Search Results for author:
Found 10 papers, 4 papers with code
CCMB: A Large-scale Chinese Cross-modal Benchmark
In this work, we build a large-scale high-quality Chinese Cross-Modal Benchmark named CCMB for the research community, which contains the currently largest public pre-training dataset Zero and five human-annotated fine-tuning datasets for downstream tasks.
Ranked #3 on
Image Retrieval
on Flickr30k-CN
DenseBox: Unifying Landmark Localization with End to End Object Detection
How can a single fully convolutional neural network (FCN) perform on object detection?
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph
Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood.
Ranked #6 on
Link Property Prediction
on ogbl-wikikg2
Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling
Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency.
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Face Recognition has been studied for many decades.
The 2nd Anti-UAV Workshop & Challenge: Methods and Results
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment
Basically, there are two main methods for SLU tasks: (1) Two-stage method, which uses a speech model to transfer speech to text, then uses a language model to get the results of downstream tasks; (2) One-stage method, which just fine-tunes a pre-trained speech model to fit in the downstream tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors
Translation-based knowledge graph embedding has been one of the most important branches for knowledge representation learning since TransE came out.
Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?
Recently, with the development of geometric neural networks, the data-driven schemes have been successfully applied in this field, both for molecular conformation generation (in vacuum) and binding pose generation (in protein pocket).
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation
Additionally, in the absence of AAV9 capsid data, apart from one wild-type sequence, we used the same model to directly generate a number of viable sequences with up to 9 mutations.
