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Found 9 papers, 5 papers with code
CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion
Recent advancements in text-to-image generative systems have been largely driven by diffusion models.
CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations
Vision-Language Models (VLMs) have demonstrated their widespread viability thanks to extensive training in aligning visual instructions to answers.
PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields
Through extensive experiments, we demonstrate the effectiveness of PlanarNeRF in various scenarios and remarkable improvement over existing works.
CogAgent: A Visual Language Model for GUI Agents
People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e. g., computer or smartphone screens.
Ranked #15 on
Visual Question Answering
on MM-Vet
CogVLM: Visual Expert for Pretrained Language Models
We introduce CogVLM, a powerful open-source visual language foundation model.
Ranked #4 on
Visual Question Answering (VQA)
on InfiMM-Eval
ViLTA: Enhancing Vision-Language Pre-training through Textual Augmentation
Vision-language pre-training (VLP) methods are blossoming recently, and its crucial goal is to jointly learn visual and textual features via a transformer-based architecture, demonstrating promising improvements on a variety of vision-language tasks.
EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systems
Our method incorporates an Error-state Kalman Filter (ESKF) to estimate gyroscope bias and correct rotation estimates from monocular SLAM, overcoming dependence on pure monocular SLAM for rotation estimation.
Real-Time Dense 3D Mapping of Underwater Environments
To address this problem, we propose to use SVIn2, a robust VIO method, together with a real-time 3D reconstruction pipeline.
Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation
We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching.
