The recent research in the field has seen significant advancements aimed at enhancing user interaction and experience, particularly for individuals with vision deficiencies. Innovations in web extensions have led to the development of tools that personalize color adjustments and selective zoom features, significantly improving color perception for those with color blindness. In the realm of object detection, there has been a notable shift towards more generalized approaches, addressing the limitations of traditional datasets by introducing benchmarks that support the simultaneous detection of salient and camouflaged objects in unconstrained scenes. This has led to the creation of new models that decouple attribute distinction from mask reconstruction, enhancing the ability to differentiate between these objects. Additionally, advancements in interactive colorization techniques have introduced region-specific control mechanisms, mitigating issues of color collapse through the use of lasso tools. These developments not only improve the precision of colorization but also enhance user efficiency. Furthermore, the field has witnessed the introduction of large-scale datasets for alignment-free RGB-Thermal salient object detection, accompanied by new networks that model inter- and intra-modal correlations for accurate predictions in unaligned image pairs. Lastly, there is a growing interest in exploring alternative color spaces for unsupervised object detection, with studies demonstrating improved performance when leveraging color channel independence. These recent trends collectively push the boundaries of current technologies, offering more robust and user-friendly solutions across various applications.