historical activation rate for the token’s task type, PRESERVED_PLACEHOLDER_^^^^8ti:\8^^^^9 controls task-importance influence, PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8query8^^^^ is sigmoid, and PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8ti:\8^^^^ is a learnable threshold vector. The sign operator is handled with a straight-through-style estimator by copying the upstream gradient to the pre-activation term (&&&^^^^8query8^^^^&&&).

TimeExpert further extends routing with token-adaptive expert management. It records expert activation counts PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8 OR abs:\8^^^^, aggregates embeddings of tokens that fail to activate any expert into PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8 OR ti:\8^^^^, and adds new experts when underrepresented token populations accumulate:

PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8 OR ti:\8^^^^

Persistently inactive experts are pruned by

PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8 OR ti:\8^^^^

The paper’s interpretation is that the expert set itself should adapt to the empirical distribution of timestamp, score, and text tokens rather than remain fixed throughout training (&&&^^^^8query8^^^^&&&).

Specialization is further encouraged by a task-dependent auxiliary loss

PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8 OR ti:\8^^^^

where PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8 OR ti:\8^^^^ is expert activation count, PRESERVED_PLACEHOLDER_^^^^8 OR abs:\88^^^^ is the number of task tokens assigned to that expert, and PRESERVED_PLACEHOLDER_^^^^8 OR abs:\8^^^^9 is the expert representation vector. This loss is explicitly distinguished from conventional MoE load balancing: its aim is not uniform traffic alone, but alignment between expert usage and task-token distributions (&&&^^^^8query8^^^^&&&).

^^^^8 OR ti:\8^^^^. Training procedure and inference behavior

TimeExpert is trained in three stages. The first stage, Task Module Pretraining, trains the vision compression layer, task encoder, and task heads on ^^^^8ti:\8^^^^.9M samples from Valley, LLaVA-Image, TextVR, ShareGPT^^^^8 OR ti:\8^^^^Video, and VTG-IT using cross-entropy over generated tokens. The second stage, MoE Decoder Pretraining, introduces the MoE decoder and trains it on ^^^^8query8^^^^.9M samples from ValleyPRESERVED_PLACEHOLDER_^^^^8 OR ti:\8query8^^^^, TextVRPRESERVED_PLACEHOLDER_^^^^8 OR ti:\8ti:\8^^^^, ShareGPT^^^^8 OR ti:\8^^^^VideoPRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR abs:\8^^^^, VTG-ITPRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^, ActivityNet Captions, VideoChatGPT, InternVid, and Next-QA using cross-entropy, z-loss, and the task-dependent auxiliary loss. The third stage, Supervised Fine-tuning, jointly fine-tunes the full framework except the visual encoder on ^^^^8 OR abs:\8^^^^.^^^^8 OR ti:\8^^^^M samples from filtered and reannotated data including previous-stage sources as well as EgoQA, STAR, Moment-^^^^8ti:\8query8^^^^M, and LLaVA-Video-^^^^8ti:\8 OR ti:\88^^^^K (&&&^^^^8query8^^^^&&&).

Inference remains autoregressive, but generation is structured by event fields. The decoder produces time, score, and text segments in a serialized event format, using PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ between consecutive timestamps or scores and PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ at task transitions or sequence end. The paper does not provide a fully explicit literal template string, but it is clear that the response is not unconstrained free text; it is a typed event sequence whose token categories directly drive routing behavior (&&&^^^^8query8^^^^&&&).

The paper also reports token-dependent expert utilization. The adaptive-PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ variant averages ^^^^8ti:\8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^ / 9.^^^^8ti:\8^^^^ / ^^^^8ti:\8ti:\8^^^^.8 experts across the three zero-shot benchmarks, while the variant trained on TRACE’s data recipe averages ^^^^8ti:\8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^ / 8.^^^^8 OR ti:\8^^^^ / ^^^^8ti:\8query8^^^^.^^^^8 OR ti:\8^^^^ experts. This is presented as evidence that TimeExpert does not require one fixed expert budget across DVC, MR, and VHD (&&&^^^^8query8^^^^&&&).

