Temporal Adapter in Neural Models

• Temporal adapter is a lightweight neural module that efficiently injects temporal reasoning into static backbones using methods like convolution, attention, and memory augmentation.
• It seamlessly integrates with various architectures such as transformers, CNNs, and multimodal models, enabling effective adaptation with minimal parameters and computational overhead.
• Empirical results demonstrate that temporal adapters achieve state-of-the-art performance in video action recognition, tracking, forecasting, and clinical EEG analysis.

A temporal adapter is a lightweight, often parameter-efficient neural network module designed to inject or refine temporal modeling capabilities into a backbone architecture that lacks, or insufficiently exploits, temporal dependencies. Temporal adapters are integrated into diverse foundation models—in video, multimodal tracking, time series forecasting, and neural time-series (e.g., EEG) analysis—as mechanisms to enable temporal reasoning, transfer learning across modalities or domains, and efficient adaptation to task-specific dynamics. The breadth of designs encompasses convolutional, attention-based, memory-augmented, and Gaussian-process formulations, unified by their distinctive role as isolatable layers or branches responsible for temporal context modeling with minimal additional parameters or computational overhead.

1. Core Principles and Mathematical Structures

Temporal adapters are instantiated in various architectural forms, tailored to the specific backbone and application domain. The central principle is the separation of temporal modeling capacity from the main backbone, implemented through parallelism (branching), bottleneck design, residual connections, and specialized fusion or gating mechanisms.

Key constructions include:

• Parallel Branching: Double-branch adapters (e.g., LoSA (Gupta et al., 2024), D²ST-Adapter (Pei et al., 2023)) run short- and long-range temporal branches in parallel, each using distinct temporal receptive fields (small, local convolutions vs. global or cross-attention).
• Depthwise Temporal Convolutions: Modules like ST-Adapter (Pan et al., 2022) and DMTrack's STMA (Li et al., 3 Aug 2025) use per-channel or depthwise Conv1d/3D operators along the temporal axis for efficient local context modeling.
• Attention-based Temporal Modeling: MV-Adapter's Temporal Adaptation Module (Jin et al., 2023) and LoSA's (cross-)attention-based temporal aggregators leverage self-attention for global context.
• Memory-Augmented Adapters: VMDA's multi-bank memory adapter (Xu et al., 30 Jun 2025) incorporates FIFO, attention-refreshed long-term memory, and permanent memory for robust multi-scale temporal cue propagation.
• Residual and Gating Fusion: Gated mechanisms, as in LoSA (Gupta et al., 2024), fuse temporal adapter outputs with the backbone's identity path:

$$F_{\mathrm{out}}(X) = g(X)\odot F_s(X) + (1-g(X))\odot F_l(X) + X,$$

where $F_s, F_l$ denote short/long temporal branches and $g(X)$ is a dynamically computed gate.

• Position and Token-Type Adaptation: Modules such as STAMP (Shook et al., 13 Nov 2025) incorporate combined spatial, token-wise, and temporal positional encodings to supplement the frozen backbone's representations prior to GMLP-based temporal gating.

2. Architectural Integration with Backbones

The mode of adapter integration is dictated by the operational constraints (e.g., frozen backbone, compute/memory efficiency), the base model's inductive biases, and the target downstream task:

• Transformers (ViT, CLIP, VideoMAE, etc.): Temporal adapters are inserted into each transformer block (LoSA), after the FFN or just before/after MHSA (ST-Adapter, MV-Adapter), or in a side-branch parallel to the main path (BT-Adapter (Liu et al., 2023)).
• CNN Backbones: D²ST-Adapter is placed after major convolutional stages with channel-reduction, followed by dual deformable attention branches.
• Multimodal Architectures: Adapters are duplicated per modality, applied independently, or jointly coupled by cross-fusion (DSTA (Zeng et al., 2024), STMA (Li et al., 3 Aug 2025)).
• Head-Only or On-top MLPs: For time-series applications, adapters are inserted as the sole trainable "head" atop a frozen TSFM, with all task-specific transformations happening prior to the classifier (STAMP (Shook et al., 13 Nov 2025)).

