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Found 16 papers, 6 papers with code
Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?
Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis.
Global–Local Information Soft-Alignment for Cross-Modal Remote-Sensing Image–Text Retrieval
To mitigate semantic ambiguities during the alignment of local features, we design a local information soft-alignment (LISA) module.
Ranked #3 on
Cross-Modal Retrieval
on RSICD
HaltingVT: Adaptive Token Halting Transformer for Efficient Video Recognition
Action recognition in videos poses a challenge due to its high computational cost, especially for Joint Space-Time video transformers (Joint VT).
Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head
Accurate localization of cephalometric landmarks holds great importance in the fields of orthodontics and orthognathics due to its potential for automating key point labeling.
Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval
To address these issues, we propose an efficient video representation network with Differentiable Resolution Compression and Alignment mechanism, which compresses non-essential information in the early stage of the network to reduce computational costs while maintaining consistent temporal correlations.
GaitFormer: Revisiting Intrinsic Periodicity for Gait Recognition
The second component is the Temporal Aggregation Module (TAM), which separates embeddings into trend and seasonal components, and extracts meaningful temporal correlations to identify primary components, while filtering out random noise.
3D-CSL: self-supervised 3D context similarity learning for Near-Duplicate Video Retrieval
In this paper, we introduce 3D-CSL, a compact pipeline for Near-Duplicate Video Retrieval (NDVR), and explore a novel self-supervised learning strategy for video similarity learning.
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results.
Motion Sensitive Contrastive Learning for Self-supervised Video Representation
Contrastive learning has shown great potential in video representation learning.
A Novel Markov Model for Near-Term Railway Delay Prediction
We then propose a delay prediction model based on non-homogeneous Markov chains.
End-to-End Human Object Interaction Detection with HOI Transformer
We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner.
Ranked #32 on
Human-Object Interaction Detection
on HICO-DET
(using extra training data)
Physical rendering of synthetic spaces for topological sound transport
Synthetic dimensions can be rendered in the physical space and this has been achieved with photonics and cold atomic gases, however, little to no work has been succeeded in acoustics because acoustic wave-guides cannot be weakly coupled in a continuous fashion.
Mesoscale and Nanoscale Physics Classical Physics
An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data
Dysfunction of the prefrontal cortex contributes to cognitive deficits and the majority of neurodevelopmental disorders; there is therefore a need for detailed knowledge of the development of the prefrontal cortex.
A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex
Dysfunction of the prefrontal cortex contributes to cognitive deficits and the majority of neurodevelopmental disorders; there is therefore a need for detailed knowledge of the development of the prefrontal cortex.
Discriminative Transformation Learning for Fuzzy Sparse Subspace Clustering
This paper develops a novel iterative framework for subspace clustering in a learned discriminative feature domain.
Target Oriented High Resolution SAR Image Formation via Semantic Information Guided Regularizations
Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its remarkable performance to generate a feature enhanced high resolution image, in which a sparsity-inducing regularizer is involved by exploiting the sparsity priors of some visual features in the underlying image.
