百模竞发的 365 天：Hugging Face 年度回顾揭示 VLM 能力曲线与拐点 | Jinqiu Select

Core Insights - The article discusses the rapid evolution of visual language models (VLMs) and highlights the emergence of smaller yet powerful multimodal architectures, showcasing advancements in capabilities such as multimodal reasoning and long video understanding [1][3]. Group 1: New Model Trends - The article introduces the concept of "Any-to-any" models, which can input and output various modalities (images, text, audio) by aligning different modalities [5][6]. - New models like Qwen 2.5 Omni and DeepSeek Janus-Pro-7B exemplify the latest advancements in multimodal capabilities, enabling seamless input and output across different modalities [6][10]. - The trend of smaller, high-performance models (Smol Yet Capable) is gaining traction, promoting local deployment and lightweight applications [7][15]. Group 2: Reasoning Models - Reasoning models are emerging in the VLM space, capable of solving complex problems, with notable examples including Qwen's QVQ-72B-preview and Moonshot AI's Kimi-VL-A3B-Thinking [11][12]. - These models are designed to handle long videos and various document types, showcasing their advanced reasoning capabilities [14]. Group 3: Multimodal Safety Models - The need for multimodal safety models is emphasized, which filter inputs and outputs to prevent harmful content, with Google launching ShieldGemma 2 as a notable example [31][32]. - Meta's Llama Guard 4 is highlighted as a dense multimodal safety model that can filter outputs from visual language models [34]. Group 4: Multimodal Retrieval-Augmented Generation (RAG) - The development of multimodal RAG is discussed, which enhances the retrieval process for complex documents, allowing for better integration of visual and textual data [35][38]. - Two main architectures for multimodal retrieval are introduced: DSE models and ColBERT-like models, each with distinct approaches to processing and returning relevant information [42][44]. Group 5: Multimodal Intelligent Agents - The article highlights the emergence of visual language action models (VLA) that can interact with physical environments, with examples like π0 and GR00T N1 showcasing their capabilities [21][22]. - Recent advancements in intelligent agents, such as ByteDance's UI-TARS-1.5, demonstrate the ability to navigate user interfaces and perform tasks in real-time [47][54]. Group 6: Video Language Models - The challenges of video understanding are addressed, with models like Meta's LongVU and Qwen2.5VL demonstrating advanced capabilities in processing video frames and understanding temporal relationships [55][57]. Group 7: New Benchmark Testing - The article discusses the emergence of new benchmarks like MMT-Bench and MMMU-Pro, aimed at evaluating VLMs across a variety of multimodal tasks [66][67][68].