Search Results for author:
Found 11 papers, 8 papers with code
Empowering Biomedical Discovery with AI Agents
We envision 'AI scientists' as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate machine learning tools with experimental platforms.
UniTS: Building a Unified Time Series Model
However, current foundation models apply to sequence data but not to time series, which present unique challenges due to the inherent diverse and multidomain time series datasets, diverging task specifications across forecasting, classification and other types of tasks, and the apparent need for task-specialized models.
A Decoupled Spatio-Temporal Framework for Skeleton-based Action Segmentation
Effectively modeling discriminative spatio-temporal information is essential for segmenting activities in long action sequences.
Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus is suitable for LoT study.
ChatAnything: Facetime Chat with LLM-Enhanced Personas
For MoV, we utilize the text-to-speech (TTS) algorithms with a variety of pre-defined tones and select the most matching one based on the user-provided text description automatically.
MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer
To solve this issue, we propose a Masked Diffusion Transformer (MDT) that introduces a mask latent modeling scheme to explicitly enhance the DPMs' ability to contextual relation learning among object semantic parts in an image.
Ranked #2 on
Image Generation
on ImageNet 256x256
Towards Sustainable Self-supervised Learning
In this work, we explore a sustainable SSL framework with two major challenges: i) learning a stronger new SSL model based on the existing pretrained SSL model, also called as "base" model, in a cost-friendly manner, ii) allowing the training of the new model to be compatible with various base models.
Ranked #1 on
Semantic Segmentation
on ImageNet-S
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further.
Ranked #2 on
Instance Segmentation
on COCO 2017 val
(AP metric)
SERE: Exploring Feature Self-relation for Self-supervised Transformer
Specifically, instead of conducting self-supervised learning solely on feature embeddings from multiple views, we utilize the feature self-relations, i. e., spatial/channel self-relations, for self-supervised learning.
Ranked #2 on
Semantic Segmentation
on ImageNet-S
Large-scale Unsupervised Semantic Segmentation
In this work, we propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to help the research progress.
Ranked #1 on
Unsupervised Semantic Segmentation
on ImageNet-S-300
Hifi: Hierarchical feature integration for skeleton detection
In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts.
