Object-Level Vision-Radar Alignment for Fine-Grained Detection
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Solution Overview
Problem
Existing multi-modal contrastive learning methods, such as CLIP, struggle with fine-grained classification tasks due to their inability to ground textual concepts to image regions, limiting their performance in object detection and semantic segmentation, especially when applied to radar data which lacks LiDAR sensors and suffers from noisy augmentations.
Innovation Solution
A method that applies multi-modal contrastive learning on radar-image pairs by using object detection models to map bounding boxes to regions of interest, cropping these regions, and passing them through modality-specific encoders to generate object-level embeddings, followed by a loss function to update the encoders' parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame-level contrastive learning is applied to radar-LiDAR pairs, then multi-modal alignment is achieved, but the ability to learn fine-granular features is limited
Solution Approach 1:
The patent segments the frame-level alignment problem into object-level alignment by detecting individual objects in both radar and LiDAR frames. Each detected object becomes a separate learning unit, allowing fine-granular feature learning while maintaining multi-modal alignment. The segmentation is achieved through object detection models that identify and isolate specific objects within the broader frame context.
Solution Approach 2:
The patent applies local quality by focusing the contrastive learning objective on specific object regions rather than the entire frame. By extracting object-level features and applying contrastive loss only to corresponding object pairs across modalities, the method enhances fine-granular feature learning where it matters most, while avoiding noise from irrelevant background regions.
2Adaptability or versatility
If LiDAR-radar pairing is assumed with LiDAR as primary sensor, then multi-modal contrastive learning is enabled, but data collection is limited by LiDAR sensor cost
Solution Approach 1:
The patent inverts the traditional LiDAR-radar hierarchy by treating radar as the primary modality and LiDAR as the supplementary modality. This inversion allows systems without expensive LiDAR sensors to benefit from multi-modal learning, as radar data becomes the foundation and LiDAR data serves as optional enhancement where available.
Solution Approach 2:
The patent enables radar to serve itself as the primary sensing modality, reducing dependency on external LiDAR infrastructure. By designing the contrastive learning framework to work effectively with radar as the main input, the system can collect and leverage radar data independently, expanding training data volume without being constrained by LiDAR sensor availability.
3Loss of information
If CLIP's whole-image to whole-sentence alignment is used, then overall semantic meaning is captured, but object-level correspondence learning is prevented
Solution Approach 1:
The patent segments the whole-image and whole-sentence alignment into object-level alignments by detecting individual objects and their corresponding textual mentions. Each object-detector pair becomes a separate learning unit, preserving the ability to learn object-level correspondence while maintaining the semantic context provided by the full image and sentence.
Solution Approach 2:
The patent adds an object-level dimension to the traditional frame-level alignment by introducing spatial localization information through object detection. This creates a hierarchical learning structure where both global semantic alignment and local object correspondence are learned simultaneously across different levels of granularity.
Data Source
AI summary
A method includes receiving a plurality of paired input images, wherein the paired images includes a first set of images from a first modality and a second set of images from a second modality, outputting a list of bounding boxes and labels in response to running an image-based object detection model, mapping each bounding box to a region of interest that is corresponding to the bounding box and associated with the second set of images, cropping the region of interest from the first and second set of images to generate a cropped first and second set of images, sending the cropped first set of images to a first encoder and a cropped second set of images to a second encoder, wherein the first encoder is configured for the first modality and the second encoder is configured for the second modality, outputting object-level embeddings for both the cropped first and second set of images utilizing encoders, identifying a loss function associated with the images, and in response to when a threshold is met, outputting final updated parameters.