^^^^8 OR ti:\8^^^^. Empirical performance and ablation evidence

TimeExpert is evaluated on YouCook^^^^8 OR abs:\8^^^^ for DVC, Charades-STA for MR, QVHighlights for VHD, and ActivityNet Captions for both DVC-style and MR-style evaluation. In zero-shot evaluation against TRACE, TimeExpert with adaptive PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ improves YouCook^^^^8 OR abs:\8^^^^ from SODAPRESERVED_PLACEHOLDER_^^^^8 OR ti:\88^^^^ ^^^^8 OR abs:\8^^^^.^^^^8 OR abs:\8^^^^ to ^^^^8 OR abs:\8^^^^.^^^^8 OR ti:\8^^^^, CIDEr 8.^^^^8ti:\8^^^^ to 8.^^^^8 OR abs:\8^^^^, and F^^^^8ti:\8^^^^ ^^^^8 OR abs:\8 OR abs:\8^^^^.^^^^8 OR ti:\8^^^^ to ^^^^8 OR abs:\8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^; on Charades-STA it improves PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8^^^^9 from ^^^^8 OR ti:\8query8^^^^.^^^^8 OR ti:\8^^^^ to ^^^^8 OR ti:\8 OR abs:\8^^^^.8 and PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8query8^^^^ from ^^^^8ti:\8^^^^9.^^^^8 OR ti:\8^^^^ to ^^^^8 OR abs:\8query8^^^^.^^^^8 OR ti:\8^^^^; on QVHighlights it improves mAP from ^^^^8 OR abs:\8 OR ti:\8^^^^.8 to ^^^^8 OR abs:\8^^^^9.^^^^8 OR ti:\8^^^^ and HIT@^^^^8ti:\8^^^^ from ^^^^8 OR ti:\8 OR abs:\8^^^^.^^^^8 OR ti:\8^^^^ to ^^^^8 OR ti:\8 OR ti:\8^^^^.9. A TimeExpert variant trained on TRACE’s data recipe still outperforms TRACE, which the paper uses to argue that the gains are not attributable only to a broader training mixture (&&&^^^^8query8^^^^&&&).

After fine-tuning for two epochs, the model improves over TRACE on YouCook^^^^8 OR abs:\8^^^^ from SODAPRESERVED_PLACEHOLDER_^^^^8 OR ti:\8ti:\8^^^^ ^^^^8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^ to ^^^^8 OR ti:\8^^^^.^^^^8 OR abs:\8^^^^, CIDEr ^^^^8 OR ti:\8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^ to ^^^^8 OR ti:\8^^^^9.^^^^8query8^^^^, and F^^^^8ti:\8^^^^ ^^^^8 OR ti:\8ti:\8^^^^.8 to ^^^^8 OR ti:\8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^. On Charades-STA, it improves PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR abs:\8^^^^ from ^^^^8 OR ti:\8ti:\8^^^^.^^^^8 OR ti:\8^^^^ to ^^^^8 OR ti:\8 OR ti:\8^^^^.^^^^8ti:\8^^^^ and PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ from ^^^^8 OR ti:\8ti:\8^^^^.^^^^8 OR ti:\8^^^^ to ^^^^8 OR ti:\8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^. On ActivityNet Captions, it reports METEOR ^^^^8 OR ti:\8^^^^.^^^^8query8^^^^, SODAPRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ ^^^^8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^, CIDEr ^^^^8 OR abs:\88^^^^.^^^^8 OR ti:\8^^^^, F^^^^8ti:\8^^^^ ^^^^8 OR ti:\8query8^^^^.^^^^8 OR ti:\8^^^^, R@^^^^8ti:\8^^^^@^^^^8query8 OR ti:\8^^^^ ^^^^8 OR ti:\8^^^^9.^^^^8 OR abs:\8^^^^, R@^^^^8ti:\8^^^^@^^^^8query8 OR ti:\8^^^^ ^^^^8 OR abs:\8 OR ti:\8^^^^.^^^^8ti:\8^^^^, and mIoU ^^^^8 OR ti:\8ti:\8^^^^.^^^^8 OR ti:\8^^^^, outperforming TRACE on CIDEr, F^^^^8ti:\8^^^^, R@^^^^8ti:\8^^^^@^^^^8query8 OR ti:\8^^^^, and mIoU (&&&^^^^8query8^^^^&&&).

The efficiency argument is narrower than a full systems benchmark. The paper reports activated parameter counts rather than latency or FLOPs. Dense ^^^^8 OR ti:\8^^^^B baselines such as TimeChat, VTG-LLM, and TRACE use ^^^^8 OR ti:\8^^^^B active parameters, whereas TimeExpert uses approximately ^^^^8 OR ti:\8^^^^.9B / ^^^^8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^B / ^^^^8 OR ti:\8^^^^.8B activated parameters across DVC, MR, and VHD in one setting, and approximately ^^^^8 OR ti:\8^^^^.^^^^8 OR abs:\8^^^^B / ^^^^8 OR ti:\8^^^^.^^^^8ti:\8^^^^B / ^^^^8 OR ti:\8^^^^.^^^^8query8^^^^B in the TRACE-data setting (&&&^^^^8query8^^^^&&&).

