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Found 66 papers, 29 papers with code
``What Do You Mean by That?'' A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
Translating Headers of Tabular Data: A Pilot Study of Schema Translation
Experiments on our dataset demonstrate that CAST significantly outperforms state-of-the-art neural machine translation models.
Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity
Ensuring relevance quality in product search is a critical task as it impacts the customer’s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term.
Spelling Correction using Phonetics in E-commerce Search
In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries.
From Image to Video, what do we need in multimodal LLMs?
Multimodal Large Language Models (MLLMs) have demonstrated profound capabilities in understanding multimodal information, covering from Image LLMs to the more complex Video LLMs.
AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior
Language significantly influences the formation and evolution of Human emergent behavior, which is crucial in understanding collective intelligence within human societies.
Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents
The challenges and costs of collecting realistic interactive logs for data analysis hinder the quantitative evaluation of Large Language Model (LLM) agents in this task.
NoteLLM: A Retrievable Large Language Model for Note Recommendation
Indeed, learning to generate hashtags/categories can potentially enhance note embeddings, both of which compress key note information into limited content.
NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries
Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis.
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized.
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.
Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking
In addition to the previous method of treating objects as nodes, the network innovatively treats object trajectories as nodes for information interaction, improving the graph neural network's feature representation capability.
Towards the Law of Capacity Gap in Distilling Language Models
The pain is mainly resulted by the curse of capacity gap, which describes that a larger teacher LM cannot always lead to a better student LM than one distilled from a smaller teacher LM due to the affect of capacity gap increment.
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments
Online learning to rank (ONL2R) is a foundational problem for recommender systems and has received increasing attention in recent years.
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning
The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge.
Piecing Together Clues: A Benchmark for Evaluating the Detective Skills of Large Language Models
We introduces the DetectBench, a reading comprehension dataset designed to assess a model's ability to jointly ability in key information detection and multi-hop reasoning when facing complex and implicit information.
Improving the Transferability of Time Series Forecasting with Decomposition Adaptation
However, in time series forecasting, it is difficult to obtain enough data, which limits the performance of neural forecasting models.
AdaSelection: Accelerating Deep Learning Training through Data Subsampling
In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance.
Secure Vertical Federated Learning Under Unreliable Connectivity
Most work in privacy-preserving federated learning (FL) has focused on horizontally partitioned datasets where clients hold the same features and train complete client-level models independently.
Uncovering and Categorizing Social Biases in Text-to-SQL
In this work, we aim to uncover and categorize social biases in Text-to-SQL models.
TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering
A common challenge in HQA and other passage-table QA datasets is that it is generally unrealistic to iterate over all table rows, columns, and linked passages to retrieve evidence.
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition
Also, humans have brain regions dedicated to understanding the minds of others and analyzing their intentions, such as the medial prefrontal cortex of the temporal lobe.
Ranked #2 on
Skeleton Based Action Recognition
on NTU RGB+D 120
(using extra training data)
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation
CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investigated, where most efforts directly adopt CLIP features without deliberative adaptations.
Multi-view reconstruction of bullet time effect based on improved NSFF model
By using the optical flow prediction information to suppress the dynamic network timely, the network is forced to improve the reconstruction effect of dynamic and static networks independently, and the ability to understand and reconstruct dynamic and static scenes is improved.
Adaptive Approximate Implicitization of Planar Parametric Curves via Weak Gradient Constraints
Converting a parametric curve into the implicit form, which is called implicitization, has always been a popular but challenging problem in geometric modeling and related applications.
OvarNet: Towards Open-vocabulary Object Attribute Recognition
In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario.
Ranked #1 on
Open Vocabulary Attribute Detection
on OVAD benchmark
(using extra training data)
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables.
Learning Attribute and Class-Specific Representation Duet for Fine-Grained Fashion Analysis
Fashion representation learning involves the analysis and understanding of various visual elements at different granularities and the interactions among them.
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing
Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems.
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation
The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications.
Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL
Following this study, we propose a simple yet effective counterfactual example generation approach that automatically produces ambiguous and unanswerable text-to-SQL examples.
Federated Learning for Inference at Anytime and Anywhere
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio
Self-supervised learning (SSL) has proven vital in speech and audio-related applications.
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed.
Federated Self-supervised Learning for Video Understanding
The ubiquity of camera-enabled mobile devices has lead to large amounts of unlabelled video data being produced at the edge.
Ranked #1 on
Action Recognition
on UCF-101
(Accuracy metric)
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training
We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models.
NFormer: Robust Person Re-identification with Neighbor Transformer
To tackle this issue, we propose a Neighbor Transformer Network, or NFormer, which explicitly models interactions across all input images, thus suppressing outlier features and leading to more robust representations overall.
Federated Self-supervised Speech Representations: Are We There Yet?
The ubiquity of microphone-enabled devices has lead to large amounts of unlabelled audio data being produced at the edge.
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL
Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffective when applied to different settings.
Decoupled IoU Regression for Object Detection
Prior works propose to predict Intersection-over-Union (IoU) between bounding boxes and corresponding ground-truths to improve NMS, while accurately predicting IoU is still a challenging problem.
Reasoning Like Program Executors
Reasoning over natural language is a long-standing goal for the research community.
Ranked #2 on
Question Answering
on DROP Test
(using extra training data)
Physically Disentangled Intra- and Inter-Domain Adaptation for Varicolored Haze Removal
Consequently, we perform inter-domain adaptation between the synthetic and real images by mutually exchanging the background and other two components.
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge
To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario.
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
HiTab provides 10, 686 QA pairs and descriptive sentences with well-annotated quantity and entity alignment on 3, 597 tables with broad coverage of table hierarchies and numerical reasoning types.
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
On-device Federated Learning with Flower
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud.
Hyperspectral Image Denoising Based On Multi-Stream Denoising Network
Our network consists of the noise estimation subnetwork and denoising subnetwork.
A first look into the carbon footprint of federated learning
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.
Occluded Video Instance Segmentation: A Benchmark
On the OVIS dataset, the highest AP achieved by state-of-the-art algorithms is only 16. 3, which reveals that we are still at a nascent stage for understanding objects, instances, and videos in a real-world scenario.
Ranked #39 on
Video Instance Segmentation
on OVIS validation
Dynamic-K Recommendation with Personalized Decision Boundary
The ranking objective is the same as existing methods, i. e., to create a ranking list of items according to users' interests.
"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
Flower: A Friendly Federated Learning Research Framework
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud.
Compositional Generalization by Learning Analytical Expressions
Compositional generalization is a basic and essential intellective capability of human beings, which allows us to recombine known parts readily.
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition
The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR).
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition
In the specific context of Automatic Speech Recognition (ASR), distillation from ensembles of acoustic models has recently shown promising results in increasing recognition performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation
Given such good instance bounding box, we further design a simple instance-level semantic segmentation pipeline and achieve the 1st place on the segmentation challenge.
A Hybrid Semantic Parsing Approach for Tabular Data Analysis
This paper presents a novel approach to translating natural language questions to SQL queries for given tables, which meets three requirements as a real-world data analysis application: cross-domain, multilingualism and enabling quick-start.
C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) that only needs image-level annotations has obtained much attention recently.
Utilizing the Instability in Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the objective.
Characterizing Shadow Price via Lagrangian Multiplier for Nonsmooth Problem
It is shown that the Lagrangian Multiplier is the upper bound of shadow price for convex optimization and a class of Lipschtzian optimizations.
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.
Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers
We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements).
Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach
Gait as a biometric trait has attracted much attention in many security and privacy applications such as identity recognition and authentication, during the last few decades.
