Search Results for author:
Found 20 papers, 9 papers with code
Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM
Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality of output videos.
OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization
To tackle these issues, we introduce OnlineVPO, a more efficient preference learning approach tailored specifically for video diffusion models.
IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language Model
The rapid advancement of Large Vision-Language models (LVLMs) has demonstrated a spectrum of emergent capabilities.
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning
The rapid development of diffusion models has triggered diverse applications.
Magic Clothing: Controllable Garment-Driven Image Synthesis
We propose Magic Clothing, a latent diffusion model (LDM)-based network architecture for an unexplored garment-driven image synthesis task.
OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on
We present OOTDiffusion, a novel network architecture for realistic and controllable image-based virtual try-on (VTON).
ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling
By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities.
DiffusionGPT: LLM-Driven Text-to-Image Generation System
Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on open-source platforms.
AffordanceLLM: Grounding Affordance from Vision Language Models
Affordance grounding refers to the task of finding the area of an object with which one can interact.
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search
Moreover, we propose a proximity data generation (PDG) module to automatically produce more diverse data for cross-modal training.
Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning
Recent advances in text-to-image (T2I) diffusion models have enabled impressive image generation capabilities guided by text prompts.
MSRL: Distributed Reinforcement Learning with Dataflow Fragments
Yet, current distributed RL systems tie the definition of RL algorithms to their distributed execution: they hard-code particular distribution strategies and only accelerate specific parts of the computation (e. g. policy network updates) on GPU workers.
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
We hope that this project will be the foundation of Chinese cognitive intelligence.
Humble Teachers Teach Better Students for Semi-Supervised Object Detection
We propose a semi-supervised approach for contemporary object detectors following the teacher-student dual model framework.
HYPE-C: Evaluating Image Completion Models Through Standardized Crowdsourcing
Recent work has proposed the use of human evaluation for image synthesis models, allowing for a reliable method to evaluate the visual quality of generated images.
OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
Single-view 3D is the task of recovering 3D properties such as depth and surface normals from a single image.
Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control
We experimentally demonstrate the strength of our approach over different non-hierarchical and hierarchical baselines.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+4
Learning Single-Image Depth from Videos using Quality Assessment Networks
Depth estimation from a single image in the wild remains a challenging problem.
Surface Normals in the Wild
We study the problem of single-image depth estimation for images in the wild.
Single-Image Depth Perception in the Wild
This paper studies single-image depth perception in the wild, i. e., recovering depth from a single image taken in unconstrained settings.
