Dynamic Global-Local Memory (DGLM)

• Dynamic Global-Local Memory is an architectural paradigm that partitions memory into global and local components to capture long-range coherence and fine-grained details.
• It uses dynamic, attention-based fusion to integrate compressed historical features with recent local cues, addressing shortcomings of traditional models.
• Empirical results show that DGLM improves performance in tasks such as video colorization, ensuring consistent attribute retention over extended sequences.

Dynamic Global-Local Memory (DGLM) is a paradigm and set of architectural principles that enable neural models to dynamically integrate both global (long-range, all-history or holistic) and local (short-range, segmental or neighborhood) contextual information through explicit memory mechanisms. DGLM modules are designed to enhance consistency, stability, and context sensitivity in tasks requiring both fine local detail and global coherence, particularly in domains such as video generation, sequence modeling, structured vision, and real-time perception.

1. Fundamental Principles and Motivation

Dynamic Global-Local Memory arises from the observation that many complex learning tasks require simultaneous access to both locally relevant and globally consistent features across space or time. Traditional models—such as convolutional neural networks, RNNs, or attention-based Transformers—may emphasize either local receptive fields or, with adaptations, global interactions, but typically struggle to maintain long-term coherence as errors accumulate over lengthy sequences or large spatial extents.

The DGLM design principle is to explicitly partition memory into local and global components, each dynamically queried and updated as the model processes an input. This ensures that local processing (e.g., smoothing transitions between adjacent segments, capturing high-frequency changes) can benefit from, and be regulated by, global memory (e.g., enforcing long-term consistency, preserving low-frequency attributes).

2. Core Architectural Elements

A canonical DGLM architecture incorporates the following elements:

• Local Memory ($\mathcal{V}_l$): Stores features from a recent window or segment (e.g., a sliding window of recent frames or adjacent patches). Enables modeling of short-range dependencies and transition smoothness.
• Global Memory ($\mathcal{V}_g$): Aggregates features from all or a selected subset of the entire prior context (e.g., all generated frames in a video, memory tokens representing full-sequence context in Transformers), capturing global, low-frequency, or rare attributes.
• Dynamic Feature Compression and Selection: Since naïvely storing all history is inefficient, DGLM architectures compress global memory using specialized models (e.g., long video understanding models) and select representative frames or states by measuring semantic diversity and informativeness, often employing criteria such as model uncertainty (entropy) and dissimilarity metrics.
• Attention-Based Fusion: Both global and local memory features are projected and fused using multi-head cross-attention mechanisms, allowing the model to adaptively retrieve and combine relevant context at each generation or recognition step.

A typical mathematical instantiation for generation tasks is: $$\text{Attention}^m = \text{Softmax} \left( \frac{\boldsymbol{Q} \cdot \boldsymbol{K}}{\sqrt{d}} \right) \cdot \boldsymbol{V}$$ with separate keys and values for global and local memory.

3. Integration in Frameworks: Example of LongAnimation

In the "LongAnimation: Long Animation Generation with Dynamic Global-Local Memory" framework (2507.01945), DGLM is realized to address long-term colorization consistency in video generation. The core module operates as follows:

• Historical Feature Compression: Uses a long video understanding model (Video-XL) to encode past frames into compact key-value caches. Frames are grouped into segments based on feature change detected via pretrained vision models, allowing adaptive temporal granularity.
• Multi-Layer Feature Utilization: Empirically, mid-level layers from multimodal language-vision models encode global scene and color features more effectively than top layers. Therefore, DGLM extracts and fuses features at multiple intermediary levels.
• Dynamic Attention Fusion: For each generation step, fused global and local features are retrieved by cross-attention and injected into the video generation backbone, modulating the output to ensure both immediate and long-range consistency.
• Color Consistency Reward: During training, a non-gradient reward aligns generated color features with those in reference animations at the global context level, further reinforcing temporal coherence.

