Search Results for author:
Found 11 papers, 6 papers with code
AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models
Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks.
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction
Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design.
Multi-level Interaction Modeling for Protein Mutational Effect Prediction
Protein-protein interactions are central mediators in many biological processes.
Visual Transformation Telling
Humans can naturally reason from superficial state differences (e. g. ground wetness) to transformations descriptions (e. g. raining) according to their life experience.
Visual Reasoning: from State to Transformation
Such \textbf{state driven} visual reasoning has limitations in reflecting the ability to infer the dynamics between different states, which has shown to be equally important for human cognition in Piaget's theory.
S$^2$-FPN: Scale-ware Strip Attention Guided Feature Pyramid Network for Real-time Semantic Segmentation
This paper presents a new model to achieve a trade-off between accuracy/speed for real-time road scene semantic segmentation.
Ranked #1 on
Real-Time Semantic Segmentation
on Cityscapes
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
Transformation Driven Visual Reasoning
Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i. e.~Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views).
A Tensor Network based Decision Diagram for Representation of Quantum Circuits
Tensor networks have been successfully applied in simulation of quantum physical systems for decades.
Quantum Physics Data Structures and Algorithms
Robust Reinforcement Learning with Wasserstein Constraint
Robust Reinforcement Learning aims to find the optimal policy with some extent of robustness to environmental dynamics.
Deep Fusion Network for Image Completion
The fusion block not only provides a smooth fusion between restored and existing content, but also provides an attention map to make network focus more on the unknown pixels.
