Adaface Sample Computer Vision Questions

Consider a complex object detection scenario where an autonomous vehicle needs to detect pedestrians under challenging environmental conditions. You have access to three sensor modalities: RGB camera, thermal infrared, and depth sensor. Your goal is to design a multi-modal neural network architecture that can effectively fuse features from these different sensor inputs to improve pedestrian detection accuracy, particularly in low-visibility scenarios such as night-time or foggy conditions.
            
            Given the following architectural considerations:
            
            - Input dimensions: RGB (224x224x3), Thermal (224x224x1), Depth (224x224x1)
            - Baseline single-modal mAP (mean Average Precision) for pedestrian detection:
                - RGB: 68.5%
                - Thermal: 72.3%
                - Depth: 59.7%
            
            Which fusion strategy and network architecture would most likely provide the optimal performance improvement and feature representation?

A: Early Fusion CNN with concatenation of raw input channels followed by shared convolutional layers
            B: Late Fusion Ensemble method using separate backbones with weighted majority voting
            C: Squeeze-and-Excitation based fusion with channel-adaptive feature recalibration across modalities
            D: Feature-level fusion with channel-wise adaptive attention and residual skip connections
            E: Parallel multi-stream network with independent feature extractors and a cross-modal transformer fusion mechanism

You trained a Faster R-CNN model on a custom object detection dataset that is both small and imbalanced. Some classes rarely appear, making fair evaluation challenging. You consider: (1) using data augmentation (e.g., random cropping, rotation, photometric distortions) to improve generalization, and (2) adopting class-weighted or specialized metrics to better measure performance on rare classes. Which approach simultaneously enhances generalization to new samples and ensures a more balanced, fair performance assessment across minority classes?

A: Using mAP as the sole evaluation metric
            B: Employing weighted AP metrics to highlight minority classes
            C: Applying random cropping, rotation, and distortions
            D: Increasing anchor sizes only for frequent classes
            E: Using Class Weighted IoU
            F: Combining data augmentation with a class-weighted metric (e.g., Weighted AP)