Object detection / segmentation, human pose (2D/3D), 3D human motion tracking and modeling, multi-object tracking, re-identification and metric learning, action recognition, OCR, handwriting, face and biometrics, open-vocabulary recognition, 3D geometry and vision-language-action models, autonomous driving, epipolar geometry, triangulation, SLAM, PnP, bundle adjustment, structure-from-motion, 3D reconstruction (meshes, NeRFs, Gaussian splatting, point clouds), depth/normal/optical flow estimation, 3D scene flow, recovering material properties, inverse rendering, differentiable rendering, camera calibration, sensor fusion, IMUs, LiDAR, birds eye view perception. Generative modeling, text-to-image diffusion, video generation and editing, question answering, un- and self-supervised representation learning (contrastive, masked modeling), semi/weak supervision, few-shot and meta-learning, domain adaptation, continual learning, active learning, synthetic data, test-time augmentation strategies, low-level image processing and computational photography, event cameras, denoising, deblurring, super-resolution, frame-interpolation, dehazing, HDR, color calibration, medical imaging, remote sensing, industrial inspection, edge deployment, quantization, distillation, pruning, architecture search, auto-ML, distributed training, inference systems, evaluation/benchmarking, metric design, explainability etc.
You can't put all that into a single generic textbook.