Search Results for author:
Found 210 papers, 83 papers with code
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.
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.
StarCoder 2 and The Stack v2: The Next Generation
Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
Ranked #24 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 #1 on
Facial Expression Recognition (FER)
on RAF-DB
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.
Position Paper: An Integrated Perspective on Data, Metrics, and Methodology for Deep Time-Series Forecasting
Deep time-series forecasting plays an integral role in numerous practical applications.
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 Unbalanced Motion: Part-Decoupling Network for Video Portrait Segmentation
That is, motion of different parts of the portraits is unbalanced.
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.
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 #42 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.
Missing Data Imputation with Graph Laplacian Pyramid Network
Data imputation is a prevalent and important task due to the ubiquitousness of missing data.
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 #7 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.
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.
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.
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
Here, we regard an expressive face as the comprehensive result of a set of facial muscle movements on one's poker face (i. e., emotionless face), inspired by Facial Action Coding System.
Ranked #5 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 #15 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 #5 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 #8 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 #4 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
RGB-D Salient Object Detection with Ubiquitous Target Awareness
To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales.
Ranked #10 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
Pose-guided Inter- and Intra-part Relational Transformer for Occluded Person Re-Identification
Therefore, we propose a Pose-guided inter-and intra-part relational transformer (Pirt) for occluded person Re-Id, which builds part-aware long-term correlations by introducing transformers.
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
This observation leads to more explanations of the CNN's generalization behaviors in both robustness to common perturbations and out-of-distribution detection, and motivates a new perspective on data augmentation designed by re-combing the phase spectrum of the current image and the amplitude spectrum of the distracter image.
Ranked #7 on
Out-of-Distribution Detection
on CIFAR-10
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues.
Mixture of Linear Models Co-supervised by Deep Neural Networks
Our main idea is a mixture of discriminative models that is trained with the guidance from a DNN.
Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation
Given an image, we employ an Hourglass Network to infer all the keypoints from different persons indiscriminately as well as the guiding offsets connecting the adjacent keypoints belonging to the same persons.
Ranked #10 on
Pose Estimation
on CrowdPose
FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains
In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains.
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition
Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects.
Ranked #28 on
Fine-Grained Image Classification
on CUB-200-2011
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection
Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i. e. with object bounding boxes) to train object detectors for target domains that are weakly labeled (i. e. with image-level tags).
Ranked #4 on
Weakly Supervised Object Detection
on Clipart1k
Exploring Driving-aware Salient Object Detection via Knowledge Transfer
In this network, we construct an attentionbased knowledge transfer module to make up the knowledge difference.
Epsilon Consistent Mixup: Structural Regularization with an Adaptive Consistency-Interpolation Tradeoff
In this paper we propose $\epsilon$-Consistent Mixup ($\epsilon$mu).
Functional and spatial rewiring jointly generate convergent-divergent units in self-organizing networks
The prominence of minimizing wiring distance in the dynamic evolution of the network determines the extent to which the core is encapsulated from the rest of the network, i. e., the context-sensitivity of its computations.
Structural Similarity of Boundary Conditions and an Efficient Local Search Algorithm for Goal Conflict Identification
By taking experiments on a set of cases, we show that LOGION effectively exploits the structural similarity of BCs.
Mask-GVAE: Blind Denoising Graphs via Partition
We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs.
Graph Autoencoders with Deconvolutional Networks
Recent studies have indicated that Graph Convolutional Networks (GCNs) act as a \emph{low pass} filter in spectral domain and encode smoothed node representations.
Incorporating Causal Effects into Deep Learning Predictions on EHR Data
Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality.
Learning Open Set Network with Discriminative Reciprocal Points
In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data.
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations is less understood.
Ranked #2 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
TUTA: Tree-based Transformers for Generally Structured Table Pre-training
First, we devise a unified tree-based structure, called a bi-dimensional coordinate tree, to describe both the spatial and hierarchical information of generally structured tables.
Dirichlet Graph Variational Autoencoder
In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors.
DanceIt: Music-inspired Dancing Video Synthesis
To explore the relationship between music and dance movements, we propose a cross-modal alignment module that focuses on dancing video clips, accompanied on pre-designed music, to learn a system that can judge the consistency between the visual features of pose sequences and the acoustic features of music.