Adapters are tuned, while the large backbone remains frozen, preserving generalization and dramatically reducing parameter and memory footprints. Example: LoSA adapts only ∼10–15% of backbone parameters, conferring full backbone adaptation at the memory cost of head-only training (Gupta et al., 2024).

3. Temporal Adapter Methodologies by Domain

Below is a summary table of representative temporal adapters and their defining characteristics across major application classes:

Adapter	Temporal Mechanism	Backbone Integration
LoSA (Gupta et al., 2024)	Short/long conv, gated fusion	All ViT blocks
ST-Adapter (Pan et al., 2022)	Depthwise 3D conv (T), residual	Before MHSA in ViT
D²ST-Adapter (Pei et al., 2023)	Dual deformable 3D attention	After Conv/ViT block
VMDA (Xu et al., 30 Jun 2025)	3-level memory bank + attention	Per-layer token fusion
DSTA (Zeng et al., 2024)	Bi-directional adapter MLPs	Select Transformer layers
MV-Adapter (Jin et al., 2023)	Temporal transformer + calibration	Post-FFN in every block
TFMAdapter (Dange et al., 17 Sep 2025)	GP regressor cascade	On-top TSFM, frozen
STMA (Li et al., 3 Aug 2025)	Bottleneck, depthwise 1D conv	Each modality, per layer
BT-Adapter (Liu et al., 2023)	Branch transformer, divided attention	Parallel video branch
STAMP (Shook et al., 13 Nov 2025)	CC-GMLP temporal gating, pooling	On frozen EEG TSFM

This modularity enables swift and memory-efficient adaptation to the unique temporal requirements of each domain (e.g., local motion for action recognition, long-range dependencies for tracking, uncertainty handling for clinical EEG).

4. Parameter Efficiency, Memory Footprint, and Design Tradeoffs

Temporal adapters are characterized by their minimal parameter increment relative to full fine-tuning:

• LoSA adds 12–143M params for models up to 1B parameters (14% of full) (Gupta et al., 2024).
• ST-Adapter uses 7.2–14M params in ViT-B/L (6–8% of full) and matches or exceeds full fine-tuning (Pan et al., 2022).
• D²ST-Adapter maintains ≤8% overhead by strict channel bottlenecks and disentanglement (Pei et al., 2023).
• BT-Adapter introduces only 2.3M parameters as a temporal branch for CLIP, leveraging high asymmetric masking to further lower the pretraining cost (Liu et al., 2023).
• STAMP maintains 0.7–0.8M parameters for clinical EEG TSFM adaptation (~1/10th of bespoke EEGFMs) (Shook et al., 13 Nov 2025).
• STMA in DMTrack keeps per-layer adapter size at O($10^4$), total 0.23M per modality, resulting in a full model adaptation budget of 0.93M (≈0.9% of the backbone) (Li et al., 3 Aug 2025).

GPU memory requirements follow suit; for instance, LoSA reduces training memory for VideoMAEv2-ViT-g from out-of-memory in full tuning to 40.6GB, enabling end-to-end adaptation for the first time on billion-parameter models (Gupta et al., 2024).

These design tradeoffs are validated by ablation studies demonstrating nearly all temporal benefit is attributable to these adapters, with further parameter reduction causing only slight accuracy degradation.

5. Empirical Results and Application Impact

Temporal adapters have been shown to deliver or exceed state-of-the-art results in their respective domains at a fraction of the training cost:

