Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Haiwen Diao, Penghao Wu, Hanming Deng +55 authors
Unified vision-language models treat understanding and generation as integrated processes rather than separate tasks, demonstrating strong performance across multiple multimodal capabilities including image synthesis and action reasoning.
Tencent Robotics X, HY Vision Team, Xumin Yu +19 authors
HY-Embodied-0.5 is a foundation model family for embodied agents featuring Mixture-of-Transformers architecture and iterative post-training for enhanced visual perception and reasoning capabilities.
Mustafa Shukor, Dana Aubakirova, Francesco Capuano +11 authors
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
Yukang Chen, Wei Huang, Baifeng Shi +11 authors
A framework scales vision-language models for long video reasoning using reinforcement learning, achieving strong performance on benchmarks and demonstrating consistent gains with increased video frames.
Shihao Wang, Shilong Liu, Yuanguo Kuang +10 authors
Parallel Box Decoding enables efficient and accurate unified visual grounding and detection by decoding geometric elements as atomic units, improving both throughput and localization quality.
Qinsi Wang, Bo Liu, Tianyi Zhou +6 authors
Vision-Zero is a domain-agnostic framework that enhances vision-language models through self-improvement in competitive visual games, using Iterative Self-Play Policy Optimization and achieving state-of-the-art performance without human annotation.
Aditi, Niket Agarwal, Arslan Ali +288 authors
Cosmos 3 is an omnimodal world model that processes and generates multiple data types through a unified mixture-of-transformers architecture, achieving state-of-the-art performance in various understanding and generation tasks.
Hejun Dong, Junbo Niu, Bin Wang +3 authors
MinerU-Diffusion is a diffusion-based framework that replaces autoregressive decoding with parallel diffusion denoising for document OCR, improving robustness and decoding speed.
Yanzhe Chen, Zechen Bai, Zhijun Cao +7 authors
Show-Harness links vision-language models to robot control via discrete semantic actions interpreted by embodiment-specific modules, enabling zero-shot and efficient fine-tuned deployment across robots and GUIs.
Hugo Laurençon, Andrés Marafioti, Victor Sanh +1 authors
A comprehensive tutorial on building vision-language models, detailing the development of Idefics3-8B with an improved dataset enhancing document understanding.
Weijie Wang, Xiaoxuan He, Youping Gu +9 authors
World-R1 framework improves video generation by incorporating 3D constraints through reinforcement learning and specialized text datasets while maintaining visual quality and scalability.
Senqiao Yang, Yukang Chen, Zhuotao Tian +4 authors
VisionZip reduces visual token redundancy and enhances efficiency in vision-language models, improving performance and inference speed across various tasks.
Xinlei Yu, Gen Li, Qingyi Si +13 authors
DOPD addresses privilege illusion in on-policy distillation by dynamically routing token-level supervision between teacher and student policies based on advantage gaps and probabilities, improving capability transfer in large and vision-language models.
Zhaoyang Liu, JingJing Xie, Zichen Ding +18 authors
ScaleCUA, a large-scale dataset and model for computer use agents, achieves state-of-the-art performance across multiple platforms and tasks by leveraging data-driven scaling.
Seokju Cho, Ryo Hachiuma, Abhishek Badki +8 authors
SpatialClaw is a training-free framework that uses code as an action interface to enable flexible, stateful spatial reasoning in vision-language models, achieving superior performance across diverse 3D/4D spatial reasoning tasks.
Wenqi Zhang, Hang Zhang, Xin Li +6 authors
A new high-quality instructional video-based corpus improves VLM pretraining through more coherent context and richer knowledge, enhancing performance in knowledge-intensive tasks.
Nikita Kachaev, Mikhail Kolosov, Daniil Zelezetsky +2 authors
Systematic study reveals that naive action fine-tuning degrades visual representations in Vision-Language-Action models, but targeted strategies can mitigate this and improve generalization.
Hugo Laurençon, Léo Tronchon, Matthieu Cord +1 authors
Idefics2, a vision-language model with 8 billion parameters, achieves state-of-the-art performance on multimodal benchmarks through extensive experimental validation.
Minki Kang, Shizhe Diao, Ryo Hachiuma +4 authors
Agents using vision-language models with extended reasoning face challenges in tool utilization, which are addressed through AXPO, a method that improves performance by optimizing thinking prefixes and tool call resampling.
Nimrod Shabtay, Moshe Kimhi, Artem Spector +5 authors
AwaRes is a spatial-on-demand framework for vision-language models that dynamically retrieves high-resolution image segments based on query needs, using tool-calling and multi-turn reinforcement learning with composite rewards.
北京市昌平区探索星信息技术及软件开发工作室
京ICP备2026059466号