Intrinsic Relationship Reasoning for Small Object Detection
In this paper, we propose a novel context reasoning approach for small object detection which models and infers the intrinsic semantic and spatial layout relationships between objects.
Cooperative Bi-path Metric for Few-shot Learning
Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples.
Permutation-based tests for discontinuities in event studies
Under a high-level condition that the observed data can be coupled by a collection of conditionally independent variables, we establish the asymptotic validity of the permutation test, allowing the sizes of the local subsamples to be either be fixed or grow to infinity.
SEKD: Self-Evolving Keypoint Detection and Description
Researchers have attempted utilizing deep neural network (DNN) to learn novel local features from images inspired by its recent successes on a variety of vision tasks.
Is Depth Really Necessary for Salient Object Detection?
To solve this, many recent RGBD-based networks are proposed by adopting the depth map as an independent input and fuse the features with RGB information.
Ranked #1 on
RGB-D Salient Object Detection
on RGBD135
Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network
In this paper, we propose a novel and more flexible GCN model with a feature encoder that adaptively updates the adjacency matrix during learning and demonstrate that this model design leads to improved performance.
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously.
Adversarial Attack on Community Detection by Hiding Individuals
It has been demonstrated that adversarial graphs, i. e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks.
Single Image Dehazing Using Ranking Convolutional Neural Network
To obtain effective features for single image dehazing, this paper presents a novel Ranking Convolutional Neural Network (Ranking-CNN).
Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss
Through these two attentions, we use the Purificatory Mechanism to impose strict weights with different regions of the whole salient objects and purify results from hard-to-distinguish regions, thus accurately predicting the locations and details of salient objects.
Zooming into Face Forensics: A Pixel-level Analysis
The stunning progress in face manipulation methods has made it possible to synthesize realistic fake face images, which poses potential threats to our society.
Deep Variable-Block Chain with Adaptive Variable Selection
In this paper, we propose a framework that imposes on blocks of variables a chain structure obtained by step-wise greedy search so that the DNN architecture can leverage the constructed grid.
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one.
Ranked #9 on
Keypoint Detection
on COCO test-dev
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems
We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems.
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning
By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space.
Word embedding re-examined: is the symmetrical factorization optimal?
Such linear transformation will result in these good properties.
Near-Zero-Cost Differentially Private Deep Learning with Teacher Ensembles
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.
Exploring Reciprocal Attention for Salient Object Detection by Cooperative Learning
Typically, objects with the same semantics are not always prominent in images containing different backgrounds.
Distortion-adaptive Salient Object Detection in 360$^\circ$ Omnidirectional Images
Moreover, benchmarking results of the proposed baseline approach and other methods on 360$^\circ$ SOD dataset show the proposed dataset is very challenging, which also validate the usefulness of the proposed dataset and approach to boost the development of SOD on 360$^\circ$ omnidirectional scenes.
Learning Local Feature Descriptor with Motion Attribute for Vision-based Localization
For better performance, the features used for open-loop localization are required to be short-term globally static, and the ones used for re-localization or loop closure detection need to be long-term static.
Cartoon Face Recognition: A Benchmark Dataset
Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications.
Combining Reinforcement Learning and Configuration Checking for Maximum k-plex Problem
Based on the BLP and DTCC, we develop a local search algorithm named BDCC and improve it by a hyperheuristic strategy.
Private Deep Learning with Teacher Ensembles
Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information.
Predicting Path Failure In Time-Evolving Graphs
Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.
Downhole Track Detection via Multiscale Conditional Generative Adversarial Nets
A multiscale shared convolution structure is adopted in the discriminator network to further supervise training the generator.
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation
In recent years, the generation of conversation content based on deep neural networks has attracted many researchers.
Image Quality Assessment for Omnidirectional Cross-reference Stitching
Along with the development of virtual reality (VR), omnidirectional images play an important role in producing multimedia content with immersive experience.
Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency
In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model.
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e. g., a user group in the above example.
Ranked #10 on
Graph Classification
on D&D
Ultrafast Video Attention Prediction with Coupled Knowledge Distillation
To address these issues, we design an extremely light-weight network with ultrafast speed, named UVA-Net.
Harmonic Unpaired Image-to-image Translation
The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations.
