The field of visual recognition and enhancement is moving towards more efficient and accurate models, with a focus on real-time performance and limited data requirements. Researchers are exploring new approaches to sign language recognition, salient object detection, and image enhancement, leveraging techniques such as transformer-based architectures, convolutional neural networks, and attention mechanisms. Notable papers in this area include: SignBart, which presents a novel approach to sign language recognition using an encoder-decoder architecture, achieving 96.04% accuracy on the LSA-64 dataset. Oneta, which proposes a multi-style image enhancement model using eigentransformation functions, demonstrating high performance on six enhancement tasks across 30 datasets. Rapid Salient Object Detection with Difference Convolutional Neural Networks, which introduces an efficient network design for salient object detection, achieving significant improvements in efficiency-accuracy trade-offs. MobileIE, which presents an extremely lightweight ConvNet for real-time image enhancement on mobile devices, achieving real-time inference at up to 1,100 frames per second.