Search Results for author:
Found 14 papers, 3 papers with code
MaskBEV: Towards A Unified Framework for BEV Detection and Map Segmentation
However, current multimodal perception research follows independent paradigms designed for specific perception tasks, leading to a lack of complementary learning among tasks and decreased performance in multi-task learning (MTL) due to joint training.
HybridOcc: NeRF Enhanced Transformer-based Multi-Camera 3D Occupancy Prediction
This paper proposes HybridOcc, a hybrid 3D volume query proposal method generated by Transformer framework and NeRF representation and refined in a coarse-to-fine SSC prediction framework.
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Specifically, we present a sample-level contrastive distillation mechanism that transfers comprehensive knowledge containing cross-sample correlations to reconstruct missing semantics.
CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision
At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations (in natural language), resulting a model able to generate better embeddings for binary code.
Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty
This paper first elaborates on the mismatch between more accurate forecasts and more optimal decisions in the power system caused by statistical-based learning (SBL) and explains how DOL resolves this problem.
Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality
In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function).
Adaptive Decision-Objective Loss for Forecast-then-Optimize in Power Systems
However, the implementation of decision loss in power systems faces challenges in 1) accommodating multi-stage decision-making problems where upstream optimality cannot guarantee final optimality; 2) adapting to dynamic environments such as changing parameters and nature of the problem like continuous or discrete optimization tasks.
ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
In safety-critical domains such as autonomous vehicles, offline deep reinforcement learning (offline DRL) is frequently used to train models on pre-collected datasets, as opposed to training these models by interacting with the real-world environment as the online DRL.
Human 3D Avatar Modeling with Implicit Neural Representation: A Brief Survey
This paper comprehensively reviews the application of implicit neural representation in human body modeling.
Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey
The overall aim of this survey is to demonstrate the development trend of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.
Risk-Aware Objective-Based Forecasting in Inertia Management
The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process.
Co-Speech Gesture Synthesis by Reinforcement Learning With Contrastive Pre-Trained Rewards
There is a growing demand of automatically synthesizing co-speech gestures for virtual characters.
Wireless Transmission of Images With The Assistance of Multi-level Semantic Information
In particular, the proposed model includes a multilevel semantic feature extractor, that extracts both the highlevel semantic information, such as the text semantics and the segmentation semantics, and the low-level semantic information, such as local spatial details of the images.
A deep learning-based remaining useful life prediction approach for bearings
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs).
