Search Results for author:
Found 61 papers, 28 papers with code
Causal Augmentation for Causal Sentence Classification
Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification.
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling
However, the cross-item relations have been under-explored in the current multimodal pre-train models.
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames.
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques.
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Based on this proposal, we introduce Unified Spatio-Temporal Diffusion Models (USTD) to address the tasks uniformly within the uncertainty-aware diffusion framework.
UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web
To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of textual modality into urban imagery profiling, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP).
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
To address these issues, we propose UniTime for effective cross-domain time series learning.
Domain-wise Invariant Learning for Panoptic Scene Graph Generation
Panoptic Scene Graph Generation (PSG) involves the detection of objects and the prediction of their corresponding relationships (predicates).
Towards Complex-query Referring Image Segmentation: A Novel Benchmark
Referring Image Understanding (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms.
Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization
We theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature.
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker.
SOGDet: Semantic-Occupancy Guided Multi-view 3D Object Detection
This indicates that the combination of 3D object detection and 3D semantic occupancy leads to a more comprehensive perception of the 3D environment, thereby aiding build more robust autonomous driving systems.
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection
Early detection of dysplasia of the cervix is critical for cervical cancer treatment.
Panoptic Scene Graph Generation with Semantics-Prototype Learning
To promise consistency and accuracy during the transfer process, we propose to measure the invariance of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities.
Ranked #3 on
Panoptic Scene Graph Generation
on PSG Dataset
In Defense of Clip-based Video Relation Detection
While recent video-based methods utilizing video tubelets have shown promising results, we argue that the effective modeling of spatial and temporal context plays a more significant role than the choice between clip tubelets and video tubelets.
Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials
Street-view imagery provides us with novel experiences to explore different places remotely.
Ranked #3 on
Image-Based Localization
on cvact
LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry
In this paper, we introduce \texttt{LaDe}, the first publicly available last-mile delivery dataset with millions of packages from the industry.
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
To mitigate these limitations, we introduce the LargeST benchmark dataset.
Graph Neural Processes for Spatio-Temporal Extrapolation
We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph.
A real-time blind quality-of-experience assessment metric for HTTP adaptive streaming
In today's Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm.
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting.
Do We Really Need Graph Neural Networks for Traffic Forecasting?
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting.
CrossMatch: Source-Free Domain Adaptive Semantic Segmentation via Cross-Modal Consistency Training
Source-free domain adaptive semantic segmentation has gained increasing attention recently.
AirFormer: Predicting Nationwide Air Quality in China with Transformers
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic and social growth.
Analyzing Modality Robustness in Multimodal Sentiment Analysis
In this work, we hope to address that by (i) Proposing simple diagnostic checks for modality robustness in a trained multimodal model.
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer
Automatic transfer of text between domains has become popular in recent times.
Mix-up Self-Supervised Learning for Contrast-agnostic Applications
Contrastive self-supervised learning has attracted significant research attention recently.
DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition
While transformers have shown great potential on video recognition with their strong capability of capturing long-range dependencies, they often suffer high computational costs induced by the self-attention to the huge number of 3D tokens.
Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
Sensors are the key to sensing the environment and imparting benefits to smart cities in many aspects, such as providing real-time air quality information throughout an urban area.
When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?
In view of this, one may ask: can we leverage the additional signals from contrastive learning to alleviate data scarcity, so as to benefit STG forecasting?
Recovering the Unbiased Scene Graphs from the Biased Ones
Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects.
Ranked #2 on
Unbiased Scene Graph Generation
on Visual Genome
ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos
Detecting human-object interactions (HOI) is an important step toward a comprehensive visual understanding of machines.
Ranked #3 on
Human-Object Interaction Anticipation
on VidHOI
Towards Modelling Coherence in Spoken Discourse
While there has been significant progress towards modelling coherence in written discourse, the work in modelling spoken discourse coherence has been quite limited.
Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers
In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS).
LIFI: Towards Linguistically Informed Frame Interpolation
We also release several datasets to test computer vision video generation models of their speech understanding.
Domain Divergences: a Survey and Empirical Analysis
Domain divergence plays a significant role in estimating the performance of a model in new domains.
Multimodal Research in Vision and Language: A Review of Current and Emerging Trends
Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data.
Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal Representations
Visual relationship detection aims to reason over relationships among salient objects in images, which has drawn increasing attention over the past few years.
Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data
The abundance of information in web applications make recommendation essential for users as well as applications.
LSTM Networks for Online Cross-Network Recommendations
Existing models (1) fail to capture complex non-linear relationships in user interactions, and (2) are designed for offline settings hence, not updated online with incoming interactions to capture the dynamics in the recommender environment.
Exploring the use of Time-Dependent Cross-Network Information for Personalized Recommendations
The solution first learns historical user models in the target network by aggregating user preferences from multiple source networks.
CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users
The resultant user preferences are used in a Siamese network based neural recommender architecture.
Zero-Shot Multi-View Indoor Localization via Graph Location Networks
In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization.
"Notic My Speech" -- Blending Speech Patterns With Multimedia
To close the gap between speech understanding and multimedia video applications, in this paper, we show the initial experiments by modelling the perception on visual speech and showing its use case on video compression.
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
In this paper, we aim to learn effective modality representations to aid the process of fusion.
Learning based Methods for Code Runtime Complexity Prediction
Predicting the runtime complexity of a programming code is an arduous task.
Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings
In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings.
Conversational Transfer Learning for Emotion Recognition
We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target).
Ranked #19 on
Emotion Recognition in Conversation
on DailyDialog
Towards Multimodal Sarcasm Detection (An \_Obviously\_ Perfect Paper)
As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows.
Lipper: Synthesizing Thy Speech using Multi-View Lipreading
The model takes silent videos as input and produces speech as the output.
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)
As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows.
Harnessing GANs for Zero-shot Learning of New Classes in Visual Speech Recognition
To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases.
Kiki Kills: Identifying Dangerous Challenge Videos from Social Media
There has been upsurge in the number of people participating in challenges made popular through social media channels.
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
Emotion recognition in conversations is crucial for building empathetic machines.
Ranked #50 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Recognition in Conversation
General Classification
+2
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
In multilingual societies like the Indian subcontinent, use of code-switched languages is much popular and convenient for the users.
A Multimodal Approach to Predict Social Media Popularity
In this work, we propose a multimodal dataset consisiting of content, context, and social information for popularity prediction.
Theme-weighted Ranking of Keywords from Text Documents using Phrase Embeddings
Keyword extraction is a fundamental task in natural language processing that facilitates mapping of documents to a concise set of representative single and multi-word phrases.
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos
Emotion recognition in conversations is crucial for the development of empathetic machines.
Ranked #52 on
Emotion Recognition in Conversation
on IEMOCAP
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings
Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases.
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text.
Ranked #1 on
Sarcasm Detection
on SARC (all-bal)
