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Found 21 papers, 10 papers with code
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
Unified Hallucination Detection for Multimodal Large Language Models
Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination.
Seamless and multi-resolution energy forecasting
The relationship between (i) errors in both time and frequency domains and (ii) operational value of the forecasts is analysed.
FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications.
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction
This pioneering effort aims to set the first benchmark for the AVTSE task, offering fresh insights into enhancing the ac-curacy of back-end speech recognition systems through AVTSE in challenging and real acoustic environments.
Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023
Three different structures based on attention-guided feature gathering (AFG) are designed for deep feature fusion.
MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing
To address this issue, we propose a new Transformer variant, which applies the Taylor expansion to approximate the softmax-attention and achieves linear computational complexity.
FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information
In the first stage, we improve the lightness of low-light images by estimating the amplitude transform map in the Fourier space.
Brighten-and-Colorize: A Decoupled Network for Customized Low-Light Image Enhancement
The colorization sub-task is accomplished by regarding the chrominance of the low-light image as color guidance like the user-guide image colorization.
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots.
Target-Referenced Reactive Grasping for Dynamic Objects
Reactive grasping, which enables the robot to successfully grasp dynamic moving objects, is of great interest in robotics.
Swin-Pose: Swin Transformer Based Human Pose Estimation
We are interested in exploring its capability in human pose estimation, and thus propose a novel model based on transformer architecture, enhanced with a feature pyramid fusion structure.
Simple Baseline for Single Human Motion Forecasting
Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction.
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images
In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image.
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
To quickly detect graspness in practice, we develop a neural network named graspness model to approximate the searching process.
Ranked #3 on
Robotic Grasping
on GraspNet-1Billion
Transferable Active Grasping and Real Embodied Dataset
Grasping in cluttered scenes is challenging for robot vision systems, as detection accuracy can be hindered by partial occlusion of objects.
GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping
Object grasping is critical for many applications, which is also a challenging computer vision problem.
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace.
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching
Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
DELTA: A DEep learning based Language Technology plAtform
In this paper we present DELTA, a deep learning based language technology platform.
Ranked #3 on
Text Classification
on Yahoo! Answers
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform.
Multiagent Systems
