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Found 33 papers, 21 papers with code
System-2 Mathematical Reasoning via Enriched Instruction Tuning
Unlike existing CoT prompting methods that generate reasoning chains only depending on LLM's internal knowledge, our method leverages human-annotated initial answers as ``meta-knowledge'' to help LLMs generate more detailed and precise reasoning processes, leading to a more trustworthy LLM expert for complex mathematical problems.
From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle
In response to the call for agent-based solutions that leverage the ever-increasing capabilities of the deep models' ecosystem, we introduce Hive -- a comprehensive solution for selecting appropriate models and subsequently planning a set of atomic actions to satisfy the end-users' instructions.
DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation
The adaptation process has two stages: the first aligns the models on stable features using a mutual information consistency loss, and the second dynamically adjusts the perturbation level based on the loss from the first stage, encouraging the model to explore a broader range of the target domain while preserving existing performance.
Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network
Fundus imaging is a pivotal tool in ophthalmology, and different imaging modalities are characterized by their specific advantages.
Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization
Specifically, we construct a real-world dataset with 85 snowy videos, and then present a Semi-supervised Video Desnowing Network (SemiVDN) equipped by a novel Distribution-driven Contrastive Regularization.
Ranked #2 on
Snow Removal
on RVSD
(using extra training data)
WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents
The resulting world model is composed of the LLM and the learned rules.
Cross-conditioned Diffusion Model for Medical Image to Image Translation
First, we propose a Modality-specific Representation Model (MRM) to model the distribution of target modalities.
Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning
We incorporate multi-task supervision into diffusion models to promote the discrimination of diffusion models on pixel-by-pixel segmentation.
RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining
Recently, the linear-complexity operator of the state space models (SSMs) has contrarily facilitated efficient long-term temporal modeling, which is crucial for rain streaks and raindrops removal in videos.
Ranked #1 on
Video deraining
on Video Waterdrop Removal Dataset
LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos
To this end, we collect and annotate the first ultrasound video dataset with 100 videos for uterine fibroid segmentation (UFUV).
Ranked #1 on
Video Polyp Segmentation
on SUN-SEG-Easy
Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance
Firstly, we introduce a non-aligned reference frame matching module, leveraging an adaptive sliding window to match high-quality reference frames from clear videos.
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction
In this paper, we propose CloudFort, a novel defense mechanism designed to enhance the robustness of 3D point cloud classifiers against backdoor attacks.
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 Segmentation
To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for medical video segmentation tasks.
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 an open-source 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.
