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Found 16 papers, 6 papers with code
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning
The weakly supervised prompt learning model only utilizes the classes of images in the dataset to guide the learning of the specific class vector in the prompt, while the learning of other context vectors in the prompt requires no manual annotations for guidance.
Intensive Vision-guided Network for Radiology Report Generation
Given the above limitation in feature extraction, we propose a Globally-intensive Attention (GIA) module in the medical image encoder to simulate and integrate multi-view vision perception.
Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models
Our experiments validate the effectiveness of SparklesChat in understanding and reasoning across multiple images and dialogue turns.
Crystal Structure Prediction by Joint Equivariant Diffusion
To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry.
PSLT: A Light-weight Vision Transformer with Ladder Self-Attention and Progressive Shift
In this work, we propose a ladder self-attention block with multiple branches and a progressive shift mechanism to develop a light-weight transformer backbone that requires less computing resources (e. g. a relatively small number of parameters and FLOPs), termed Progressive Shift Ladder Transformer (PSLT).
Company Competition Graph
Based on the output from our system, we construct a knowledge graph with more than 700 nodes and 1200 edges.
Form 10-K Itemization
With the segmented Item sections, NLP techniques can directly apply on those Item sections related to downstream tasks.
Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets
The time proximity of trades across stocks reveals interesting topological structures of the equity market in the United States.
SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems
In this paper, we propose a scalable evaluation methodology (SAIH) for analyzing the AI performance trend of HPC systems with scaling the problem sizes of customized AI applications.
Trade Co-occurrence, Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets
The time proximity of high-frequency trades can contain a salient signal.
EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models
Although the AI community has expanded the model scale to the trillion parameter level, the practical deployment of 10-100 billion parameter models is still uncertain due to the latency, throughput, and memory constraints.
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
In this paper, we propose \textbf{LayoutLMv3} to pre-train multimodal Transformers for Document AI with unified text and image masking.
Ranked #1 on
Key Information Extraction
on EPHOIE
A Picture is Worth a Thousand Words: A Unified System for Diverse Captions and Rich Images Generation
In this work, we demonstrate such an AI creation system to produce both diverse captions and rich images.
Unifying Multimodal Transformer for Bi-directional Image and Text Generation
We adopt Transformer as our unified architecture for its strong performance and task-agnostic design.
Self-Motivated Communication Agent for Real-World Vision-Dialog Navigation
Vision-Dialog Navigation (VDN) requires an agent to ask questions and navigate following the human responses to find target objects.
Decoupling Localization and Classification in Single Shot Temporal Action Detection
Each branch produces a set of action anchor layers by applying deconvolution to the feature maps of the main stream.
Ranked #26 on
Temporal Action Localization
on THUMOS’14
