Search Results for author:
Found 51 papers, 29 papers with code
Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection
Although sparse-point methods lower computational load and maintain high accuracy in complex lane geometries, current methods fail to fully leverage the geometric structure of lanes in both lane geometry representations and model design.
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
Given the large volume of side information from different modalities, multimodal recommender systems have become increasingly vital, as they exploit richer semantic information beyond user-item interactions.
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning
To this end, we propose a plug-and-play modification by incorporating class-aware contrastive learning (CL).
Unleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations
Despite advancements in individual retrieval methods, multi-channel fusion, the process of efficiently merging multi-channel retrieval results, remains underexplored.
Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis
In the second stage, LoFI leverages a pre-trained language model with a novel prompt-based tuning method to extract fine-grained information of interest from the collected logs.
Contextualized Data-Wrangling Code Generation in Computational Notebooks
With CoCoMine, we construct CoCoNote, a dataset containing 58, 221 examples for Contextualized Data-wrangling Code generation in Notebooks.
From 2015 to 2023: How Machine Learning Aids Natural Product Analysis
In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity and volume of data generated in contemporary research endeavors.
A Comprehensive Survey on Retrieval Methods in Recommender Systems
By detailing the retrieval stage, which is fundamental for effective recommendation, this survey aims to bridge the existing knowledge gap and serve as a cornerstone for researchers interested in optimizing this critical component of cascade recommender systems.
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection
Current unsupervised anomaly detection approaches perform well on public datasets but struggle with specific anomaly types due to the domain gap between pre-trained feature extractors and target-specific domains.
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
RAR consists of two key sub-modules, which synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations.
Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach
We also share our experience in deploying COLA in our real-world cloud system, Cloud X.
FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems
To date, FaultProfIT has analyzed 10, 000+ incidents from 30+ cloud services, successfully revealing several fault trends that have informed system improvements.
Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection
3D object Detection with LiDAR-camera encounters overfitting in algorithm development which is derived from the violation of some fundamental rules.
Distillation Improves Visual Place Recognition for Low Quality Images
Real-time visual localization often utilizes online computing, for which query images or videos are transmitted to remote servers for visual place recognition (VPR).
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation.
Log-based Anomaly Detection based on EVT Theory with feedback
System logs play a critical role in maintaining the reliability of software systems.
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure.
Ranked #2 on
Hierarchical Multi-label Classification
on WOS
Hierarchical Multi-label Classification
MUlTI-LABEL-ClASSIFICATION
+2
Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation
To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance.
BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment
We offer an example of deployment to the TensorRT backend in branch dev2. 0 and show how fast the BEVDet paradigm can be processed on it.
Imperceptible Adversarial Attack via Invertible Neural Networks
Adding perturbations via utilizing auxiliary gradient information or discarding existing details of the benign images are two common approaches for generating adversarial examples.
CodeExp: Explanatory Code Document Generation
Our experiments show that (1) our refined training dataset lets models achieve better performance in the explanation generation tasks compared to larger unrefined data (15x larger), and (2) fine-tuned models can generate well-structured long docstrings comparable to human-written ones.
Execution-based Evaluation for Data Science Code Generation Models
Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions.
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA
However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem.
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems
The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption.
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
Specifically, BEVerse first performs shared feature extraction and lifting to generate 4D BEV representations from multi-timestamp and multi-view images.
Ranked #15 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline
To the best of our knowledge, this is the first large-scale dataset for gait recognition in the wild.
WebFace260M: A Benchmark for Million-Scale Deep Face Recognition
For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively.
HFT: Lifting Perspective Representations via Hybrid Feature Transformation
In order to reap the benefits and avoid the drawbacks of CBFT and CFFT, we propose a novel framework with a Hybrid Feature Transformation module (HFT).
BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection
Single frame data contains finite information which limits the performance of the existing vision-based multi-camera 3D object detection paradigms.
Ranked #18 on
3D Object Detection
on nuScenes Camera Only
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
While many networks are signed or directed, or both, there is a lack of unified software packages on graph neural networks (GNNs) specially designed for signed and directed networks.
Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data
More concretely, we first design a search-based module to retrieve a user's relevant historical behaviors, which are then mixed up with her recent records to be fed into a time-aware sequential network for capturing her time-sensitive demands.
Reasoning over Hybrid Chain for Table-and-Text Open Domain QA
CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering.
Ranked #2 on
Question Answering
on OTT-QA
BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View
As a fast version, BEVDet-Tiny scores 31. 2% mAP and 39. 2% NDS on the nuScenes val set.
Ranked #20 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Face-NMS: A Core-set Selection Approach for Efficient Face Recognition
The current research on this problem mainly focuses on designing an efficient Fully-connected layer (FC) to reduce GPU memory consumption caused by a large number of identities.
Single Node Injection Attack against Graph Neural Networks
In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i. e., the attacker is only allowed to inject one single node during the test phase to hurt GNN's performance.
Signed Bipartite Graph Neural Networks
Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.
Masked Face Recognition Challenge: The WebFace260M Track Report
There are second phase of the challenge till October 1, 2021 and on-going leaderboard.
CoSQA: 20,000+ Web Queries for Code Search and Question Answering
Finding codes given natural language query isb eneficial to the productivity of software developers.
SIMPLE: SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation
To make a better trade-off between accuracy and efficiency, we propose a novel multi-person pose estimation framework, SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE).
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition
In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Benchmark datasets have a significant impact on accelerating research in programming language tasks.
Ranked #1 on
Cloze Test
on CodeXGLUE - CT-maxmin
SDGNN: Learning Node Representation for Signed Directed Networks
Guided by related sociological theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks.
AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation
As AID successfully pushes the performance boundary of human pose estimation problem by considerable margin and sets a new state-of-the-art, we hope AID to be a regular configuration for training human pose estimators.
Ranked #1 on
Multi-Person Pose Estimation
on COCO minival
The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation
Specifically, by investigating the standard data processing in state-of-the-art approaches mainly including coordinate system transformation and keypoint format transformation (i. e., encoding and decoding), we find that the results obtained by common flipping strategy are unaligned with the original ones in inference.
Ranked #15 on
Pose Estimation
on COCO test-dev
Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation
Human pose estimation are of importance for visual understanding tasks such as action recognition and human-computer interaction.
Signed Graph Attention Networks
We evaluate the proposed SiGAT method by applying it to the signed link prediction task.
Ranked #1 on
Link Sign Prediction
on Epinions
COS960: A Chinese Word Similarity Dataset of 960 Word Pairs
Word similarity computation is a widely recognized task in the field of lexical semantics.
Optical Flow Based Online Moving Foreground Analysis
Obtained by moving object detection, the foreground mask result is unshaped and can not be directly used in most subsequent processes.
An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes
Real-time motion detection in non-stationary scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource.
Motion Detection
Motion Detection In Non-Stationary Scenes
+1
Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes
Real-time moving object detection in unconstrained scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource.
