Search Results for author:
Found 71 papers, 22 papers with code
Non-Autoregressive Chinese ASR Error Correction with Phonological Training
Automatic Speech Recognition (ASR) is an efficient and widely used input method that transcribes speech signals into text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network
To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities.
RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation
Inspired by the shadowing factor in the physical propagation model, we integrate obstacle-related factors to express the obstacle-induced signal stochastic decay.
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model.
ProTCT: Projection quantification and fidelity constraint integrated deep reconstruction for Tangential CT
This work contributes to exploring the sampling condition and improving the slice quality of TCT, further promoting the application of large view field CT imaging.
Nonlinear Motion-Guided and Spatio-Temporal Aware Network for Unsupervised Event-Based Optical Flow
Meanwhile, we propose a nonlinear motion compensation loss that utilizes the accurate nonlinear motion between events to improve the unsupervised learning of our network.
Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval
The model uses a co-alignment strategy to generate codes optimized for maximizing retrieval efficiency.
Optimal Brain Apoptosis
The increasing complexity and parameter count of Convolutional Neural Networks (CNNs) and Transformers pose challenges in terms of computational efficiency and resource demands.
FlexControl: Computation-Aware ControlNet with Differentiable Router for Text-to-Image Generation
To address this gap, we propose FlexControl, a novel framework that copies all diffusion blocks during training and employs a trainable gating mechanism to dynamically select which blocks to activate at each denoising step.
4D-CS: Exploiting Cluster Prior for 4D Spatio-Temporal LiDAR Semantic Segmentation
Semantic segmentation of LiDAR points has significant value for autonomous driving and mobile robot systems.
EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera
Point clouds or depth images captured by current RGB-D cameras often suffer from low resolution, rendering them insufficient for applications such as 3D reconstruction and robots.
Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting
As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research.
LOMA: Language-assisted Semantic Occupancy Network via Triplane Mamba
Nowadays, conventional methods usually project the image-based vision features to 3D space and learn the geometric information through the attention mechanism, enabling the 3D semantic occupancy prediction.
StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding
In this paper, we introduce StreamingBench, the first comprehensive benchmark designed to evaluate the streaming video understanding capabilities of MLLMs.
Towards Graph Prompt Learning: A Survey and Beyond
Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, image recognition, and multimodal retrieval.
EV-MGDispNet: Motion-Guided Event-Based Stereo Disparity Estimation Network with Left-Right Consistency
In this paper, we propose EV-MGDispNet, a novel event-based stereo disparity estimation method.
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce
Most existing query intent classification methods rely on the users' click behavior as a supervised signal to construct training samples.
StreamMOS: Streaming Moving Object Segmentation with Multi-View Perception and Dual-Span Memory
Specifically, we utilize a short-term memory to convey historical features, which can be regarded as spatial prior of moving objects and adopted to enhance current inference by temporal fusion.
FlowTrack: Point-level Flow Network for 3D Single Object Tracking
Specifically, by estimating the flow for each point in the target, our method could capture the local motion details of target, thereby improving the tracking performance.
SyROCCo: Enhancing Systematic Reviews using Machine Learning
The application of ML techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy.
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories
Our experiments reveal these LLMs' coding abilities in real-world code repositories.
Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling
First, we raise the scaling law between batch sizes and optimal learning rates in the sign of gradient case, in which we prove that the optimal learning rate first rises and then falls as the batch size increases.
Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images.
Graph Enhanced Reinforcement Learning for Effective Group Formation in Collaborative Problem Solving
The study opens new avenues for exploring the application of graph theory and reinforcement learning in social and behavioral sciences, highlighting the potential for empirical validation in future work.
EventRPG: Event Data Augmentation with Relevance Propagation Guidance
Based on this, we propose EventRPG, which leverages relevance propagation on the spiking neural network for more efficient augmentation.
Ranked #1 on
Action Recognition
on SL-Animals
SeqTrack3D: Exploring Sequence Information for Robust 3D Point Cloud Tracking
Most existing methods perform tracking between two consecutive frames while ignoring the motion patterns of the target over a series of frames, which would cause performance degradation in the scenes with sparse points.
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
Based on the insight of graph pre-training, we propose to bridge the graph signal gap and the graph structure gap with learnable prompts in the spectral space.
Model Composition for Multimodal Large Language Models
In this paper, we propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model.
Spike-EVPR: Deep Spiking Residual Network with Cross-Representation Aggregation for Event-Based Visual Place Recognition
This module is designed to extract features shared between the two representations and features specific to each.
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations
To tackle the above issues, we propose a neural Spatial-Temporal Delay Differential Equation model, namely STDDE.
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance
Multi-agent generative adversarial imitation learning (MAGAIL) allows multi-AUV to learn from expert demonstration instead of pre-defined reward functions, but suffers from the deficiency of requiring optimal demonstrations and not surpassing provided expert demonstrations.
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.
Motion-to-Matching: A Mixed Paradigm for 3D Single Object Tracking
3D single object tracking with LiDAR points is an important task in the computer vision field.
Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search
To overcome this challenge, in this paper, we propose knowledge condensation (KC), a simple yet effective knowledge distillation framework to boost the classification performance of the online FastText model under strict low latency constraints.
STTracker: Spatio-Temporal Tracker for 3D Single Object Tracking
Previous methods usually input the last two frames and use the predicted box to get the template point cloud in previous frame and the search area point cloud in the current frame respectively, then use similarity-based or motion-based methods to predict the current box.
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling
Most existing topic models rely on bag-of-words (BOW) representation, which limits their ability to capture word order information and leads to challenges with out-of-vocabulary (OOV) words in new documents.
EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss
In this paper, we propose EV-MGRFlowNet, an unsupervised event-based optical flow estimation pipeline with motion-guided recurrent networks using a hybrid motion-compensation loss.
MMF-Track: Multi-modal Multi-level Fusion for 3D Single Object Tracking
To overcome the limitations of geometry matching, we propose a Multi-modal Multi-level Fusion Tracker (MMF-Track), which exploits the image texture and geometry characteristic of point clouds to track 3D target.
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.
A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System
Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively.
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction
In this paper, we propose a few-shot anomaly detection strategy that works in a low-data regime and can generalize across products at no cost.
Ranked #12 on
Anomaly Detection
on MPDD
FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events
To our knowledge, this is the first end-to-end network that goes beyond the existing event-based and frame-based SOTA methods to fuse frame and events directly for VPR.
Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking
Meanwhile, the encoder applies the attention on multi-scale features to compensate for the lack of information caused by the sparsity of point cloud and the single scale of features.
Real-time 3D Single Object Tracking with Transformer
PTT module in the voting stage could model the interactions among point patches, which learns context-dependent features.
A New Adjacency Matrix Configuration in GCN-based Models for Skeleton-based Action Recognition
Comprehensive experimental results and analysis reveals that 1) the most widely used human natural skeleton structure adjacency matrix is unsuitable in skeleton-based action recognition; 2) The proposed adjacency matrix is superior in model performance, noise robustness and transferability.
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes
For visual representation, a representation driven by a combination of the imageaugmented forward dynamics and the reward is acquired.
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media
Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
3D Object Tracking with Transformer
By using cross-attention, the transformer decoder fuses features and includes more target cues into the current point cloud feature to compute the region attentions, which makes the similarity computing more efficient.
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds
Our code is open-sourced for the robotics community at https://github. com/shanjiayao/PTT.
3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud
Then, to fuse the information of features in the two branches and obtain their similarity, we propose two cross correlation modules, named Pointcloud-wise and Point-wise respectively.
Deep Differential Amplifier for Extractive Summarization
In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework.
Implementing an Improved Test of Matrix Rank in Stata
We develop a Stata command, bootranktest, for implementing the matrix rank test of Chen and Fang (2019) in linear instrumental variable regression models.
A Unifying Framework for Testing Shape Restrictions
This paper makes the following original contributions.
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
However, the representation ability of such models is limited due to: (1) shallow GNNs are incapable to capture long-range spatial correlations, (2) only spatial connections are considered and a mass of semantic connections are ignored, which are of great importance for a comprehensive understanding of traffic networks.
Ranked #5 on
Traffic Prediction
on LargeST
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection
Non-Maximum Suppression (NMS) is essential for object detection and affects the evaluation results by incorporating False Positives (FP) and False Negatives (FN), especially in crowd occlusion scenes.
Ranked #6 on
Pedestrian Detection
on Caltech
A Query-Driven Topic Model
Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus.
Improved Signed Distance Function for 2D Real-time SLAM and Accurate Localization
In this paper, we propose an improved Signed Distance Function (SDF) for both 2D SLAM and pure localization to improve the accuracy of mapping and localization.
TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting
Further analysis indicates that the locality and globality of the traffic networks are critical to traffic flow prediction and the proposed TSSRGCN model can adapt to the various temporal traffic patterns.
Event-VPR: End-to-End Weakly Supervised Network Architecture for Event-based Visual Place Recognition
In this paper, we propose an end-to-end visual place recognition network for event cameras, which can achieve good place recognition performance in challenging environments.
Inference for Large-Scale Linear Systems with Known Coefficients
This paper considers the problem of testing whether there exists a non-negative solution to a possibly under-determined system of linear equations with known coefficients.
Deep Patch-based Human Segmentation
3D human segmentation has seen noticeable progress in re-cent years.
OpenArray v1.0: a simple operator library for the decoupling of ocean modeling and parallel computing
In this work, we design a simple computing library to bridge the gap and decouple the work of ocean modeling from parallel computing.
A Projection Framework for Testing Shape Restrictions That Form Convex Cones
Based on a monotonicity property afforded by such a geometric structure, we construct a bootstrap procedure that, unlike many studies in nonstandard settings, dispenses with estimation of local parameter spaces, and the critical values are obtained in a way as simple as computing the test statistic.
Precision annealing Monte Carlo methods for statistical data assimilation and machine learning
In this paper, we develop a systematic formulation based on Monte Carlo sampling to achieve such information transfer.
DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image
Recovering 3D human body shape and pose from 2D images is a challenging task due to high complexity and flexibility of human body, and relatively less 3D labeled data.
Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing
We proceed from a scalar time series $s(t_n); t_n = t_0 + n \Delta t$ and using methods of nonlinear time series analysis show how to produce a $D_E > 1$ dimensional time delay embedding space in which the time series has no false neighbors as does the observed $s(t_n)$ time series.
Joint Entity Linking with Deep Reinforcement Learning
Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base.
Ranked #6 on
Entity Disambiguation
on AIDA-CoNLL
Parallel and Scalable Heat Methods for Geodesic Distance Computation
In this paper, we propose a parallel and scalable approach for geodesic distance computation on triangle meshes.
Graphics
RGB-D SLAM in Dynamic Environments Using Point Correlations
This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups.
Efficient Image Categorization with Sparse Fisher Vector
In object recognition, Fisher vector (FV) representation is one of the state-of-art image representations ways at the expense of dense, high dimensional features and increased computation time.