The ablations are structurally informative. Removing token-adaptive routing lowers DVC SODAPRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ from ^^^^8 OR abs:\8^^^^.^^^^8 OR ti:\8^^^^ to ^^^^8 OR abs:\8^^^^.^^^^8ti:\8^^^^, MR PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ from ^^^^8 OR abs:\8query8^^^^.^^^^8 OR ti:\8^^^^ to ^^^^8ti:\8^^^^9.^^^^8 OR abs:\8^^^^, and VHD HIT@^^^^8ti:\8^^^^ from ^^^^8 OR ti:\8 OR ti:\8^^^^.9 to ^^^^8 OR ti:\8 OR abs:\8^^^^.^^^^8 OR ti:\8^^^^. Removing the task-dependent loss causes smaller but consistent drops, including DVC F^^^^8ti:\8^^^^ ^^^^8 OR abs:\8 OR ti:\8^^^^.^^^^8 OR ti:\8^^^^ \rightarrow ^^^^8 OR abs:\8 OR abs:\8^^^^.8, MR PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ ^^^^8 OR abs:\8query8^^^^.^^^^8 OR ti:\8^^^^ \rightarrow ^^^^8ti:\8^^^^9.^^^^8 OR ti:\8^^^^, and VHD HIT@^^^^8ti:\8^^^^ ^^^^8 OR ti:\8 OR ti:\8^^^^.9 \rightarrow ^^^^8 OR ti:\8 OR ti:\8^^^^.^^^^8 OR abs:\8^^^^. Fixed-PRESERVED_PLACEHOLDER_^^^^8 OR ti:\88^^^^ MoE ablations show PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8^^^^9 is weaker, PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8query8^^^^ is better, PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8ti:\8^^^^ is best or tied-best, and PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR abs:\8^^^^ saturates. The paper interprets this as evidence that expert multiplicity helps up to the point at which redundancy dominates, motivating adaptive routing rather than a globally fixed PRESERVED_PLACEHOLDER_^^^^8 OR ti:\8 OR ti:\8^^^^ (&&&^^^^8query8^^^^&&&).

^^^^8 OR ti:\8^^^^. Position within temporal-AI research, strengths, and limitations

TimeExpert belongs to a broader pattern in temporal modeling: decomposition into specialized submodules is often used when temporal outputs are heterogeneous. In video understanding, VideoExpert also separates temporal grounding from language generation, but it does so through parameter-decoupled Temporal Expert and Spatial Expert modules coordinated by a special <LOC> token rather than through token-level MoE routing inside a unified decoder (&&&^^^^8ti:\88^^^^&&&). In time-series forecasting, xTime organizes experts by rarity levels for extreme-event prediction (&&&^^^^8ti:\8^^^^9&&&), Time Tracker inserts sparse experts into a decoder-only forecasting foundation model to address heterogeneous temporal patterns and multivariate dependencies (&&&^^^^8 OR abs:\8query8^^^^&&&), and Temporal Experts Averaging (TEA) uses one expert per temporal domain and combines them by parameter-space averaging rather than per-token routing (&&&^^^^8 OR abs:\8ti:\8^^^^&&&). TimeExpert differs from all of these by specializing directly over VTG token types—timestamps, saliency scores, and text—inside a Video-LLM decoder (&&&^^^^8query8^^^^&&&).

Its main strengths, as stated by the paper, are threefold. First, it turns VTG into structured event generation rather than flat text generation. Second, it explicitly models task-token importance through dynamic expert routing, adaptive expert addition and removal, and dedicated time/score/text interfaces. Third, it reaches state-of-the-art performance on DVC, MR, and VHD among the compared VTG-specific Video-LLMs while often activating fewer parameters than dense ^^^^8 OR ti:\8^^^^B baselines (&&&^^^^8query8^^^^&&&).

The paper also leaves several limitations visible. Some method details are underspecified, including full formulas for the main autoregressive loss, z-loss, and exact decoding templates. The efficiency claims rely on activated parameter counts rather than full FLOP, latency, wall-clock, or memory benchmarks. Dynamic expert management introduces additional control logic, and the three-stage training pipeline uses millions of samples and substantial dataset curation. The method also depends on a structured serialization design for time, score, and text tokens, so a plausible implication is that performance is tied to the chosen output format and tokenizer design rather than arising solely from generic MoE scaling (&&&^^^^8query8^^^^&&&).

Taken together, TimeExpert can be understood as a VTG-specific answer to a precise modeling claim: timestamp localization, saliency estimation, and caption generation should not share one undifferentiated decoding path. Its technical novelty lies not in MoE alone, but in combining structured event outputs, task-aware token routing, adaptive expert management, and dedicated time/score/text interfaces within a single Video-LLM framework (&&&^^^^8query8^^^^&&&).