Search Results for author:
Found 15 papers, 7 papers with code
LayoutDiT: Exploring Content-Graphic Balance in Layout Generation with Diffusion Transformer
To address these challenges, we introduce LayoutDiT, an effective framework that balances content and graphic features to generate high-quality, visually appealing layouts.
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
We try to narrow the gap by mining the potential of VLMs for better performance and any-to-any workflow from three aspects, i. e., high-resolution visual tokens, high-quality data, and VLM-guided generation.
DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving
We utilize the challenging driving scenarios from the CARLA leaderboard 2. 0, which involve high-speed driving and lane-changing, and propose a rule-based expert policy to control the vehicle and generate ground truth labels for its reasoning process across different driving aspects and the final decisions.
DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation
However, many of these works face challenges in identifying correct output modalities and generating coherent images accordingly as the number of output modalities increases and the conversations go deeper.
A Survey of Reasoning with Foundation Models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
Mask-Attention-Free Transformer for 3D Instance Segmentation
Therefore, we abandon the mask attention design and resort to an auxiliary center regression task instead.
DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation
Recent Diffusion Transformers (e. g., DiT) have demonstrated their powerful effectiveness in generating high-quality 2D images.
Ranked #1 on
Point Cloud Generation
on ShapeNet Car
TriVol: Point Cloud Rendering via Triple Volumes
However, artifacts still appear in rendered images, due to the challenges in extracting continuous and discriminative 3D features from point clouds.
Command-Driven Articulated Object Understanding and Manipulation
The key of Cart is to utilize the prediction of object structures to connect visual observations with user commands for effective manipulations.
TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation
We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation.
ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation
Separating 3D point clouds into individual instances is an important task for 3D vision.
Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation
In a unified framework, we jointly predict the feasible 6-DoF grasp poses, instance semantic segmentation, and collision information.
Scale-aware Automatic Augmentation for Object Detection
We propose Scale-aware AutoAug to learn data augmentation policies for object detection.
Reinforcement Learning for the Beginning of Starcraft II Game
In this project, we plan to develop a reinforcement learning model for the beginning of Starcraft II game, instead of the full-length game.
Vehicle Re-identification with Viewpoint-aware Metric Learning
This paper considers vehicle re-identification (re-ID) problem.