Selectivity or Invariance: Boundary-aware Salient Object Detection
In this network, the feature selectivity at boundaries is enhanced by incorporating a boundary localization stream, while the feature invariance at interiors is guaranteed with a complex interior perception stream.
Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation
In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively.
Visual Attention on the Sun: What Do Existing Models Actually Predict?
Visual attention prediction is a classic problem that seems to be well addressed in the deep learning era.
Complementary Segmentation of Primary Video Objects with Reversible Flows
Segmenting primary objects in a video is an important yet challenging problem in computer vision, as it exhibits various levels of foreground/background ambiguities.
Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction
After that, the visual saliency knowledge encoded in the most representative paths is selected and aggregated to improve the capability of MM-Net in predicting spatial saliency in aerial scenarios.
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning
To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule the rapid deployment of volunteers to rescue victims in dynamic settings.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Forecasting People's Needs in Hurricane Events from Social Network
Our proposed sequence to sequence method forecast people's needs more successfully than either of the other models.
Lifted Proximal Operator Machines
LPOM is block multi-convex in all layer-wise weights and activations.
Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification
In this manner, the influence of bias and noise in the web data can be gradually alleviated, leading to the steadily improving performance of URNet.
AirDialogue: An Environment for Goal-Oriented Dialogue Research
However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated.
A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters
When the support points of the barycenter are pre-specified, this problem can be modeled as a linear programming (LP) problem whose size can be extremely large.
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild
A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated.
Learning a Saliency Evaluation Metric Using Crowdsourced Perceptual Judgments
Due to the effectiveness of the learned metric, it also can be used to facilitate the development of new models for fixation prediction.
Collaborative Annotation of Semantic Objects in Images with Multi-granularity Supervisions
In this paper, we propose a human-agent collaborative annotation approach that can efficiently generate per-pixel masks of semantic objects in tagged images with multi-granularity supervisions.
Hierarchical Deep Co-segmentation of Primary Objects in Aerial Videos
Primary object segmentation plays an important role in understanding videos generated by unmanned aerial vehicles.
Image Co-segmentation via Multi-scale Local Shape Transfer
Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category.
Detecting Comma-shaped Clouds for Severe Weather Forecasting using Shape and Motion
Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms.
Aggregated Wasserstein Metric and State Registration for Hidden Markov Models
This distance quantifies the dissimilarity of GMM-HMMs by measuring both the difference between the two marginal GMMs and that between the two transition matrices.
Embedding 3D Geometric Features for Rigid Object Part Segmentation
One stream, named AppNet, is to extract 2D appearance features from the input image.
Look, Perceive and Segment: Finding the Salient Objects in Images via Two-Stream Fixation-Semantic CNNs
Toward this end, this paper proposes two-stream fixation-semantic CNNs, whose architecture is inspired by the fact that salient objects in complex images can be unambiguously annotated by selecting the pre-segmented semantic objects that receive the highest fixation density in eye-tracking experiments.
Primary Video Object Segmentation via Complementary CNNs and Neighborhood Reversible Flow
By applying CCNN on each video frame, the spatial foregroundness and backgroundness maps can be initialized, which are then propagated between various frames so as to segment primary video objects and suppress distractors.
What Is and What Is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors
Finding what is and what is not a salient object can be helpful in developing better features and models in salient object detection (SOD).
Detecting Masked Faces in the Wild With LLE-CNNs
Detecting masked faces (i. e., faces with occlusions) is a challenging task due to two main reasons: 1)the absence of large datasets of masked faces, and 2)the absence of facial cues from the masked regions.
Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering
The new clustering method is easy to use and consistently outperforms other methods on a variety of data sets.
CT Image Reconstruction in a Low Dimensional Manifold
In this paper, we propose a CT image reconstruction method based on the prior knowledge of the low-dimensional manifold of CT image.
Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning
Low-rank structures play important role in recent advances of many problems in image science and data science.
Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data
We proposed a probabilistic approach to joint modeling of participants' reliability and humans' regularity in crowdsourced affective studies.
AutoScaler: Scale-Attention Networks for Visual Correspondence
We propose AutoScaler, a scale-attention network to explicitly optimize this trade-off in visual correspondence tasks.
A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection
Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliency-guided stacked autoencoders.
