Search Results for author:
Found 21 papers, 13 papers with code
UniArk: Improving Generalisation and Consistency for Factual Knowledge Extraction through Debiasing
We hypothesize that simultaneously debiasing these objectives can be the key to generalisation over unseen prompts.
Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal
Although ViWS-Net is proposed to remove adverse weather conditions in videos with a single set of pre-trained weights, it is seriously blinded by seen weather at train-time and degenerates when coming to unseen weather during test-time.
EEE-QA: Exploring Effective and Efficient Question-Answer Representations
Current approaches to question answering rely on pre-trained language models (PLMs) like RoBERTa.
GuardT2I: Defending Text-to-Image Models from Adversarial Prompts
Recent advancements in Text-to-Image (T2I) models have raised significant safety concerns about their potential misuse for generating inappropriate or Not-Safe-For-Work (NSFW) contents, despite existing countermeasures such as NSFW classifiers or model fine-tuning for inappropriate concept removal.
An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion
A total of 6 teams from 4 countries participated, whose proposed methods were evaluated on 16 images and two preoperative 3D models from two patients.
Vivim: a Video Vision Mamba for Medical Video Object Segmentation
Traditional convolutional neural networks have a limited receptive field while transformer-based networks are mediocre in constructing long-term dependency from the perspective of computational complexity.
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
Our SegMamba, in contrast to Transformer-based methods, excels in whole volume feature modeling from a state space model standpoint, maintaining superior processing speed, even with volume features at a resolution of {$64\times 64\times 64$}.
MMA-Diffusion: MultiModal Attack on Diffusion Models
In recent years, Text-to-Image (T2I) models have seen remarkable advancements, gaining widespread adoption.
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld
While large language models (LLMs) excel in a simulated world of texts, they struggle to interact with the more realistic world without perceptions of other modalities such as visual or audio signals.
Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation
In this work, we propose the first framework for restoring videos from all adverse weather conditions by developing a video adverse-weather-component suppression network (ViWS-Net).
MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening
This work devises MammoDG, a novel deep-learning framework for generalisable and reliable analysis of cross-domain multi-center mammography data.
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
We address it by "Continual Task Allocation via Sparse Prompting (CoTASP)", which learns over-complete dictionaries to produce sparse masks as prompts extracting a sub-network for each task from a meta-policy network.
DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification
However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification.
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
In this paper, we present a pure-Python library called PyPop7 for black-box optimization (BBO).
Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning
Empowered by the robust relation net built on SSL, we found that BEYOND outperforms baselines in terms of both detection ability and speed.
Out-of-Distribution Detection with Semantic Mismatch under Masking
This paper proposes a novel out-of-distribution (OOD) detection framework named MoodCat for image classifiers.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction
For legitimate inputs that are correctly inferred, the synthetic output tries to reconstruct the input.
Pareto Policy Pool for Model-based Offline Reinforcement Learning
Model-based offline RL instead trains an environment model using a dataset of pre-collected experiences so online RL methods can learn in an offline manner by solely interacting with the model.
HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation
Particularly, for the Extrinsic Consistency, we leverage the knowledge across multiple source domains to enforce data-level consistency.
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis
Consequently, we propose a novel learning-based solution to model such contradictions for AE detection.
The distance between the weights of the neural network is meaningful
In the application of neural networks, we need to select a suitable model based on the problem complexity and the dataset scale.
