Trending Research
SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
To address this, we co-design an inference engine Nunchaku that fuses the kernels of the low-rank branch into those of the low-bit branch to cut off redundant memory access.
FilterNet: Harnessing Frequency Filters for Time Series Forecasting
While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting.
Docling Technical Report
DS4SD/docling
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This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion.
How to Correctly do Semantic Backpropagation on Language-based Agentic Systems
Language-based agentic systems have shown great promise in recent years, transitioning from solving small-scale research problems to being deployed in challenging real-world tasks.
DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting
Recent progress in scene synthesis makes standalone SLAM systems purely based on optimizing hyperprimitives with a Rendering objective possible.
DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding
DINO-X employs the same Transformer-based encoder-decoder architecture as Grounding DINO 1. 5 to pursue an object-level representation for open-world object understanding.
X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models
Extensive experiments validate the model's performance across diverse seen image generation tasks and its capacity to generalize to previously unseen tasks.
MinerU: An Open-Source Solution for Precise Document Content Extraction
Document content analysis has been a crucial research area in computer vision.
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail
We introduce Stereo Anywhere, a novel stereo-matching framework that combines geometric constraints with robust priors from monocular depth Vision Foundation Models (VFMs).
StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows
ag2ai/ag2
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In StateFlow, we distinguish between "process grounding" (via state and state transitions) and "sub-task solving" (through actions within a state), enhancing control and interpretability of the task-solving procedure.
