ColorMNet: A Memory-based Deep Spatial-Temporal Feature Propagation Network for Efficient Video Colorization

Khái niệm cốt lõi

The proposed ColorMNet effectively explores spatial-temporal features for video colorization by: 1) using a large-pretrained visual model to guide the estimation of robust spatial features for each frame, 2) developing a memory-based feature propagation module to adaptively propagate useful features from far-apart frames, and 3) exploiting the similar contents of adjacent frames through a local attention module.

Tóm tắt

The paper presents ColorMNet, a memory-based deep spatial-temporal feature propagation network for efficient video colorization.

Key highlights:

• The large-pretrained visual model guided feature estimation (PVGFE) module extracts robust spatial features from each frame by leveraging the semantic and non-local information from large-pretrained visual models.
• The memory-based feature propagation (MFP) module adaptively propagates useful features from far-apart frames, while reducing GPU memory consumption compared to directly stacking or recurrently propagating features.
• The local attention (LA) module exploits the similar contents of adjacent frames to better utilize the spatial-temporal features.
• Extensive experiments show that ColorMNet outperforms state-of-the-art video colorization methods in terms of colorization quality, temporal consistency, and efficiency.

Thống kê

The size of the test images for measuring the running time is 960 × 536 pixels.
ColorMNet performs favorably against state-of-the-art methods in terms of PSNR, SSIM, FID, and LPIPS on the DAVIS, Videvo, and NVCC2023 validation sets.
The maximum GPU memory consumption of ColorMNet is only 11.2% of DeepRemaster, 10.0% of DeepExemplar, and 5.4% of BiSTNet.
The average running time of ColorMNet is at least 8x faster than the evaluated state-of-the-art methods.
The color distribution consistency index (CDC) of ColorMNet is lower than exemplar-based methods DeepRemaster, DeepExemplar, and BiSTNet on the DAVIS dataset, indicating improved temporal consistency.

Trích dẫn

"To better explore long-range temporal information, several approaches [20,36] develop bidirectional recurrent-based feature propagation methods for video colorization. As the recurrent-based feature propagation treats the features of each frame equally, if the features are not estimated accurately, the errors will accumulate, thus affecting the final video colorization."
"To overcome these problems, we then propose a memory-based feature propagation (MFP) module that can not only adaptively explore and propagate useful features from far-apart frames but also reduce memory consumption."

Thông tin chi tiết chính được chắt lọc từ

ColorMNet

by Yixin Yang,J... lúc arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06251.pdf

Yêu cầu sâu hơn

How can the proposed ColorMNet be extended to handle more diverse exemplar inputs, such as sketches or paintings, for video colorization

To extend the proposed ColorMNet to handle more diverse exemplar inputs, such as sketches or paintings, for video colorization, several modifications can be made. One approach is to incorporate a multi-modal feature extraction module that can process different types of exemplar inputs. This module can consist of separate branches for processing sketches, paintings, or other types of images, allowing the network to extract relevant features from each type of input. Additionally, the network can be trained on a more diverse dataset that includes various types of exemplars to improve its ability to generalize to different input types. By enhancing the feature extraction capabilities and training on a diverse dataset, the ColorMNet can be adapted to handle a wider range of exemplar inputs for video colorization.

What are the potential limitations of the memory-based feature propagation approach, and how can it be further improved to handle even longer video sequences

The memory-based feature propagation approach in ColorMNet may have limitations when handling extremely long video sequences due to memory constraints and potential information loss over time. To address these limitations and improve the handling of longer video sequences, several strategies can be implemented. One approach is to incorporate a mechanism for adaptive memory management, where the network dynamically adjusts the amount of historical information stored based on the complexity of the video sequence. This can help reduce memory consumption while maintaining relevant temporal information. Additionally, implementing a hierarchical memory structure that prioritizes important temporal features can help improve the network's ability to handle longer sequences without accumulating errors. By optimizing the memory management and structure, the memory-based feature propagation approach can be enhanced to handle even longer video sequences more effectively.

Given the strong performance of the large-pretrained visual models in guiding the spatial feature estimation, how can the proposed framework be adapted to leverage the latest advancements in self-supervised visual representation learning for other video enhancement tasks

To leverage the latest advancements in self-supervised visual representation learning for other video enhancement tasks within the proposed framework, the ColorMNet can be adapted to incorporate pre-trained models that have been trained on large-scale self-supervised learning tasks. By integrating these models into the feature extraction process, the network can benefit from the rich visual representations learned through self-supervised learning, enhancing its ability to capture complex visual features in videos. Additionally, the framework can be extended to include self-supervised learning objectives during training, allowing the network to learn more robust and generalized representations for various video enhancement tasks. By integrating the latest advancements in self-supervised visual representation learning, the ColorMNet can further improve its performance on a wide range of video enhancement tasks.