Online Data Curation for Object Detection via Marginal Contributions to Dataset-level Average Precision

The study benchmarks various deep learning-based object detection models using the MobilTelesco dataset, which features sparse astrophotography images. Traditional datasets like ImageNet and COCO focus on everyday objects, lacking the unique challenges presented by feature-deficient conditions. The research highlights the difficulties these models face when applied to non-commercial domains, emphasizing the need for specialized datasets in astrophotography.

The paper titled 'RSPose: Ranking Based Losses for Human Pose Estimation' addresses limitations in heatmap-based human pose estimation methods. It identifies three main issues: the ineffectiveness of Mean Squared Error (MSE) Loss in improving joint localization, spatial and class-wise imbalances in heatmaps, and a mismatch between evaluation metrics and loss functions. The authors propose ranking-based losses as a solution, demonstrating both theoretically and empirically that these new losses outperform traditional heatmap losses, enhancing the correlation between confidence scores and locali…

The paper introduces MCAQ-YOLO, a novel morphological complexity-aware quantization framework designed for efficient object detection. Unlike traditional methods that apply uniform bit precision, MCAQ-YOLO utilizes five morphological metrics to assess local visual complexity and adaptively allocate bit precision. This approach enhances quantization sensitivity and includes a curriculum-based training scheme to progressively increase quantization difficulty, leading to improved optimization and convergence in neural networks.

The article discusses advancements in fine-tuning Vision-Language Models (VLMs) to enhance spatial reasoning. Traditional methods often suffer from biases and errors due to imbalanced data collection and annotation from real-world scenes. To overcome these issues, the authors propose a redesigned fine-tuning process that includes controlled data generation and annotation, ensuring quality and balance. This approach involves comprehensive sampling of object attributes and aims to improve the transferability of VLMs to real-world applications.