Search Results for author:
Found 523 papers, 160 papers with code
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint.
Ranked #695 on
Image Classification
on ImageNet
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage.
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.
Ranked #1 on
Semi-Supervised Object Detection
on COCO 10% labeled data
(detector metric)
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
This strategy effectively alleviates the interference between the two tasks of image-text alignment and instruction following and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset.
Ranked #6 on
Visual Question Answering (VQA)
on InfiMM-Eval
Instruction Following
Optical Character Recognition (OCR)
+7
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets
To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint.
TernaryBERT: Distillation-aware Ultra-low Bit BERT
Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks. However, these models are both computation and memory expensive, hindering their deployment to resource-constrained devices.
BinaryBERT: Pushing the Limit of BERT Quantization
In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization.
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
In addition to its large scale, CodeNet has a rich set of high-quality annotations to benchmark and help accelerate research in AI techniques for a variety of critical coding tasks, including code similarity and classification, code translation between a large variety of programming languages, and code performance (runtime and memory) improvement techniques.
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.
Ranked #12 on
Lane Detection
on CurveLanes
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
Ranked #17 on
Fine-Grained Image Classification
on CUB-200-2011
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments.
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
To further enhance CLIP's few-shot capability, CLIP-Adapter proposed to fine-tune a lightweight residual feature adapter and significantly improves the performance for few-shot classification.
Fast Video Shot Transition Localization with Deep Structured Models
In order to train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos.
Ranked #3 on
Camera shot boundary detection
on ClipShots
(using extra training data)
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search.
OpenUE: An Open Toolkit of Universal Extraction from Text
We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.
Ranked #3 on
Joint Entity and Relation Extraction
on WebNLG
PointCLIP: Point Cloud Understanding by CLIP
On top of that, we design an inter-view adapter to better extract the global feature and adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in 2D.
Ranked #3 on
3D Open-Vocabulary Instance Segmentation
on STPLS3D
3D Open-Vocabulary Instance Segmentation
Few-Shot Learning
+6
Dynamic Graph Representation Learning via Self-Attention Networks
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization.
Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods including 3D tracking methods by large margins (5. 4% higher MOTSA and 18 times faster over MOTSFusion) with the near real-time speed (22 FPS).
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+1
PointTrack++ for Effective Online Multi-Object Tracking and Segmentation
In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.
Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds
Current 3D single object tracking approaches track the target based on a feature comparison between the target template and the search area.
Ranked #2 on
Object Tracking
on KITTI
Down to the Last Detail: Virtual Try-on with Detail Carving
However, existing methods can hardly preserve the details in clothing texture and facial identity (face, hair) while fitting novel clothes and poses onto a person.
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach.
Ranked #36 on
Action Recognition
on UCF101
Freeform Body Motion Generation from Speech
Motivated by studies in linguistics, we decompose the co-speech motion into two complementary parts: pose modes and rhythmic dynamics.
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization
Therefore many modified normalization techniques have been proposed, which either fail to restore the performance of BN completely, or have to introduce additional nonlinear operations in inference procedure and increase huge consumption.
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models.
One Million Scenes for Autonomous Driving: ONCE Dataset
To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Can we design an algorithm that is robust in a heterogeneous environment, while being communication efficient and maintaining the best-possible convergence rate?
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
On network configurations with low bandwidth or high latency, D-PSGD can be up to one order of magnitude faster than its well-optimized centralized counterparts.
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains.
Ranked #15 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
AutoBlock: A Hands-off Blocking Framework for Entity Matching
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity.
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.
Model-based Deep Hand Pose Estimation
For the first time, we show that embedding such a non-linear generative process in deep learning is feasible for hand pose estimation.
Unsupervised Person Image Generation with Semantic Parsing Transformation
Firstly, a semantic generative network is proposed to transform between semantic parsing maps, in order to simplify the non-rigid deformation learning.
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples.
A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection
In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset.
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures.
