Search Results for author:
Found 291 papers, 135 papers with code
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
There have been some early attempts to tackle noisy labels in FL.
CombiBench: Benchmarking LLM Capability for Combinatorial Mathematics
CombiBench is suitable for testing IMO solving capabilities since it includes all IMO combinatorial problems since 2000 (except IMO 2004 P3 as its statement contain an images).
VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge Injection
Audiovisual emotion recognition (AVER) aims to infer human emotions from nonverbal visual-audio (VA) cues, offering modality-complementary and language-agnostic advantages.
Ranked #1 on
Video Emotion Recognition
on CREMA-D
Contrastive Learning
Dynamic Facial Expression Recognition
+3
PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition
Besides, it introduces Long- and Short-Term Temporal Contrastive Learning (LS-TCL) to capture emotional synchronization at different temporal resolutions within modalities.
A Pre-Training and Adaptive Fine-Tuning Framework for Graph Anomaly Detection
Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet it remains challenging due to the scarcity of abnormal nodes and the high cost of label annotations.
The Tenth NTIRE 2025 Image Denoising Challenge Report
This paper presents an overview of the NTIRE 2025 Image Denoising Challenge ({\sigma} = 50), highlighting the proposed methodologies and corresponding results.
Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning
We introduce Kimina-Prover Preview, a large language model that pioneers a novel reasoning-driven exploration paradigm for formal theorem proving, as showcased in this preview release.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(using extra training data)
PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood Estimation
In constructing this benchmark, we first collected recent trend forecasting questions and then filtered the data using CIL, resulting in an inferable benchmark for event prediction.
SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chain
Co-speech gesture generation enhances human-computer interaction realism through speech-synchronized gesture synthesis.
Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification
Medical time series (MedTS) classification is crucial for improved diagnosis in healthcare, and yet it is challenging due to the varying granularity of patterns, intricate inter-channel correlation, information redundancy, and label scarcity.
Indoor Fusion Positioning Based on "IMU-Ultrasonic-UWB" and Factor Graph Optimization Method
This paper presents a high-precision positioning system that integrates ultra-wideband (UWB) time difference of arrival (TDoA) measurements, inertial measurement unit (IMU) data, and ultrasonic sensors through factor graph optimization.
A Survey of Cross-domain Graph Learning: Progress and Future Directions
Graph learning plays a vital role in mining and analyzing complex relationships involved in graph data, which is widely used in many real-world applications like transaction networks and communication networks.
Unify and Anchor: A Context-Aware Transformer for Cross-Domain Time Series Forecasting
The rise of foundation models has revolutionized natural language processing and computer vision, yet their best practices to time series forecasting remains underexplored.
InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization
In order to decode the complex knowledge of multiple properties in the inversion path, we propose a gradient-based Pareto search method to balance conflicting properties and generate Pareto optimal molecules.
Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion Model
Effective generation of molecular structures, or new chemical entities, that bind to target proteins is crucial for lead identification and optimization in drug discovery.
CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer
Accurate Subseasonal-to-Seasonal (S2S) climate forecasting is pivotal for decision-making including agriculture planning and disaster preparedness but is known to be challenging due to its chaotic nature.
BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction
Notably, BatteryLife is the first to release battery life datasets of zinc-ion batteries, sodium-ion batteries, and industry-tested large-capacity lithium-ion batteries.
CodeSwift: Accelerating LLM Inference for Efficient Code Generation
Experimental results show that CodeSwift can reach up to 2. 53x and 2. 54x speedup compared to autoregressive decoding in repository-level and standalone code generation tasks, respectively, outperforming state-of-the-art inference acceleration approaches by up to 88%.
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Moreover, existing methods often struggle with the integration of extracted CF information with LLMs due to its implicit representation and the modality gap between graph structures and natural language explanations.
S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning
In this work, we introduce S$^2$R, an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference.
Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
On the miniF2F benchmark, it achieves a 57. 6% success rate (Pass@32), exceeding the previous best open-source model by 7. 6%.
Do Audio-Visual Segmentation Models Truly Segment Sounding Objects?
Our study reveals a fundamental bias in current methods: they tend to generate segmentation masks based predominantly on visual salience, irrespective of the audio context.
Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints
Many existing algorithms cannot recognize new cell types present in only one of the two samples when establishing a correspondence between clusters obtained from two samples.
InfoBFR: Real-World Blind Face Restoration via Information Bottleneck
Blind face restoration (BFR) is a highly challenging problem due to the uncertainty of data degradation patterns.
Baichuan-Omni-1.5 Technical Report
We introduce Baichuan-Omni-1. 5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities.
Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning
Depicting novel classes with language descriptions by observing few-shot samples is inherent in human-learning systems.
Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study
However, the effectiveness of LLMs in detecting code vulnerabilities is largely under-explored.
Mixed geometry information regularization for image multiplicative denoising
We employ the second order SAV algorithm to further speed up the calculation while maintaining accuracy.
Why language models collapse when trained on recursively generated text
We present a new finding: the performance of LMs gradually declines when trained on recursively generated text until they perform no better than a randomly initialized LM.
Agent Journey Beyond RGB: Unveiling Hybrid Semantic-Spatial Environmental Representations for Vision-and-Language Navigation
SUSA includes a Textual Semantic Understanding (TSU) module, which narrows the modality gap between instructions and environments by generating and associating the descriptions of environmental landmarks in agent's immediate surroundings.
Ranked #1 on
Visual Navigation
on R2R
How to Use Diffusion Priors under Sparse Views?
However, the diffusion model, as an external prior that can directly provide visual supervision, has always underperformed in sparse-view 3D reconstruction using Score Distillation Sampling (SDS) due to the low information entropy of sparse views compared to text, leading to optimization challenges caused by mode deviation.
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation
Adversarial attacks can be used as a criterion for judging the adaptability of neural networks to real data, since adversarial attacks can find the most extreme perturbations that make neural networks ineffective.
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
We then present an analysis of decomposing the optimization target into a consistency penalty and a structure modification based on cause-effect relations.
Ranked #50 on
Node Classification
on Squirrel
UniGAD: Unifying Multi-level Graph Anomaly Detection
Existing methods generally focus on a single graph object type (node, edge, graph, etc.)
Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models
As large language models become increasingly prevalent in the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance.
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
Numerous industrial sectors necessitate models capable of providing robust forecasts across various horizons.
ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations
Accurate forecasting of spatiotemporal data remains challenging due to complex spatial dependencies and temporal dynamics.
EvoCodeBench: An Evolving Code Generation Benchmark with Domain-Specific Evaluations
These evaluations help practitioners select superior LLMs in specific domains and discover the shortcomings of existing LLMs.
Graph Pre-Training Models Are Strong Anomaly Detectors
Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results.
GCoder: Improving Large Language Model for Generalized Graph Problem Solving
Furthermore, GCoder efficiently manages large-scale graphs with millions of nodes and diverse input formats, overcoming the limitations of previous models focused on the reasoning steps paradigm.
aiXcoder-7B: A Lightweight and Effective Large Language Model for Code Processing
In this paper, we propose a lightweight and effective LLM for code completion named aiXcoder-7B.
Addressing Heterogeneity and Heterophily in Graphs: A Heterogeneous Heterophilic Spectral Graph Neural Network
Specifically, we propose a Heterogeneous Heterophilic Spectral Graph Neural Network (H2SGNN), which employs a dual-module approach: local independent filtering and global hybrid filtering.
Zeroth-Order Fine-Tuning of LLMs in Random Subspaces
Fine-tuning Large Language Models (LLMs) has proven effective for a variety of downstream tasks.
DAT: Dialogue-Aware Transformer with Modality-Group Fusion for Human Engagement Estimation
Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affective computing and human-computer interaction.
Text Proxy: Decomposing Retrieval from a 1-to-N Relationship into N 1-to-1 Relationships for Text-Video Retrieval
By replacing a single text query with a series of text proxies, TV-ProxyNet not only broadens the query scope but also achieves a more precise expansion.
Generating Equivalent Representations of Code By A Self-Reflection Approach
In this paper, we propose a self-reflection approach to generating ERs of code.
Showing LLM-Generated Code Selectively Based on Confidence of LLMs
HonestCoder selectively shows the generated programs to developers based on LLMs' confidence.
