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Found 16 papers, 4 papers with code
RGB-T Tracking Based on Mixed Attention
An RGB-T tracker based on mixed attention mechanism to achieve complementary fusion of modalities (referred to as MACFT) is proposed in this paper.
HQANN: Efficient and Robust Similarity Search for Hybrid Queries with Structured and Unstructured Constraints
The in-memory approximate nearest neighbor search (ANNS) algorithms have achieved great success for fast high-recall query processing, but are extremely inefficient when handling hybrid queries with unstructured (i. e., feature vectors) and structured (i. e., related attributes) constraints.
Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition
Recognizing human non-speech vocalizations is an important task and has broad applications such as automatic sound transcription and health condition monitoring.
Ranked #1 on
Audio Classification
on VocalSound
Cross-domain User Preference Learning for Cold-start Recommendation
With user history represented by a domain-aware sequential model, a frequency encoder is applied to the underlying tags for user content preference learning.
Stable Prediction on Graphs with Agnostic Distribution Shift
The results demonstrate that our method outperforms various SOTA GNNs for stable prediction on graphs with agnostic distribution shift, including shift caused by node labels and attributes.
Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries
Tuberculosis (TB) is a top-10 cause of death worldwide.
M6: A Chinese Multimodal Pretrainer
In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases
To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States.
DeVLBert: Learning Deconfounded Visio-Linguistic Representations
In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned.
Poet: Product-oriented Video Captioner for E-commerce
Then, based on the aspects of the video-associated product, we perform knowledge-enhanced spatial-temporal inference on those graphs for capturing the dynamic change of fine-grained product-part characteristics.
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning
In real-world applications, networks usually consist of billions of various types of nodes and edges with abundant attributes.
Comprehensive Information Integration Modeling Framework for Video Titling
In e-commerce, consumer-generated videos, which in general deliver consumers' individual preferences for the different aspects of certain products, are massive in volume.
Grounded and Controllable Image Completion by Incorporating Lexical Semantics
Existing image completion procedure is highly subjective by considering only visual context, which may trigger unpredictable results which are plausible but not faithful to a grounded knowledge.
Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soil with Generalized LIBS Spectra
Machine learning has been used to develop the model.
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC
Multi-structure model fitting has traditionally taken a two-stage approach: First, sample a (large) number of model hypotheses, then select the subset of hypotheses that optimise a joint fitting and model selection criterion.
Exponential Family Graph Matching and Ranking
We present a method for learning max-weight matching predictors in bipartite graphs.