• Video Action Localization/Recognition: LoSA on THUMOS-14 and ActivityNet (e.g., +3.1pp and +1.4pp mAP over prior bests) (Gupta et al., 2024), D²ST-Adapter +4–5pp over prior adapters on SSv2 1-shot (Pei et al., 2023), ST-Adapter matching/outperforming full fine-tuning on K400 and SSv2 (Pan et al., 2022).
• Multimodal and Memory/Tracking: VMDA's full-memory bank boosts precision by +3pp over baseline visual adapters (Xu et al., 30 Jun 2025); DSTA in RGB-T tracking increases LasHeR precision by +0.9% with <0.3% params (Zeng et al., 2024); DMTrack's STMA+PMCA outperforms previous RGBT trackers by 5–10pp with <1M parameter overhead (Li et al., 3 Aug 2025).
• Time Series Forecasting: TFMAdapter yields a 24–27% MAE reduction over TSFMs on diverse real-world benchmarks, with only 3 calls per input and a single GP regression (Dange et al., 17 Sep 2025).
• EEG Foundation Models: STAMP closes the gap between general TSFMs and purpose-built EEGFMs on clinical tasks, achieving AUROC up to 0.78 with sub-million parameter adapters (Shook et al., 13 Nov 2025).
• Video-Text and QA: MV-Adapter and Tem-Adapter both exceed prior retrieval and VideoQA approaches with marginal parameter and compute additions, leveraging dynamic per-frame temporal modeling (Jin et al., 2023, Chen et al., 2023).
• Plug-and-Play Video Conversation: BT-Adapter enables zero-shot video chat and retrieval, outperforming previous large-scale video chatbots at <0.01× the training compute (Liu et al., 2023).

6. Comparative Analysis with Prior Temporal Adaptation Approaches

Temporal adapters differ significantly from classical full fine-tuning or shallow prompt-based adaption:

• Prompt/adaptor tuning (e.g., AdaptFormer): typically involves only linear projections without temporally-aware operations and is empirically inferior to adapters incorporating explicit temporal modeling (e.g., falls short by ~2–3pp retrieval on MSR-VTT (Jin et al., 2023)).
• Traditional Conv/Attention video backbones: Full fine-tuning of architectures like TimeSformer, XViT, or SlowFast is compute- and storage-prohibitive; temporal adapters enable comparable or superior accuracy with <10% parameter updates (Pan et al., 2022, Gupta et al., 2024).
• Adapters without explicit temporal structure: Static or spatial-only adapters (e.g., vanilla NLP adapters) fail on temporal tasks (e.g., ~20pp drop on SSv2 (Pan et al., 2022)); explicit temporal aggregation is essential.

Adapters leveraging memory (e.g., VMDA), criss-cross structured gating (STAMP), or dynamic calibrated upsampling (MV-Adapter) increasingly narrow the gap with extensive backbone retraining, supporting complex temporal reasoning with minimal compute.

7. Limitations and Future Directions

While temporal adapters are robust and economical, certain challenges and open directions remain:

• Long-Range Global Modeling: Adapters using only local convolutions may underperform on tasks with high long-range temporal dependence. Memory-based, global attention, or hierarchical fusion designs address this partially (VMDA (Xu et al., 30 Jun 2025), LoSA (Gupta et al., 2024)).
• Scalability with Input Size: Gaussian process adapters (TFMAdapter, GP-Adapter (Li et al., 2016)) can face O($n^2$) scaling in time series length; inducing-point or approximation strategies are a plausible future enhancement (Dange et al., 17 Sep 2025).
• Domain Transfer and Meta-Learning: Most adapters are tuned per task/dataset; principled mechanisms for meta-learned or cross-domain adapters remain a topic of future work (Dange et al., 17 Sep 2025).
• Parameter Budget: While per-layer bottlenecks keep costs minimal, in very deep backbones the cumulative overhead may still be non-negligible for edge devices. Selective (layerwise) insertion and bottleneck scaling are effective mitigations (Pan et al., 2022, Pei et al., 2023).
• Extreme Sequence Lengths: For tasks such as lifelong tracking or continuous VideoQA, advanced memory management or hybrid convolution-attention designs may be needed (Xu et al., 30 Jun 2025).

Conclusion

Temporal adapters constitute a foundational paradigm for parameter-, memory-, and computation-efficient temporal modeling in neural architectures. They provide a systematic solution for extending static backbones to temporal and sequential tasks, supporting both local and global context aggregation, robust adaptation across domains, and rapid training under strict resource constraints. By abstracting temporal adaptation into modular, low-cost plug-ins, temporal adapters have become integral to the state of the art in video action localization, tracking, forecasting, medical time series, and video-language understanding across academic and applied settings (Gupta et al., 2024, Pan et al., 2022, Pei et al., 2023, Xu et al., 30 Jun 2025, Jin et al., 2023, Dange et al., 17 Sep 2025, Shook et al., 13 Nov 2025, Li et al., 3 Aug 2025, Liu et al., 2023).