FAN: Fourier Analysis Networks
Despite the remarkable successes of general-purpose neural networks, such as MLPs and Transformers, we find that they exhibit notable shortcomings in modeling and reasoning about periodic phenomena, achieving only marginal performance within the training domain and failing to generalize effectively to out-of-domain (OOD) scenarios.
Energy Saving and Traffic Steering Use Case and Testing by O-RAN RIC xApp/rApp Multi-vendor Interoperability
This paper discusses the use case of energy saving and traffic steering in O-RAN, the mechanism of multi-vendor interoperability to make it work and depict its test methodology.
ControlMath: Controllable Data Generation Promotes Math Generalist Models
The module creates diverse equations, which the Problem-Crafter agent then transforms into math word problems.
Look Through Masks: Towards Masked Face Recognition with De-Occlusion Distillation
Many real-world applications today like video surveillance and urban governance need to address the recognition of masked faces, where content replacement by diverse masks often brings in incomplete appearance and ambiguous representation, leading to a sharp drop in accuracy.
Efficient Low-Resolution Face Recognition via Bridge Distillation
Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability.
Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data
Dynamic facial expression recognition (DFER) infers emotions from the temporal evolution of expressions, unlike static facial expression recognition (SFER), which relies solely on a single snapshot.
Ranked #3 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
Seeing is Believing? Enhancing Vision-Language Navigation using Visual Perturbations
Autonomous navigation for an embodied agent guided by natural language instructions remains a formidable challenge in vision-and-language navigation (VLN).
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
Intuitively, the performance degradation is dominated by clients with higher noise rates since their trained models contain more misinformation from data, thus it is necessary to devise an effective optimization scheme to mitigate the negative impacts of these noisy clients.
E$^3$NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images
To effectively introduce event streams into the neural volumetric representation learning process, we propose an event-enhanced blur rendering loss and an event rendering loss, which guide the network via modeling the real blur process and event generation process, respectively.
Building Trust in Mental Health Chatbots: Safety Metrics and LLM-Based Evaluation Tools
Conclusion: The study validated an evaluation framework for mental health chatbots, proving its effectiveness in improving safety and reliability.
PGNeXt: High-Resolution Salient Object Detection via Pyramid Grafting Network
To compensate for the lack of HRSOD dataset, we thoughtfully collect a large-scale high resolution salient object detection dataset, called UHRSD, containing 5, 920 images from real-world complex scenarios at 4K-8K resolutions.
Evidence and quantification of cooperation of driving agents in mixed traffic flow
This study provides the first empirical understanding of collective cooperativeness in human-driven mixed traffic and points to new possibilities to manage mixed autonomy traffic systems.
Parameter-Efficient Fine-Tuning via Circular Convolution
Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, leveraging low-rank matrices $\mathbf{A}$ and $\mathbf{B}$ to represent weight changes (i. e., $\Delta \mathbf{W} = \mathbf{B} \mathbf{A}$).
The Oscars of AI Theater: A Survey on Role-Playing with Language Models
This survey explores the burgeoning field of role-playing with language models, focusing on their development from early persona-based models to advanced character-driven simulations facilitated by Large Language Models (LLMs).
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
To bridge this gap, we introduce GLBench, the first comprehensive benchmark for evaluating GraphLLM methods in both supervised and zero-shot scenarios.
GraphArena: Benchmarking Large Language Models on Graph Computational Problems
The "arms race" of Large Language Models (LLMs) demands novel, challenging, and diverse benchmarks to faithfully examine their progresses.
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
Moreover, we show that DEGNN is data efficient, learning with less data, and can generalize across scenarios such as unobserved orientation.
Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment
Neural Radiance Fields (NeRF) achieves impressive 3D representation learning and novel view synthesis results with high-quality multi-view images as input.
SynthTree: Co-supervised Local Model Synthesis for Explainable Prediction
Explainable machine learning (XML) has emerged as a major challenge in artificial intelligence (AI).
ProG: A Graph Prompt Learning Benchmark
Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional 'Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer issues, particularly in complex and few-shot settings.
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
The recent rapid advancements in language models (LMs) have garnered attention in medical time series-text multimodal learning.
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories
Our experiments reveal these LLMs' coding abilities in real-world code repositories.
One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments
Large Language Models (LLMs) have advanced rapidly but face significant memory demands.
