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Found 20 papers, 10 papers with code
Fantastic Semantics and Where to Find Them: Investigating Which Layers of Generative LLMs Reflect Lexical Semantics
Large language models have achieved remarkable success in general language understanding tasks.
Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization
Adversarial Training (AT), pivotal in fortifying the robustness of deep learning models, is extensively adopted in practical applications.
AutoPCF: Efficient Product Carbon Footprint Accounting with Large Language Models
The product carbon footprint (PCF) is crucial for decarbonizing the supply chain, as it measures the direct and indirect greenhouse gas emissions caused by all activities during the product's life cycle.
PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation
We first conduct systematic analyses about the components of image fusion, investigating the correlation with segmentation robustness under adversarial perturbations.
Ranked #16 on
Thermal Image Segmentation
on MFN Dataset
Bilevel Generative Learning for Low-Light Vision
In this study, we propose a generic low-light vision solution by introducing a generative block to convert data from the RAW to the RGB domain.
Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation
Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation.
Ranked #3 on
Semantic Segmentation
on FMB Dataset
A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion
Qualitative and quantitative experimental results on different categories of image fusion problems and related downstream tasks (e. g., visual enhancement and semantic understanding) substantiate the flexibility and effectiveness of our TIM.
Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation
Word sense disambiguation (WSD), which aims to determine an appropriate sense for a target word given its context, is crucial for natural language understanding.
Embracing Compact and Robust Architectures for Multi-Exposure Image Fusion
In recent years, deep learning-based methods have achieved remarkable progress in multi-exposure image fusion.
Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond
Recently, multi-modality scene perception tasks, e. g., image fusion and scene understanding, have attracted widespread attention for intelligent vision systems.
PanoViT: Vision Transformer for Room Layout Estimation from a Single Panoramic Image
In this paper, we propose PanoViT, a panorama vision transformer to estimate the room layout from a single panoramic image.
Breaking Free from Fusion Rule: A Fully Semantic-driven Infrared and Visible Image Fusion
To address these challenges, in this letter, we develop a semantic-level fusion network to sufficiently utilize the semantic guidance, emancipating the experimental designed fusion rules.
Semantic-Aware Pretraining for Dense Video Captioning
This report describes the details of our approach for the event dense-captioning task in ActivityNet Challenge 2021.
ActiveZero: Mixed Domain Learning for Active Stereovision with Zero Annotation
First, we demonstrate the transferability of our method to out-of-distribution real data by using a mixed domain learning strategy.
Triple-level Model Inferred Collaborative Network Architecture for Video Deraining
In this paper, we develop a model-guided triple-level optimization framework to deduce network architecture with cooperating optimization and auto-searching mechanism, named Triple-level Model Inferred Cooperating Searching (TMICS), for dealing with various video rain circumstances.
Global Daily CO$_2$ emissions for the year 2020
That even substantial world-wide lockdowns of activity led to a one-time decline in global CO$_2$ emissions of only 5. 4% in one year highlights the significant challenges for climate change mitigation that we face in the post-COVID era.
Atmospheric and Oceanic Physics General Economics Economics
Optimization-Inspired Learning with Architecture Augmentations and Control Mechanisms for Low-Level Vision
Specifically, by introducing a general energy minimization model and formulating its descent direction from different viewpoints (i. e., in a generative manner, based on the discriminative metric and with optimality-based correction), we construct three propagative modules to effectively solve the optimization models with flexible combinations.
Estimates of daily ground-level NO2 concentrations in China based on big data and machine learning approaches
We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters.
Automatic Question-Answering Using A Deep Similarity Neural Network
We first train this model on a large-scale public question-answering database, and then fine-tune it to transfer to the customer-care chat data.
Deep Hashing: A Joint Approach for Image Signature Learning
In this paper, we propose a novel algorithm that concurrently performs feature engineering and non-linear supervised hashing function learning.