Ranked #29 on
Entity Alignment
on DBP15k zh-en
Class-Aware Contrastive Semi-Supervised Learning
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization.
Scalable Rule-Based Representation Learning for Interpretable Classification
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Learning Interpretable Rules for Scalable Data Representation and Classification
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
To address these limitations, we introduce a novel thought prompting approach called "Everything of Thoughts" (XoT) to defy the law of "Penrose triangle of existing thought paradigms.
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer.
Ranked #1 on
Open-Domain Question Answering
on Quasar
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning.
Ranked #4 on
Open-Domain Question Answering
on Quasar
LVOS: A Benchmark for Long-term Video Object Segmentation
The videos in our LVOS last 1. 59 minutes on average, which is 20 times longer than videos in existing VOS datasets.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery.
Ranked #2 on
Text-based de novo Molecule Generation
on ChEBI-20
Reconstruction Network for Video Captioning
Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstruction network (RecNet) with a novel encoder-decoder-reconstructor architecture, which leverages both the forward (video to sentence) and backward (sentence to video) flows for video captioning.
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection
The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes.
Examining User-Friendly and Open-Sourced Large GPT Models: A Survey on Language, Multimodal, and Scientific GPT Models
Generative pre-trained transformer (GPT) models have revolutionized the field of natural language processing (NLP) with remarkable performance in various tasks and also extend their power to multimodal domains.
Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network
In this paper, we propose to guide the video caption generation with Part-of-Speech (POS) information, based on a gated fusion of multiple representations of input videos.
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
Given the multi-scale feature representations and scale-specific inter-variable dependencies, a multi-scale temporal graph neural network is introduced to jointly model intra-variable dependencies and inter-variable dependencies.
Point2Seq: Detecting 3D Objects as Sequences
We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words.
Learning Efficient Detector with Semi-supervised Adaptive Distillation
ADL enlarges the distillation loss for hard-to-learn and hard-to-mimic samples and reduces distillation loss for the dominant easy samples, enabling distillation to work on the single-stage detector first time, even if the student and the teacher are identical.
ISDNet: Integrating Shallow and Deep Networks for Efficient Ultra-High Resolution Segmentation
The huge burden of computation and memory are two obstacles in ultra-high resolution image segmentation.
Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation
In this paper, we propose MUTR, a Multi-modal Unified Temporal transformer for Referring video object segmentation.
LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement
Accurate extraction of lane graphs relies on precisely estimating vertex and edge information within the DAG.
Translating Images to Road Network:A Non-Autoregressive Sequence-to-Sequence Approach
Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence.
Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement Learning
Intelligent reflecting surface (IRS) is envisioned to change the paradigm of wireless communications from "adapting to wireless channels" to "changing wireless channels".
MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition
In this paper, we propose the Incremental MLTR (IMLTR) task in the context of incremental learning (IL), where different languages are introduced in batches.
Ranked #1 on
Incremental Learning
on MLT17
GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation
We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together.
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph
On the one hand, sparse KGs contain less information, which makes it difficult for the model to choose correct paths.
Adaptive Multi-Teacher Multi-level Knowledge Distillation
Knowledge distillation~(KD) is an effective learning paradigm for improving the performance of lightweight student networks by utilizing additional supervision knowledge distilled from teacher networks.
Heated-Up Softmax Embedding
Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples.
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning
Also, we develop a strong IconQA baseline Patch-TRM that applies a pyramid cross-modal Transformer with input diagram embeddings pre-trained on the icon dataset.
Ranked #1 on
Visual Question Answering (VQA)
on IconQA
HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation
In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation.
Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering
Existing VQA methods mainly adopt the visual attention mechanism to associate the input question with corresponding image regions for effective question answering.
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs.
Knowledge Association with Hyperbolic Knowledge Graph Embeddings
Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations.
Ranked #28 on
Entity Alignment
on DBP15k zh-en
WaterMask: Instance Segmentation for Underwater Imagery
Underwater image instance segmentation is a fundamental and critical step in underwater image analysis and understanding.
