Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception (2312.09812v1)

Abstract: Understanding vehicles in images is important for various applications such as intelligent transportation and self-driving system. Existing vehicle-centric works typically pre-train models on large-scale classification datasets and then fine-tune them for specific downstream tasks. However, they neglect the specific characteristics of vehicle perception in different tasks and might thus lead to sub-optimal performance. To address this issue, we propose a novel vehicle-centric pre-training framework called VehicleMAE, which incorporates the structural information including the spatial structure from vehicle profile information and the semantic structure from informative high-level natural language descriptions for effective masked vehicle appearance reconstruction. To be specific, we explicitly extract the sketch lines of vehicles as a form of the spatial structure to guide vehicle reconstruction. The more comprehensive knowledge distilled from the CLIP big model based on the similarity between the paired/unpaired vehicle image-text sample is further taken into consideration to help achieve a better understanding of vehicles. A large-scale dataset is built to pre-train our model, termed Autobot1M, which contains about 1M vehicle images and 12693 text information. Extensive experiments on four vehicle-based downstream tasks fully validated the effectiveness of our VehicleMAE. The source code and pre-trained models will be released at https://github.com/Event-AHU/VehicleMAE.

• The paper introduces VehicleMAE, a novel framework that integrates structural and semantic cues with masked autoencoders to enhance vehicle-centric perception.
• It leverages a large-scale dataset (Autobot1M) to achieve remarkable performance in tasks such as vehicle attribute recognition (92.21% mA) and re-identification (85.6% mAP).
• The research advances autonomous driving by combining geometric edge detection and CLIP-based semantic alignment to deliver robust, multimodal feature learning.

Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception

The paper, titled "Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception," introduces a novel framework designed to enhance vehicle perception models through a specialized pre-training process. This approach, termed VehicleMAE, addresses existing limitations in vehicle-centric autonomous systems by incorporating both structural and semantic information into the model's training phase.

Framework Overview

VehicleMAE is a pre-training framework that utilizes masked autoencoders (MAE) to improve the representation of vehicle images by integrating spatial and semantic structures. It innovatively leverages vehicle profile information and high-level natural language descriptions to facilitate effective reconstruction of masked sections in vehicle images. The approach surpasses typical pre-training methodologies by specifically tailoring to the unique dynamics present in vehicular perception tasks.

Key Components

The VehicleMAE framework comprises three principal modules:

1. Masked Auto-Encoder Module: The core of the model, designed to reconstruct the appearance of vehicles by masking a significant proportion of the image. The encoder-decoder setup allows it to learn robust features.
2. Structural Prior Module: This module extracts vehicle contours using edge detection techniques, which guide the reconstruction process to focus on geometric aspects inherent to vehicles.
3. Semantic Prior Module: By utilizing the CLIP model, this module aligns visual representations with textual descriptions, allowing the model to infer high-level semantic insights, thereby enriching the projection space of the learned embeddings.

Dataset and Experiments

The authors introduced a substantial dataset, Autobot1M, incorporating approximately one million vehicle images alongside 12,693 textual descriptions. This dataset provides a diverse set of scenarios, enhancing the model's ability to generalize across multiple vehicular contexts. The model's efficacy was validated through extensive experiments encompassing four downstream tasks: vehicle attribute recognition, re-identification, fine-grained classification, and part segmentation.

• Vehicle Attribute Recognition: The framework achieved a mean Accuracy (mA) of 92.21% on the VeRi dataset, markedly superior to models pre-trained on the ImageNet dataset.
• Vehicle Re-identification: It demonstrated a mean Average Precision (mAP) of 85.6%, outperforming standard MAE approaches.
• Fine-grained Classification and Part Segmentation: VehicleMAE achieved compelling results, validating its superior feature learning capability.

Implications and Future Directions

The implications of this research are twofold:

1. Practical Implications: Enhanced autonomous vehicle perception and intelligence can drastically improve real-world applications in smart cities and transportation systems. The ability to leverage multimodal data gives the model an edge in environments fraught with occlusions, varied lighting, and changing dynamics.
2. Theoretical Contributions: By marrying structural layout and semantic context, the research pushes the boundaries of transformer-based pre-training, extending the utility of MAEs beyond traditional object recognition tasks.

Future research in this domain should focus on broader multi-modal integrations and explore deeper cross-modal interactions to further elevate the perception capabilities of AI systems in autonomous driving scenarios. Additionally, extending this framework to other applications within intelligent transportation can yield comprehensive solutions in vehicle and traffic management systems.