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Found 43 papers, 20 papers with code
Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach
To address this trade-off between scalability and classification accuracy, we reformulate the node classification task as a subgraph classification problem and propose SubGND (Subgraph GNN for NoDe).
Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs
Ultimately, our experimental findings demonstrate that a 300B MoE LLM can be effectively trained on lower-performance devices while achieving comparable performance to models of a similar scale, including dense and MoE models.
SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection
Despite their effectiveness, these methods face two challenges: (1) feature granularity deficiency, due to reliance on last layer visual features for text alignment, leading to the neglect of crucial object-level details from intermediate layers; (2) semantic similarity confusion, resulting from CLIP's inherent biases toward certain classes, while LLM-generated descriptions based solely on labels fail to adequately capture inter-class similarities.
Easy-Poly: A Easy Polyhedral Framework For 3D Multi-Object Tracking
Recent advancements in 3D multi-object tracking (3D MOT) have predominantly relied on tracking-by-detection pipelines.
RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection
Specifically, we design a 3D Gaussian Expansion (3DGE) module to mitigate inaccuracies in radar points, including position, Radar Cross-Section (RCS), and velocity.
Distraction is All You Need for Multimodal Large Language Model Jailbreaking
Multimodal Large Language Models (MLLMs) bridge the gap between visual and textual data, enabling a range of advanced applications.
Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance
To address this issue, we first introduce the DRI method to obtain degradation vectors and quality vectors of images, which separately model the degradation and quality information of low-quality images.
An adaptative differential evolution with enhanced diversity and restart mechanism
Differential Evolution (DE) stands out as an exceptional intelligent evolutionary algorithm, acclaimed for its simplicity in implementation and the ability to optimize without necessitating differentiable conditions.
Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restoration
As SE increases computational complexity during inference, we propose a re-boosting module to the SC (Reb-SC) to improve the SC strategy further by incorporating SE into SC without increasing inference time.
Surrogate-assisted evolutionary framework with an ensemble of teaching-learning and differential evolution for expensive optimization
Surrogate-Assisted Evolutionary Algorithms (SAEAs) integrate Evolutionary Algorithms (EAs) with surrogate models to reduce the actual number of expensive function evaluations and have been widely used in solving Expensive Optimization Problems (EOPs).
ACD-DE: An adaptive cluster division Differential Evolution for mitigating population diversity deficiency
In this paper, an Adaptive Cluster Division Differential Evolution (ACD-DE) algorithm was proposed to mitigate population diversity deficiency.
EduNLP: Towards a Unified and Modularized Library for Educational Resources
We also provide a configurable pipeline to unify the data usage and model usage in standard ways, where users can customize their own needs.
DOP: Diagnostic-Oriented Prompting for Large Language Models in Mathematical Correction
Math world problems correction(MWPC) is a novel task dedicated to rectifying reasoning errors in the process of solving mathematical problems.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience
Specifically, we first identify three key problems: (1) How to capture the inherent confidence of the LLM?
Towards a MATLAB Toolbox to compute backstepping kernels using the power series method
Our first contribution is the development of initial steps towards a MATLAB toolbox dedicated to backstepping kernel computation.
TINA: Think, Interaction, and Action Framework for Zero-Shot Vision Language Navigation
We evaluate the performance of our method on the Room-to-Room dataset.
BDIQA: A New Dataset for Video Question Answering to Explore Cognitive Reasoning through Theory of Mind
This paper presents BDIQA, the first benchmark to explore the cognitive reasoning capabilities of VideoQA models in the context of ToM.
Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales
Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data.
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation
To address the above problems, in this paper, we introduce a network named TD$^2$-Net that aims at denoising and debiasing for dynamic SGG.
Multi-task Image Restoration Guided By Robust DINO Features
To guide the restoration model with the features of DINOv2, we develop a DINO-Restore adaption and fusion module to adjust the channel of fused features from PSF and then integrate them with the features from the restoration model.
3D Multiple Object Tracking on Autonomous Driving: A Literature Review
This paper undertakes a comprehensive examination, assessment, and synthesis of the research landscape in this domain, remaining attuned to the latest developments in 3D MOT while suggesting prospective avenues for future investigation.
Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches
Although unsupervised approaches based on generative adversarial networks offer a promising solution for denoising without paired datasets, they are difficult in surpassing the performance limitations of conventional GAN-based unsupervised frameworks without significantly modifying existing structures or increasing the computational complexity of denoisers.
Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark
Our model features two main components: a Dual Degradation Representation Network (DDR-Net) and a Restoration Network.
Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense
While having achieved great success in rich real-life applications, deep neural network (DNN) models have long been criticized for their vulnerability to adversarial attacks.
A Review on Machine Theory of Mind
Beliefs, desires, and intentions are the early abilities of infants and the foundation of human cognitive ability, as well as for machine with ToM.
Eliminating Contextual Prior Bias for Semantic Image Editing via Dual-Cycle Diffusion
The proposed model incorporates a Bias Elimination Cycle that consists of both a forward path and an inverted path, each featuring a Structural Consistency Cycle to ensure the preservation of image content during the editing process.
Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training
To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer.
Evolutionary Multiparty Distance Minimization
In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives.
HL-Net: Heterophily Learning Network for Scene Graph Generation
Despite their effectiveness, however, current SGG methods only assume scene graph homophily while ignoring heterophily.
Shifting More Attention to Visual Backbone: Query-modulated Refinement Networks for End-to-End Visual Grounding
Moreover, since the backbones are query-agnostic, it is difficult to completely avoid the inconsistency issue by training the visual backbone end-to-end in the visual grounding framework.
Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation
A common problem in both pruning and distillation is to determine compressed architecture, i. e., the exact number of filters per layer and layer configuration, in order to preserve most of the original model capacity.
Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments
This is very helpful for the decision makers, especially when facing changing environments.
Inferring Substitutable and Complementary Products with Knowledge-Aware Path Reasoning based on Dynamic Policy Network
To achieve this, existing approaches take advantage of the knowledge graphs to learn more evidences for inference, whereas they often suffer from invalid reasoning for lack of elegant decision making strategies.
ECNU\_ICA\_1 SemEval-2021 Task 4: Leveraging Knowledge-enhanced Graph Attention Networks for Reading Comprehension of Abstract Meaning
This paper describes our system for SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning.
ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation
As a result, this model performs quite well in both validation and explanation.
GPS-Net: Graph Property Sensing Network for Scene Graph Generation
There are three key properties of scene graph that have been underexplored in recent works: namely, the edge direction information, the difference in priority between nodes, and the long-tailed distribution of relationships.
Ranked #5 on
Scene Graph Generation
on Visual Genome
Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation
To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task.
Long Short-Term Attention
Attention is an important cognition process of humans, which helps humans concentrate on critical information during their perception and learning.
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN
Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images.