Ranked #1 on
Instance Segmentation
on UIIS
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.
Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models
BEV-InMLLM integrates multi-view, spatial awareness, and temporal semantics to enhance MLLMs' capabilities on NuInstruct tasks.
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences.
Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering.
Ranked #1 on
Image Clustering
on Coil-20
(Accuracy metric)
Weakly-Supervised Salient Object Detection Using Point Supervision
Then we develop a transformer-based point-supervised saliency detection model to produce the first round of saliency maps.
OMPQ: Orthogonal Mixed Precision Quantization
Instead of solving a problem of the original integer programming, we propose to optimize a proxy metric, the concept of network orthogonality, which is highly correlated with the loss of the integer programming but also easy to optimize with linear programming.
Entity-Level Text-Guided Image Manipulation
In the image manipulation phase, SeMani adopts a generative model to synthesize new images conditioned on the entity-irrelevant regions and target text descriptions.
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
Specifically, we build a Multi-Relational Item Graph (MRIG) based on all behavior sequences from all sessions, involving target and auxiliary behavior types.
LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images
We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) modules to remote sensing images.
SACANet: scene-aware class attention network for semantic segmentation of remote sensing images
In this paper, we integrate both scene-aware and class attentions to propose a scene-aware class attention network (SACANet) for semantic segmentation of remote sensing images.
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering
To better utilize semantic knowledge in images, we propose a novel framework to learn visual relation facts for VQA.
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization
In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure.
Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation
S2ADet comprises a hyperspectral information decoupling (HID) module, a two-stream feature extraction network, and a one-stage detection head.
Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning
To improve domain-adapted sentiment classification by learning sentiment from the target domain as well, we devise a novel deep adversarial mutual learning approach involving two groups of feature extractors, domain discriminators, sentiment classifiers, and label probers.
Data-Efficient Backdoor Attacks
Recent studies have proven that deep neural networks are vulnerable to backdoor attacks.
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Utilizing these principles, we formulated a kernel for sparse matrix multiplication (SpMM) in GCNs that employs block-level partitioning and combined warp strategy.
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models
Now text-to-image foundation models are widely applied to various downstream image synthesis tasks, such as controllable image generation and image editing, while downstream video synthesis tasks are less explored for several reasons.
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
Multi-modal large language models (MLLMs) have demonstrated remarkable success in vision and visual-language tasks within the natural image domain.
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
To aid this, we present Visualization of Embedding Representations for deBiasing system ("VERB"), an open-source web-based visualization tool that helps the users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties.
Cluster-level Feature Alignment for Person Re-identification
Instance-level alignment is widely exploited for person re-identification, e. g. spatial alignment, latent semantic alignment and triplet alignment.
Ranked #30 on
Person Re-Identification
on DukeMTMC-reID
Points as Queries: Weakly Semi-supervised Object Detection by Points
We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points.
High-order Correlation Preserved Incomplete Multi-view Subspace Clustering
Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation.
Learning Point-Language Hierarchical Alignment for 3D Visual Grounding
This paper presents a novel hierarchical alignment model (HAM) that learns multi-granularity visual and linguistic representations in an end-to-end manner.
PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs
Specifically, in the graph construction flow, we introduce buffer insertion, datapath merging, graph trimming and feature annotation techniques to transform HLS designs into graph-structured data, which encode both intra-operation micro-architectures and inter-operation interconnects annotated with switching activities.
PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
Our dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can advance the development of panoramic segmentation/tracking.
Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain
Currently, under supervised learning, a model pretrained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated the knowledge transfer learning.
Frequency Perception Network for Camouflaged Object Detection
Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment.
SFOD: Spiking Fusion Object Detector
Thereby, we establish state-of-the-art classification results based on SNNs, achieving 93. 7\% accuracy on the NCAR dataset.
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e. g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora.
E2E-LOAD: End-to-End Long-form Online Action Detection
Furthermore, we propose a novel and efficient inference mechanism that accelerates heavy spatial-temporal exploration.
Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation
Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items.
From Dark Matter to Galaxies with Convolutional Networks
In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
This paper presents a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images.
Mesh Saliency: An Independent Perceptual Measure or a Derivative of Image Saliency?
While mesh saliency aims to predict regional importance of 3D surfaces in agreement with human visual perception and is well researched in computer vision and graphics, latest work with eye-tracking experiments shows that state-of-the-art mesh saliency methods remain poor at predicting human fixations.
STNet: Spatial and Temporal feature fusion network for change detection in remote sensing images
As an important task in remote sensing image analysis, remote sensing change detection (RSCD) aims to identify changes of interest in a region from spatially co-registered multi-temporal remote sensing images, so as to monitor the local development.
BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities
Collaborative perception enables agents to share complementary perceptual information with nearby agents.
Lightweight super resolution network for point cloud geometry compression
This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network.
Tensor-Based Multi-View Block-Diagonal Structure Diffusion for Clustering Incomplete Multi-View Data
In this paper, we propose a novel incomplete multi-view clustering method, in which a tensor nuclear norm regularizer elegantly diffuses the information of multi-view block-diagonal structure across different views.
Normalization of Language Embeddings for Cross-Lingual Alignment
Learning a good transfer function to map the word vectors from two languages into a shared cross-lingual word vector space plays a crucial role in cross-lingual NLP.
Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems
The SCD framework incorporates the symbolic tree to explicably represent the complicated student-exercise interaction function, and utilizes gradient-based optimization methods to effectively learn the student and exercise parameters.
Augmentation Pathways Network for Visual Recognition
Experimental results on ImageNet demonstrate the compatibility and effectiveness on a much wider range of augmentations, while consuming fewer parameters and lower computational costs at inference time.
Improving Relation Extraction with Relational Paraphrase Sentences
In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.
A Practical Two-stage Ranking Framework for Cross-market Recommendation
The proposed solution of our team WSDM_Coggle_ is selected as the second place submission.
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Then we derive two specific attention modules, named SpatialFormer Semantic Attention (SFSA) and SpatialFormer Target Attention (SFTA), to enhance the target object regions while reduce the background distraction.
SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection
Despite significant progress in shadow detection, current methods still struggle with the adverse impact of background color, which may lead to errors when shadows are present on complex backgrounds.
Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation
To tackle the unsupervised domain adaptation problem, we explore the possibilities to generate high-quality labels as proxy labels to supervise the training on target data.
A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology.
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
Experiments show that Wukong can serve as a promising Chinese pre-training dataset and benchmark for different cross-modal learning methods.
Ranked #6 on
Image Retrieval
on MUGE Retrieval
Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection
By integrating complementary information from RGB image and depth map, the ability of salient object detection (SOD) for complex and challenging scenes can be improved.
MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations
How to capture the semantic variations associated with the changed and unchanged regions from the patch-level annotations to obtain promising change results is the critical challenge for the weakly supervised change detection task.
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD) framework for optimizing deep neural networks.
From Dark Matter to Galaxies with Convolutional Neural Networks
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations.
Effective Few-Shot Named Entity Linking by Meta-Learning
In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations.
Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning
Subsequently, a rollback algorithm is proposed to recover the high-dimensional design space so that higher pruning accuracy can be obtained.
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions.
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first.
Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging
Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics.
Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering
Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system.
Learning chemical reaction networks from trajectory data
We develop a data-driven method to learn chemical reaction networks from trajectory data.
Optimization and Control 92C42, 62M86
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
In particular, we propose a novel joint-training framework to train plain CNN by leveraging the gradients of the ResNet counterpart.
Language-Driven Anchors for Zero-Shot Adversarial Robustness
Previous researches mainly focus on improving adversarial robustness in the fully supervised setting, leaving the challenging domain of zero-shot adversarial robustness an open question.
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
FATA-Trans is field- and time-aware for sequential tabular data.
Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection
To tackle the confirmation bias from incorrect pseudo labels of minority classes, the class-rebalancing sampling module resamples unlabeled data following the guidance of the gradient-based reweighting module.
