Most visual models are designed for sRGB images, yet RAW data offers significant advantages for object detection by preserving sensor information before ISP processing. This enables improved detection accuracy and more efficient hardware designs by bypassing the ISP. However, RAW object detection is challenging due to limited training data, unbalanced pixel distributions, and sensor noise. To address this, we propose SimROD, a lightweight and effective approach for RAW object detection. We introduce a Global Gamma Enhancement (GGE) module, which applies a learnable global gamma transformation with only four parameters, improving feature representation while keeping the model efficient. Additionally, we leverage the green channel's richer signal to enhance local details, aligning with the human eye’s sensitivity and Bayer filter design. Extensive experiments on multiple RAW object detection datasets and detectors demonstrate that SimROD outperforms state-of-the-art methods like RAW-Adapter and DIAP while maintaining efficiency. Our work highlights the potential of RAW data for real-world object detection.

Supervised crowd counting relies on manual labeling, which is costly and time-consuming. This led to an increased interest in unsupervised methods. However, there is a significant domain gap issue in unsupervised methods, which is manifested by a model trained on one dataset serving dramatic performance drops when being transferred to another. This phenomenon can be attributed to the diverse domain knowledge making it difficult for the unsupervised models to transfer between general (e.g., similar distribution) and domain-specific (e.g., unique density, perspective, illumination, etc.) knowledge, leading to knowledge bias. Existing methods focus on exploring distinguishable relationships and establishing connections between the source and target domains. However, the similar knowledge transfer cannot perfectly simulate the contents of the target domain, leading to the model's inability to generalize to domain-specific knowledge. In this paper, we propose a Self-awareness Knowledge Diffusion method (SaKnD) that leverages the self-knowledge without establishing cross-domain knowledge relationships, which aims to balance the knowledge bias between general and domain-specific knowledge. Specifically, we propose a strategy to evaluate the uncertainty and consistency to define the clueless and informed areas, which determine the location and orientation of knowledge diffusion. These clueless areas serve as domain-specific knowledge that needs to be optimized, and these informed areas serve as general knowledge across domains. Extensive experiments on three standard crowd-counting benchmarks, ShanghaiTech PartA, ShanghaiTech PartB, and UCF_QNRF, show that the proposed SaKnD achieves state-of-the-art performance.