A public account focused on deep learning, computer vision, and autonomous driving perception algorithms, covering visual CV, neural networks, pattern recognition, related hardware and software configurations, and open-source projects.
This article explains the principle of vector embeddings, shows how they turn words, images, audio and other data into dense numeric vectors, compares them with one‑hot encoding, describes static and contextual models, training methods, similarity metrics, and a wide range of real‑world AI applications.
This article reviews five leading AI-driven 3D model generation tools—Tripo AI, Hunyuan3D, Seed3D, Meta SAM 3D, and Trellis 3D—detailing their capabilities, workflows, pricing tiers, and practical use cases, and explains why they are poised to dominate the 2026 market.
This article details how the Qwen‑Video‑8B model, built on Qwen3‑VL‑8B‑Instruct, is fine‑tuned with the LLaMA‑Factory framework using a curated city‑scenery dataset, addresses challenges of domain knowledge, temporal modeling and multimodal fusion, and demonstrates improved video captioning across baseline, English‑fine‑tuned and Chinese‑fine‑tuned versions.
Black Forest Labs released Flux.2 Max, the top‑performing model in the Flux.2 series featuring real‑time context generation, superior texture handling, and strong instruction following, ranking second on the Artificial Analysis leaderboard, with detailed examples, API usage, and pricing information provided.
This article introduces vision‑language models, explains why they outperform OCR‑plus‑LLM pipelines, and walks through practical OCR and information‑extraction tasks using Qwen3‑VL, complete with code snippets, example prompts, result analysis, and a discussion of the model's limitations and resource considerations.
This article explains how Vision Transformers convert images into patch embeddings, compares flattening versus convolutional approaches, discusses position and CLS tokens, analyzes the effect of patch size, explores pixel‑level tokens, and contrasts ViT’s inductive bias with CNNs.
This article walks through the full implementation of a flow‑matching generative model in PyTorch, covering dataset creation, a small MLP that learns a time‑dependent velocity field, the flow‑matching loss, training loop, ODE‑based sampling, visualisation of the learned vector field, and a discussion of the method's limitations and possible extensions.
This article walks through the theory behind flow‑matching generative models, contrasting them with diffusion models, detailing the velocity‑field formulation, training objective, and sampling procedure, and includes visual illustrations of the core concepts.
This article introduces Neural Ordinary Differential Equations, explains their core idea of learning continuous dynamics via a neural derivative function, demonstrates Euler integration, compares naive unfolding with the adjoint method for training, provides a PyTorch implementation, and offers practical tips and extensions such as event handling and physics‑informed models.
This technical article provides a comprehensive comparison of diffusion models and flow matching, covering their intuitive explanations, underlying mathematics, training objectives, sampling efficiency, theoretical guarantees, practical examples, and code implementations to illustrate how each generative approach works.
