Trending Research
TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition
Experiments show that models trained on added TextSSR-F data exhibit better accuracy compared to models trained on 4 million existing synthetic data.
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
The labeled data is used to train a reward model that simulates human judgment and serves as an automatic evaluator of the proactiveness of LLM agents.
EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation
Recent work on human animation usually involves audio, pose, or movement maps conditions, thereby achieves vivid animation quality.
SVTRv2: CTC Beats Encoder-Decoder Models in Scene Text Recognition
topdu/openocr
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In this paper, we propose SVTRv2, a CTC model that beats leading EDTRs in both accuracy and inference speed.
OminiControl: Minimal and Universal Control for Diffusion Transformer
In this paper, we introduce OminiControl, a highly versatile and parameter-efficient framework that integrates image conditions into pre-trained Diffusion Transformer (DiT) models.
StableAnimator: High-Quality Identity-Preserving Human Image Animation
Francis-Rings/StableAnimator
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During inference, we propose a novel Hamilton-Jacobi-Bellman (HJB) equation-based optimization to further enhance the face quality.
MinerU: An Open-Source Solution for Precise Document Content Extraction
Document content analysis has been a crucial research area in computer vision.
MARS: Unleashing the Power of Variance Reduction for Training Large Models
AGI-Arena/MARS
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Despite the development of numerous variance reduction algorithms in the past decade aimed at accelerating stochastic optimization in both convex and nonconvex settings, variance reduction has not found widespread success in training deep neural networks or large language models.
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
The Segment Anything Model 2 (SAM 2) has demonstrated strong performance in object segmentation tasks but faces challenges in visual object tracking, particularly when managing crowded scenes with fast-moving or self-occluding objects.
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