Text Guided Image Editing with Automatic Concept Locating and Forgetting
With the advancement of image-to-image diffusion models guided by text, significant progress has been made in image editing.
4-bit Shampoo for Memory-Efficient Network Training
In this paper, we propose the first 4-bit second-order optimizers, exemplified by 4-bit Shampoo, maintaining performance similar to that of 32-bit ones.
Canonical Variates in Wasserstein Metric Space
Central to our investigation is dimension reduction within the Wasserstein metric space to enhance classification accuracy.
Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
Low-rank adaptation~(LoRA) has recently gained much interest in fine-tuning foundation models.
A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model
There are two main research lines, namely pre-training foundation models from scratch for time series and adapting large language foundation models for time series.
A Comprehensive Evaluation on Event Reasoning of Large Language Models
It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms.
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey
This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results.
Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
This paper investigates diverse LLMs and learning-based transpilers for automated code translation tasks, finding that: although certain LLMs have outperformed current transpilers, they still have some accuracy issues, where most of the failures are induced by a lack of comprehension of source programs, missing clear instructions on I/O types in translation, and ignoring discrepancies between source and target programs.
EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories
Existing benchmarks demonstrate poor alignment with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs.
DiffMAC: Diffusion Manifold Hallucination Correction for High Generalization Blind Face Restoration
Experimental results demonstrate the superiority of DiffMAC over state-of-the-art methods, with a high degree of generalization in real-world and heterogeneous settings.
EventRPG: Event Data Augmentation with Relevance Propagation Guidance
Based on this, we propose EventRPG, which leverages relevance propagation on the spiking neural network for more efficient augmentation.
Ranked #1 on
Action Recognition
on SL-Animals
All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)
This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan.
Deep Reinforcement Learning for Modelling Protein Complexes
In this work, by taking each chain as a node and assembly actions as edges, we show that an acyclic undirected connected graph can be used to predict the structure of multi-chain protein complexes (a. k. a., protein complex modelling, PCM).
StarCoder 2 and The Stack v2: The Next Generation
Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
Ranked #35 on
Code Generation
on MBPP
GraphWiz: An Instruction-Following Language Model for Graph Problems
Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored.
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations
Existing retrieval-based methods have made significant strides in maintaining long-term conversations.
ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs
The results underscore the effectiveness of our model in achieving significant cross-dataset zero-shot transferability, opening pathways for the development of graph foundation models.
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining
In response to this challenge, we propose a novel approach called Graph COordinators for PrEtraining (GCOPE), that harnesses the underlying commonalities across diverse graph datasets to enhance few-shot learning.
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
In this paper, we introduce a novel framework Knowledge-Data Alignment (KDAlign) to integrate rule knowledge, typically summarized by human experts, to supplement the limited labeled data.
DevEval: Evaluating Code Generation in Practical Software Projects
Compared to previous benchmarks, DevEval aligns to practical projects in multiple dimensions, e. g., real program distributions, sufficient dependencies, and enough-scale project contexts.
Nucleus subtype classification using inter-modality learning
In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E.
Path-based Explanation for Knowledge Graph Completion
Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph Completion (KGC) by modelling how entities and relations interact in recent years.
Learning Performance Maximizing Ensembles with Explainability Guarantees
In this paper we propose a method for the optimal allocation of observations between an intrinsically explainable glass box model and a black box model.
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting
IMP-TIP follows the ``From Good to Great" concept, collecting multiple potential solutions from both LLMs and their Tool-Augmented counterparts for the same math problem, and then selecting or re-generating the most accurate answer after cross-checking these solutions via tool-augmented interleaf prompting.
From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos
And the TMAs capture and model the relationships of dynamic changes in facial expressions, effectively extending the pre-trained image model for videos.
Ranked #2 on
Dynamic Facial Expression Recognition
on FERV39k
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers
This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing.
Fair Text-to-Image Diffusion via Fair Mapping
In this paper, we address the limitations of existing text-to-image diffusion models in generating demographically fair results when given human-related descriptions.
Graph Prompt Learning: A Comprehensive Survey and Beyond
This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications.
Segment Every Out-of-Distribution Object
Semantic segmentation models, while effective for in-distribution categories, face challenges in real-world deployment due to encountering out-of-distribution (OoD) objects.
SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation
Inspired by the Cloze task and BERT, we fully consider the characteristics of spatial interpolation and design the SpaFormer model based on the Transformer architecture as the core of SSIN.
A Survey of Graph Meets Large Language Model: Progress and Future Directions
First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i. e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks.
Enabling CMF Estimation in Data-Constrained Scenarios: A Semantic-Encoding Knowledge Mining Model
This new data-driven framework provides a cost-effective and adaptable solution that complements the case-specific approaches for CMF estimation, which is particularly beneficial when availability of crash data or time imposes constraints.
ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation
To help human users refine their requirements and improve large language models' code generation performances, we propose ChatCoder: a method to refine the requirements via chatting with large language models.
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
This indicates that crafting multilingual corpora can be regarded as a vital strategy for enhancing model performance in a specific language, especially in mathematical reasoning tasks.
Large Language Model-Aware In-Context Learning for Code Generation
Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation.
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA
The associations between these repetitive objects are superfluous for answer reasoning; (2) two spatially distant OCR tokens detected in the image frequently have weak semantic dependencies for answer reasoning; and (3) the co-existence of nearby objects and tokens may be indicative of important visual cues for predicting answers.
ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons
Delivering precise point and distributional forecasts across a spectrum of prediction horizons represents a significant and enduring challenge in the application of time-series forecasting within various industries.
Deep Insights into Noisy Pseudo Labeling on Graph Data
We first present the error analysis of PL strategy by showing that the error is bounded by the confidence of PL threshold and consistency of multi-view prediction.
Dual-Path Temporal Map Optimization for Make-up Temporal Video Grounding
Compared with the general video grounding task, MTVG focuses on meticulous actions and changes on the face.
Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models
With an analysis of loss distributions of code tokens, we find that code tokens can be divided into two categories: challenging tokens that are difficult to predict and confident tokens that can be easily inferred.
ZC3: Zero-Shot Cross-Language Code Clone Detection
In this paper, we propose a novel method named ZC3 for Zero-shot Cross-language Code Clone detection.
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization
Besides the patternized words, a code summary also contains important keywords, which are the key to reflecting the functionality of the code.
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization.
Exploiting Diverse Feature for Multimodal Sentiment Analysis
Specifically, we achieve the results of 0. 8492 and 0. 8439 for MuSe-Personalisation in terms of arousal and valence CCC.
A Co-training Approach for Noisy Time Series Learning
Based on this, we create two views for the input time series through two different encoders.
Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures
This analysis is based on data collected at the two research institutes.
Towards Imbalanced Motion: Part-Decoupling Network for Video Portrait Segmentation
That is, motion of different parts of the portraits is imbalanced.
Relation-Oriented: Toward Causal Knowledge-Aligned AGI
Observation-Oriented paradigm currently dominates relationship learning models, including AI-based ones, which inherently do not account for relationships with temporally nonlinear effects.
Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition
Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets.
All in One: Multi-task Prompting for Graph Neural Networks
Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks.
Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer
SaaFormer applies a multi-level spectral extraction structure to segment the spectrum into multiple spectrum clips, such that the wavelength continuity of the spectrum across the channel are preserved.
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a standard comprehensive setting, (2) whether GNNs can outperform traditional algorithms such as tree ensembles, and (3) how about their efficiency on large-scale graphs.
Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning
Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications.
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series discrepancy.
A Graph Transformer-Driven Approach for Network Robustness Learning
Learning and analysis of network robustness, including controllability robustness and connectivity robustness, is critical for various networked systems against attacks.
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains.
EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models
In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects.
Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition
Deep neural networks (DNNs) are used to fit and train the pressure image stream and recognize the corresponding human behavior.
Structured Chain-of-Thought Prompting for Code Generation
In this paper, we propose Structured CoTs (SCoTs) and present a novel prompting technique for code generation, named SCoT prompting.
A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment
In this paper, we introduce FGWEA, an unsupervised entity alignment framework that leverages the Fused Gromov-Wasserstein (FGW) distance, allowing for a comprehensive comparison of entity semantics and KG structures within a joint optimization framework.
