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。
Xuyang Liu, Shihao Wang, Xinyu Wei +5 authors
OmniCompass is a holistic benchmark for omni-modal video grounding and understanding that evaluates vision-language and audio-visual reasoning through temporal grounding, spatial grounding, and question answering tasks.
Zhe Qian, Ziyang Gong, Zhongxing Xu +9 authors
LoopVL extends Loop Transformers to vision-language modeling by iteratively updating a shared multimodal state, improving reasoning and revealing dynamic visual attention shifts.
Luozheng Qin, Yuhan Zhu, Changhai Zhou +9 authors
MJev-Spatial is a fast multimodal decision model that unifies spatial reasoning tasks into a single-pass classification framework, improving speed and accuracy over autoregressive vision-language models.
Qingchuan Ma, Yuexiao Ma, Xiang Meng +7 authors
Vision-language models struggle to convert 2D visual observations into precise 3D rotational control, showing large gaps versus humans in projection alignment tasks.
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.
Zhen Wang, Changpeng Wang, Zhe Liu +5 authors
PanoVLN improves vision-and-language navigation by using panoramic observations with longer-horizon action planning, targeted branching-route supervision, and combined semantic-geometric features to achieve state-of-the-art results.
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.
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.
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.
Yijiang Li, Bingyang Wang, Nuno Vasconcelos
ACT3D trains vision-language models via reinforcement learning to actively explore 3D environments from single first-person views, improving answer accuracy through adaptive, question-conditioned navigation without direct exploration supervision.
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.
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.
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.
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.
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.
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