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks.
Staleness-aware Async-SGD for Distributed Deep Learning
Deep neural networks have been shown to achieve state-of-the-art performance in several machine learning tasks.
Unsupervised Multi-View CNN for Salient View Selection of 3D Objects and Scenes
We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named by us view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class.
Ergodic SDEs on submanifolds and related numerical sampling schemes
By Birkhoff's ergodic theorem, one approach to estimate the mean value is to compute the time average along an infinitely long trajectory of an ergodic diffusion process on the level set whose invariant measure is {\mu}.
Probability 60J60, 53C17
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension
Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media.
Understanding Adversarial Robustness from Feature Maps of Convolutional Layers
Our theoretical analysis discovers that larger convolutional feature maps before average pooling can contribute to better resistance to perturbations, but the conclusion is not true for max pooling.
Dual Representation Learning for One-Step Clustering of Multi-View Data
Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole.
Knowledge-aware Collaborative Filtering with Pre-trained Language Model for Personalized Review-based Rating Prediction
For the first issue, to utilize rich knowledge, KCF-PLM develops a transformer network to model the interactions of the extracted aspects w. r. t.
Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning
Drawing inspiration from this, we devise a novel Behavior Augmented Relevance Learning model for Alipay Search (BARL-ASe) that leverages neighbor queries of target item and neighbor items of target query to complement target query-item semantic matching.
Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records.
Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration
While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding the complexity and instability associated with the Proximal Policy Optimization (PPO) algorithm, proposing a series of order-based calibration methods as viable alternatives.
BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling
Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries.
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis.
Boosting up Scene Text Detectors with Guided CNN
Deep CNNs have achieved great success in text detection.
Knowledge Base Relation Detection via Multi-View Matching
Relation detection is a core component for Knowledge Base Question Answering (KBQA).
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.
Chinese Named Entity Recognition
named-entity-recognition
+2
SEE: Syntax-aware Entity Embedding for Neural Relation Extraction
First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU.
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training
Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100 of TeraOps/s of computational capacity) is expected to be severely communication constrained.
DeepSkeleton: Skeleton Map for 3D Human Pose Regression
For the first time, we show that training regression network from skeleton map alone is capable of meeting the performance of state-of-theart 3D human pose estimation works.
GaDei: On Scale-up Training As A Service For Deep Learning
By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e. g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers.
Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
Learning to remember long sequences remains a challenging task for recurrent neural networks.
Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition
Our LMM model can provide an end-to-end approach for jointly learning: (a) the deep networks to extract more discriminative deep features for image and object class representation; (b) the tree classifier for recognizing large numbers of object classes hierarchically; and (c) the visual hierarchy adaptation for achieving more accurate indexing of large numbers of object classes hierarchically.
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition
In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task spaces), e. g., such base deep CNNs are trained to recognize different subsets of tens of thousands of atomic object classes.
Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting
This paper describes the first attempt to nowcast storm initiation, growth, and advection simultaneously under a deep learning framework using multi-source meteorological data.
Low-rank Label Propagation for Semi-supervised Learning with 100 Millions Samples
The success of semi-supervised learning crucially relies on the scalability to a huge amount of unlabelled data that are needed to capture the underlying manifold structure for better classification.
Learning Compact Appearance Representation for Video-based Person Re-Identification
This paper presents a novel approach for video-based person re-identification using multiple Convolutional Neural Networks (CNNs).
PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning
To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately.
Integrating Topic Models and Latent Factors for Recommendation
In this work, we consider the problem of hotel recommendation for travel planning services by integrating the location information and the user's preference for recommendation.
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions.
Ranked #49 on
Question Answering
on SQuAD1.1 dev
Deep Kinematic Pose Regression
In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation.
Ranked #307 on
3D Human Pose Estimation
on Human3.6M
Empirical Study on Deep Learning Models for Question Answering
In this paper we explore deep learning models with memory component or attention mechanism for question answering task.