Alleviating Over-smoothing for Unsupervised Sentence Representation
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #53 on
Code Generation
on MBPP
Self-Edit: Fault-Aware Code Editor for Code Generation
Inspired by the process of human programming, we propose a generate-and-edit approach named Self-Edit that utilizes execution results of the generated code from LLMs to improve the code quality on the competitive programming task.
Data Imputation from the Perspective of Graph Dirichlet Energy
In our study, we examine this prevalent strategy through the lens of graph Dirichlet energy.
Cross-scale Multi-instance Learning for Pathological Image Diagnosis
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology.
AceCoder: Utilizing Existing Code to Enhance Code Generation
A key question is how to make prompts (i. e., Prompting Techniques).
Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning
Passive radio frequency (PRF)-based indoor positioning systems (IPS) have attracted researchers' attention due to their low price, easy and customizable configuration, and non-invasive design.
Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers
In this paper, we present our advanced solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation Challenge and Expression (Expr) Classification Challenge.
TriDet: Temporal Action Detection with Relative Boundary Modeling
In this paper, we present a one-stage framework TriDet for temporal action detection.
Ranked #2 on
Temporal Action Localization
on EPIC-KITCHENS-100
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
This observation allows us to provide an approximation bound for the distance between the fixed-point set of BAPG and the critical point set of GW.
Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death.
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably.
Knowledge Graph Completion with Counterfactual Augmentation
With a carefully designed instantiation of a causal model on the knowledge graph, we generate the counterfactual relations to answer the question by regarding the representations of entity pair given relation as context, structural information of relation-aware neighborhood as treatment, and validity of the composed triplet as the outcome.
Natural Response Generation for Chinese Reading Comprehension
To this end, we construct a new dataset called Penguin to promote the research of MRC, providing a training and test bed for natural response generation to real scenarios.
Chinese Reading Comprehension
Machine Reading Comprehension
+1
Bridge the Gap between Language models and Tabular Understanding
For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval.
Syntax and Domain Aware Model for Unsupervised Program Translation
The experimental results on function translation tasks between Python, Java, and C++ show that SDA-Trans outperforms many large-scale pre-trained models, especially for unseen language translation.
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
As the graphs to be aligned are usually constructed from different sources, the inconsistency issues of structures and features between two graphs are ubiquitous in real-world applications.
Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff
To this end, we design a lightweight framework while maintaining satisfying competitive accuracy.
SantaCoder: don't reach for the stars!
The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code.
Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms
Diversity maximization aims to select a diverse and representative subset of items from a large dataset.
E2NeRF: Event Enhanced Neural Radiance Fields from Blurry Images
To solve this problem, we propose a novel Event-Enhanced NeRF (E2NeRF) by utilizing the combination data of a bio-inspired event camera and a standard RGB camera.
Part-guided Relational Transformers for Fine-grained Visual Recognition
This framework, namely PArt-guided Relational Transformers (PART), is proposed to learn the discriminative part features with an automatic part discovery module, and to explore the intrinsic correlations with a feature transformation module by adapting the Transformer models from the field of natural language processing.
Ranked #8 on
Fine-Grained Image Classification
on FGVC Aircraft
Fine-Grained Image Classification
Fine-Grained Visual Recognition
+1
Parsing Objects at a Finer Granularity: A Survey
Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e. g., agriculture, remote sensing, and space technologies.
Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph.
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy.
The Stack: 3 TB of permissively licensed source code
Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)--not only for natural language processing but also for code understanding and generation.
Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space
Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane.
Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
In this paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to advance the study of dialogue agents and character alignment.
Robust DNN Surrogate Models with Uncertainty Quantification via Adversarial Training
Furthermore, we adopt methods in adversarial training to enhance the robustness of DNN surrogate models.
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Large-scale pre-trained language models have achieved impressive results on a wide range of downstream tasks recently.
Invariant and consistent: Unsupervised representation learning for few-shot visual recognition
Recent ideas propose to explore this problem in an unsupervised setting, i. e., without any labels in base classes, which reduces the heavy consumption of manual annotations.
Poison Attack and Defense on Deep Source Code Processing Models
The attackers aim to inject insidious backdoors into models by poisoning the training data with poison samples.
CodeEditor: Learning to Edit Source Code with Pre-trained Models
Pre-trained models are first pre-trained with pre-training tasks and fine-tuned with the code editing task.
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD).
Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images
Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations.
