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Found 29 papers, 11 papers with code
A Transformational Biencoder with In-Domain Negative Sampling for Zero-Shot Entity Linking
As like previous work, we rely on negative entities to encourage our model to discriminate the golden entities during training.
LEOD: Label-Efficient Object Detection for Event Cameras
On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels.
Code-Style In-Context Learning for Knowledge-Based Question Answering
Current methods for Knowledge-Based Question Answering (KBQA) usually rely on complex training techniques and model frameworks, leading to many limitations in practical applications.
Cross-view Semantic Alignment for Livestreaming Product Recognition
Moreover, a cRoss-vIew semantiC alignmEnt (RICE) model is proposed to learn discriminative instance features from the image and video views of the products.
The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solution
The goal is to predict brain responses across the entire visual brain, as it is the region where the most reliable responses to images have been observed.
EventCLIP: Adapting CLIP for Event-based Object Recognition
Recent advances in zero-shot and few-shot classification heavily rely on the success of pre-trained vision-language models (VLMs) such as CLIP.
High-Fidelity Clothed Avatar Reconstruction from a Single Image
This paper presents a framework for efficient 3D clothed avatar reconstruction.
Reusing Deep Neural Network Models through Model Re-engineering
Prior approaches to DNN model reuse have two main limitations: 1) reusing the entire model, while only a small part of the model's functionalities (labels) are required, would cause much overhead (e. g., computational and time costs for inference), and 2) model reuse would inherit the defects and weaknesses of the reused model, and hence put the new system under threats of security attack.
Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction
Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance.
MorphGANFormer: Transformer-based Face Morphing and De-Morphing
Semantic face image manipulation has received increasing attention in recent years.
Deep Contrastive One-Class Time Series Anomaly Detection
To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification.
Dual-Flattening Transformers through Decomposed Row and Column Queries for Semantic Segmentation
We propose a Dual-Flattening Transformer (DFlatFormer) to enable high-resolution output by reducing complexity to $\mathcal{O}(hw(H+W))$ that is multiple orders of magnitude smaller than the naive dense transformer.
An Empirical Study on End-to-End Singing Voice Synthesis with Encoder-Decoder Architectures
With the rapid development of neural network architectures and speech processing models, singing voice synthesis with neural networks is becoming the cutting-edge technique of digital music production.
Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach
In this paper, we proposed a novel end-to-end self-organizing framework for user behavior prediction.
Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed
In this paper, we propose a balanced filter pruning method for both performance and pruning speed.
Themes Informed Audio-visual Correspondence Learning
The applications of short-term user-generated video (UGV), such as Snapchat, and Youtube short-term videos, booms recently, raising lots of multimodal machine learning tasks.
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task.
Ranked #8 on
Relation Extraction
on WebNLG
IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner.
Ranked #18 on
Cross-Modal Retrieval
on Flickr30k
Face Beautification: Beyond Makeup Transfer
Inspired by the latest advances in style-based synthesis and face beauty prediction, we propose a novel framework of face beautification.
Digital Twin: Acquiring High-Fidelity 3D Avatar from a Single Image
We present an approach to generate high fidelity 3D face avatar with a high-resolution UV texture map from a single image.
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree
We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence.
An Extensible and Personalizable Multi-Modal Trip Planner
Despite a tremendous amount of work in the literature and in the commercial sectors, current approaches to multi-modal trip planning still fail to consistently generate plans that users deem optimal in practice.
Learning Optimal and Near-Optimal Lexicographic Preference Lists
We consider learning problems of an intuitive and concise preference model, called lexicographic preference lists (LP-lists).
Human-In-The-Loop Learning of Qualitative Preference Models
In this work, we present a novel human-in-the-loop framework to help the human user understand the decision making process that involves choosing preferred options.
Understanding Beauty via Deep Facial Features
The concept of beauty has been debated by philosophers and psychologists for centuries, but most definitions are subjective and metaphysical, and deficit in accuracy, generality, and scalability.
Semi-supervised Text Regression with Conditional Generative Adversarial Networks
Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics.
Attributes in Multiple Facial Images
To address this question, we deploy deep training for facial attributes prediction, and we explore the inconsistency issue among the attributes computed from each single image.
Latent Feature Based FM Model For Rating Prediction
Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM).
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.
