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Found 14 papers, 9 papers with code
The devil is in the object boundary: towards annotation-free instance segmentation using Foundation Models
Foundation models, pre-trained on a large amount of data have demonstrated impressive zero-shot capabilities in various downstream tasks.
Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator
Text-to-video is a rapidly growing research area that aims to generate a semantic, identical, and temporal coherence sequence of frames that accurately align with the input text prompt.
EdaDet: Open-Vocabulary Object Detection Using Early Dense Alignment
Vision-language models such as CLIP have boosted the performance of open-vocabulary object detection, where the detector is trained on base categories but required to detect novel categories.
LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models
Prompt engineering is a powerful tool used to enhance the performance of pre-trained models on downstream tasks.
Contrastive Grouping with Transformer for Referring Image Segmentation
Referring image segmentation aims to segment the target referent in an image conditioning on a natural language expression.
High-Rate Phase Association with Travel Time Neural Fields
Existing techniques relying on coarsely approximated, fixed wave speed models fail in this unexplored dense regime where the complexity of unknown wave speed cannot be ignored.
A Graph Dynamics Prior for Relational Inference
In this work, we propose a \textit{graph dynamics prior} (GDP) for relational inference.
Homophily modulates double descent generalization in graph convolution networks
Motivated by experimental observations of ``transductive'' double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model.
Manifold Rewiring for Unlabeled Imaging
We illustrate our graph denoising framework on regular synthetic graphs and then apply it to single-particle cryo-EM where the measurements are corrupted by very high levels of noise.
Neural Link Prediction with Walk Pooling
Instead of extracting transition probabilities from the original graph, it computes the transition matrix of a "predictive" latent graph by applying attention to learned features; this may be interpreted as feature-sensitive topology fingerprinting.
Ranked #1 on
Link Prediction
on Pubmed
A Scalable AI Approach for Clinical Trial Cohort Optimization
FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria.
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents.
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
With the help of novel masks or scenes, we enhance the current datasets using synthesized shadow images.
Ranked #2 on
Shadow Removal
on ISTD
Weighted Community Detection and Data Clustering Using Message Passing
In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms.