Generating Negative Samples for Sequential Recommendation
Due to the dynamics of users' interests and model updates during training, considering randomly sampled items from a user's non-interacted item set as negatives can be uninformative.
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis
In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges.
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
PF-ViT aims to separate and recognize the disturbance-agnostic emotion from a static facial image via generating its corresponding poker face, without the need for paired images.
Ranked #7 on
Facial Expression Recognition (FER)
on FER+
Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing
With the rapid development of mobile devices, modern widely-used mobile phones typically allow users to capture 4K resolution (i. e., ultra-high-definition) images.
Ranked #1 on
Image Restoration
on UHDM
ReAct: Temporal Action Detection with Relational Queries
Moreover, we propose two losses to facilitate and stabilize the training of action classification.
Ranked #17 on
Temporal Action Localization
on THUMOS’14
Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning
Graph self-supervised learning (SSL) has been vastly employed to learn representations from unlabeled graphs.
Deep Learning Eliminates Massive Dust Storms from Images of Tianwen-1
Inspired by the haze formation process on Earth, we formulate a similar visual degradation process on clean images and synthesize dusty images sharing a similar feature distribution with realistic dusty images.
Semi-Supervised Hierarchical Graph Classification
We study the node classification problem in the hierarchical graph where a 'node' is a graph instance.
Rethinking Graph Neural Networks for Anomaly Detection
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection.
MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation
Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions.
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks.
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process.
Ranked #8 on
RGB Salient Object Detection
on UHRSD
(using extra training data)
Video Demoireing with Relation-Based Temporal Consistency
Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras.
ELECRec: Training Sequential Recommenders as Discriminators
A generator, as an auxiliary model, is trained jointly with the discriminator to sample plausible alternative next items and will be thrown out after training.
Improving Contrastive Learning with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
ConTinTin: Continual Learning from Task Instructions
This work defines a new learning paradigm ConTinTin (Continual Learning from Task Instructions), in which a system should learn a sequence of new tasks one by one, each task is explained by a piece of textual instruction.
Robust facial expression recognition with global‑local joint representation learning
In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Styleverse: Towards Identity Stylization across Heterogeneous Domains
We propose a new challenging task namely IDentity Stylization (IDS) across heterogeneous domains.
A Machine Learning Paradigm for Studying Pictorial Realism: Are Constable's Clouds More Real than His Contemporaries?
The British landscape painter John Constable is considered foundational for the Realist movement in 19th-century European painting.
Intent Contrastive Learning for Sequential Recommendation
Specifically, we introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering.
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data.
Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution
The distribution gap between training datasets and data encountered in production is well acknowledged.
Deconvolutional Networks on Graph Data
In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct the input graph signal?"
Receptive Field Broadening and Boosting for Salient Object Detection
Moreover, Unlike multi-path parallel training, MHB randomly selects one branch each time for gradient back propagation in a boosting way.
Deconfounded Causal Collaborative Filtering
Experiments on real-world datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.
Transformer-based Dual Relation Graph for Multi-label Image Recognition
Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i. e., structural relation graph and semantic relation graph.
Ranked #9 on
Multi-Label Classification
on PASCAL VOC 2007
Universal Face Restoration With Memorized Modulation
Blind face restoration (BFR) is a challenging problem because of the uncertainty of the degradation patterns.
M3TR: Multi-modal Multi-label Recognition with Transformer
Multi-label image recognition aims to recognize multiple objects simultaneously in one image.
Ranked #5 on
Multi-Label Classification
on PASCAL VOC 2007
Video Forgery Detection Using Multiple Cues on Fusion of EfficientNet and Swin Transformer
The first is that capturing the general clue of artifacts is difficult.
Self-supervised Learning for Sequential Recommendation with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
Modeling Dynamic Attributes for Next Basket Recommendation
However, properly integrating them into user interest models is challenging since attribute dynamics can be diverse such as time-interval aware, periodic patterns (etc.
Heterogeneous Relational Complement for Vehicle Re-identification
The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations.
Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing
In this paper, firstly, we propose a new complementary feature enhanced framework, in which the complementary features are learned by several complementary subtasks and then together serve to boost the performance of the primary task.
Ranked #1 on
Image Dehazing
on NH-HAZE
