Search Results for author:
Found 16 papers, 11 papers with code
VideoAuteur: Towards Long Narrative Video Generation
We further introduce a Long Narrative Video Director to enhance both visual and semantic coherence in generated videos and emphasize the role of aligning visual embeddings to achieve improved overall video quality.
GenEx: Generating an Explorable World
In this work, we take a step toward this goal by introducing GenEx, a system capable of planning complex embodied world exploration, guided by its generative imagination that forms priors (expectations) about the surrounding environments.
What If We Recaption Billions of Web Images with LLaMA-3?
For discriminative models like CLIP, we observe enhanced zero-shot performance in cross-modal retrieval tasks.
Ranked #127 on
Visual Question Answering
on MM-Vet
Medical Vision Generalist: Unifying Medical Imaging Tasks in Context
This study presents Medical Vision Generalist (MVG), the first foundation model capable of handling various medical imaging tasks -- such as cross-modal synthesis, image segmentation, denoising, and inpainting -- within a unified image-to-image generation framework.
PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter
Our research undertakes a thorough exploration of the state-of-the-art perceiver resampler architecture and builds a strong baseline.
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties
Instead of relying solely on category-specific annotations, ProLab uses descriptive properties grounded in common sense knowledge for supervising segmentation models.
Rejuvenating image-GPT as Strong Visual Representation Learners
This paper enhances image-GPT (iGPT), one of the pioneering works that introduce autoregressive pretraining to predict the next pixels for visual representation learning.
Label-Free Liver Tumor Segmentation
We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans.
Ranked #1 on
Tumor Segmentation
on LiTS17
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.
Ranked #1 on
Organ Segmentation
on BTCV
AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation
Through systematic analysis, we found that the commonly used pairwise affinity loss has two limitations: (1) it works with color affinity but leads to inferior performance with other modalities such as depth gradient, (2)the original affinity loss does not prevent trivial predictions as intended but actually accelerates this process due to the affinity loss term being symmetric.
Synthetic Tumors Make AI Segment Tumors Better
We develop a novel strategy to generate synthetic tumors.
Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification
We hope that this study can direct future research on the application of Transformers to a larger variety of medical imaging tasks.
1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track
The model is trained on a composite dataset consisting of images from 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, WildDash 2, IDD, BDD, and COCO) with a simple dataset balancing strategy.
Masked Autoencoders Enable Efficient Knowledge Distillers
For example, by distilling the knowledge from an MAE pre-trained ViT-L into a ViT-B, our method achieves 84. 0% ImageNet top-1 accuracy, outperforming the baseline of directly distilling a fine-tuned ViT-L by 1. 2%.
Learning from Temporal Gradient for Semi-supervised Action Recognition
Our method achieves the state-of-the-art performance on three video action recognition benchmarks (i. e., Kinetics-400, UCF-101, and HMDB-51) under several typical semi-supervised settings (i. e., different ratios of labeled data).
CateNorm: Categorical Normalization for Robust Medical Image Segmentation
We propose a new normalization strategy, named categorical normalization (CateNorm), to normalize the activations according to categorical statistics.