Structured Memory for Neural Turing Machines
Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning.
Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis
Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene.
Recognizing Extended Spatiotemporal Expressions by Actively Trained Average Perceptron Ensembles
We collected and annotated data set by querying commercial web searches API with such spatiotemporal expressions as were missed by state-of-the- art parsers.
Exploring Metaphorical Senses and Word Representations for Identifying Metonyms
A metonym is a word with a figurative meaning, similar to a metaphor.
Conditional Restricted Boltzmann Machines for Cold Start Recommendations
Restricted Boltzman Machines (RBMs) have been successfully used in recommender systems.
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation
Prior relevant studies recommend treatments either use supervised learning (e. g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e. g. maximizing evaluation signal which indicates cumulative reward from survival rates).
Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining
Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks.
Temporal Sequence Distillation: Towards Few-Frame Action Recognition in Videos
Secondly, TSD significantly reduces the computations to run video action recognition with compressed frames on the cloud, while maintaining high recognition accuracies.
Solving Pictorial Jigsaw Puzzle by Stigmergy-inspired Internet-based Human Collective Intelligence
And through feedback, each player is provided with personalized feedback information based on the current COG and the player's exploration result, in order to accelerate his/her puzzle-solving process.
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition
Video action recognition, a critical problem in video understanding, has been gaining increasing attention.
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Reinforcement learning agents need exploratory behaviors to escape from local optima.
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding
This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly.
Learning to Decompose Compound Questions with Reinforcement Learning
Our model consists of two parts: (i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and (ii) three independent simple-question answerers that classify the corresponding relations for each simple question.
Deep Boosting of Diverse Experts
In this paper, a deep boosting algorithm is developed to learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs (base experts) with diverse capabilities, e. g., these base deep CNNs are sequentially trained to recognize a set of object classes in an easy-to-hard way according to their learning complexities.
Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment
More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image.
MSR: Multi-Scale Shape Regression for Scene Text Detection
State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes.
Weakly Supervised Semantic Segmentation for Social Images
Image semantic segmentation is the task of partitioning image into several regions based on semantic concepts.
Binarized Mode Seeking for Scalable Visual Pattern Discovery
Second, we further extend bMS to a more general form, namely contrastive binary mean shift (cbMS), which maximizes the contrastive density in binary space, for finding informative patterns that are both frequent and discriminative for the dataset.
Multiple Granularity Descriptors for Fine-Grained Categorization
Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.
A Spatio-Temporal Appearance Representation for Viceo-Based Pedestrian Re-Identification
Pedestrian re-identification is a difficult problem due to the large variations in a person's appearance caused by different poses and viewpoints, illumination changes, and occlusions.
VrR-VG: Refocusing Visually-Relevant Relationships
Relationships encode the interactions among individual instances, and play a critical role in deep visual scene understanding.
Hierarchical Photo-Scene Encoder for Album Storytelling
In this paper, we propose a novel model with a hierarchical photo-scene encoder and a reconstructor for the task of album storytelling.
Ranked #5 on
Image-guided Story Ending Generation
on VIST-E
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available.
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.
Distributed Deep Learning Strategies For Automatic Speech Recognition
We show that we can train the LSTM model using ADPSGD in 14 hours with 16 NVIDIA P100 GPUs to reach a 7. 6% WER on the Hub5- 2000 Switchboard (SWB) test set and a 13. 1% WER on the CallHome (CH) test set.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks
Selfie and cartoon are two popular artistic forms that are widely presented in our daily life.
Anti-Confusing: Region-Aware Network for Human Pose Estimation
In this work, we propose a novel framework named Region-Aware Network (RANet), which learns the ability of anti-confusing in case of heavy occlusion, nearby person and symmetric appearance, for human pose estimation.
Accelerated Target Updates for Q-learning
This paper studies accelerations in Q-learning algorithms.
Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesis
Early and accurate congestion estimation is of great benefit to guide the optimization in HLS and improve the efficiency of implementation.