The DGLM is integrated with modules such as SketchDiT (for hybrid text-image-sketch conditioning) and is critical for overcoming deficiencies in purely local paradigms, which often fail to preserve global coherence as generation proceeds over hundreds of frames.

4. Empirical Performance and Analysis

Extensive evaluation on long and short animation video colorization benchmarks demonstrates the efficacy of DGLM:

• Quantitative Improvements: On long-term evaluation (average 500 frames), DGLM-equipped LongAnimation achieves substantial improvements in perceptual and video quality metrics (e.g., LPIPS, FVD) compared to prior methods, especially in scenarios requiring consistency beyond local overlap length.
• Ablation Results: Removal of DGLM results in performance drops in metrics sensitive to long-range consistency (e.g., substantial increases in LPIPS and FVD). Further gains are realized by coupling DGLM with explicit color consistency rewards.
• Qualitative Observations: Visualizations confirm the maintenance of object color identity and scene consistency over hundreds of frames, where baseline models exhibit color drift, flicker, and mode collapse.

These results indicate that DGLM's integration of global and local context is specifically beneficial to tasks where persistent attribute retention and recovery from local errors are required.

5. Comparative Perspective with Prior Paradigms

DGLM contrasts with local-only or windowed paradigms (e.g., overlapping feature fusion) by explicitly maintaining a holistic, dynamically updated memory of all relevant historical context. A summary comparison is provided in the following table:

Feature	DGLM	Prior Local Paradigm
Temporal Scope	Global (all history) + Local	Local only (adjacent overlaps)
Feature Extraction	Dynamic, multi-level, adaptive	Fixed, shallow
Fusion Mechanism	Multi-layer cross-attention	Simple concatenation/addition
Color/Attribute Consistency	Maintains across long sequences	Prone to long-horizon drift
Extensibility	General to other cues/tasks	Limited

This framework suggests that DGLM can be adapted to other modalities (e.g., depth, segmentation), and other domains where cross-temporal consistency is required.

6. Extensions and Broader Implications

The DGLM paradigm has implications extending beyond the specific domain of animation video colorization:

• Video Object Detection and Tracking: DGLM-inspired architectures (e.g., MEGA, DMNet) have demonstrated performance gains by integrating local aggregation with long-range global memory, leading to more robust detection under occlusion and challenging temporal dynamics.
• Sequence Modeling in NLP: The GMAT architecture augments sparse Transformers with a global memory bank, enabling efficient global reasoning and high-quality compression, consistent with DGLM principles.
• Dialogue Systems: The GLMP model leverages global and local memory pointers to improve copy accuracy and robustness to out-of-vocabulary entities in task-oriented dialogue.
• General Structured Prediction: DGLM suggests that rich, context- and task-specific memory mechanisms can outperform standard windowed or convolutional approaches for structured prediction, especially under data regimes requiring long-horizon coherence.

A plausible implication is that future research will further generalize DGLM, using dynamically learned or adaptively constructed memory hierarchies, with application to reinforcement learning, spatiotemporal reasoning, and real-time control systems.

7. Key Innovations and Future Directions

Notable innovations in DGLM as implemented in LongAnimation and related models include:

• Dynamic, Content-Adaptive Global Memory: Historic context is not merely accumulated but filtered and compressed based on semantic relevance and prediction certainty.
• Layer-wise Multi-Level Fusion: Utilizing multiple intermediate feature levels yields richer, more temporally robust representations than shallow or final layer-only methods.
• Unified Attention Fusion: Joint cross-attention over concatenated global and local memories enables flexible, learnable context integration, mitigating the limitations of fixed heuristics.

Future research directions include extending DGLM to additional modalities, exploring hierarchical or multi-scale memory architectures, and optimizing for efficiency in ultra-long sequence scenarios. This suggests DGLM will continue to shape the design of models addressing the challenge of long-term consistency in dynamic, structured prediction domains.