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Found 18 papers, 8 papers with code
MiMu: Mitigating Multiple Shortcut Learning Behavior of Transformers
Then, we further design self-improvement strategy in target model to reduce the reliance on multiple shortcuts.
Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models
3) Chain-of-thought prompting notably reduces shortcut reliance and outperforms other prompting strategies, while few-shot prompts generally underperform compared to zero-shot prompts.
Event Grounded Criminal Court View Generation with Cooperative (Large) Language Models
Then, we incorporate the extracted events into court view generation by merging case facts and events.
HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition
Great efforts have been made on this task with competitive performance, however, they usually treat the two subtasks, namely span detection and type classification, as mutually independent, and the integrity and correlation between subtasks have been largely ignored.
Ranked #4 on
Few-shot NER
on Few-NERD (INTER)
Learning Spatial-Temporal Embeddings for Sequential Point Cloud Frame Interpolation
A point cloud sequence is usually acquired at a low frame rate owing to the limitations from the sensing equipment.
Spatial-Temporal Consistency Refinement Network for Dynamic Point Cloud Frame Interpolation
Point cloud frame interpolation aims to improve the frame rate of a point cloud sequence by synthesising intermediate frames between consecutive frames.
Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease
Validation analysis revealed that the AUCs of DLRP were 0. 91 for GEV (95% CI 0. 90 to 0. 93, p < 0. 05) and 0. 88 for HRV (95% CI 0. 86 to 0. 89, p < 0. 01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM.
CLIP2TV: Align, Match and Distill for Video-Text Retrieval
Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head.
Ranked #13 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
Lossless Point Cloud Attribute Compression with Normal-based Intra Prediction
In this paper, we propose a normal-based intra prediction scheme, which provides a more efficient lossless attribute compression by introducing the normals of point clouds.
RAI-Net: Range-Adaptive LiDAR Point Cloud Frame Interpolation Network
In this paper, we propose a novel LiDAR point cloud frame interpolation method, which exploits range images (RIs) as an intermediate representation with CNNs to conduct the frame interpolation process.
An Unsupervised Optical Flow Estimation For LiDAR Image Sequences
Among these works, the optical flow estimation for LiDAR image sequences has become a key issue, especially for the motion estimation of the inter prediction in PCC.
BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values
There has been increasing interest in modeling survival data using deep learning methods in medical research.
Adverse event enrichment tests using VAERS
By appropriately addressing the issues of ties and excessive zeros in AE count data, our enrichment tests performed well as demonstrated by simulation studies and analyses of VAERS data.
Methodology
Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty
The experiment results show that the Dense2Sparse method obtained higher expected reward compared with the ones using standalone dense reward or sparse reward, and it also has a superior tolerance of system uncertainty.
DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values
There has been increasing interest in modelling survival data using deep learning methods in medical research.
COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
There are substantial instructional videos on the Internet, which enables us to acquire knowledge for completing various tasks.
Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification
Furthermore, since different layers in a deep network capture feature maps of different scales, we use these feature maps to construct a spatial pyramid and then utilize multi-scale information to obtain more accurate attention scores, which are used to weight the local features in all spatial positions of feature maps to calculate attention maps.
Deep Hashing with Category Mask for Fast Video Retrieval
This paper proposes an end-to-end deep hashing framework with category mask for fast video retrieval.
