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Found 26 papers, 8 papers with code
Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning
However, we observed that most of the keyphrases are composed of some important words (seed words) in the source text, and if these words can be identified accurately and copied to create more keyphrases, the performance of the model might be improved.
Period-LLM: Extending the Periodic Capability of Multimodal Large Language Model
Periodic or quasi-periodic phenomena reveal intrinsic characteristics in various natural processes, such as weather patterns, movement behaviors, traffic flows, and biological signals.
Sage Deer: A Super-Aligned Driving Generalist Is Your Copilot
This benchmark measures the deer's perceptual decision-making ability and the super alignment's accuracy.
STFM: A Spatio-Temporal Information Fusion Model Based on Phase Space Reconstruction for Sea Surface Temperature Prediction
The sea surface temperature (SST), a key environmental parameter, is crucial to optimizing production planning, making its accurate prediction a vital research topic.
SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard Examples
This method distinguishes the entropy differences among logits of hard and easy examples, thereby identifying hard examples and increasing the utility of unlabeled data, better addressing the imbalance problem in CISSL.
TASL-Net: Tri-Attention Selective Learning Network for Intelligent Diagnosis of Bimodal Ultrasound Video
In the intelligent diagnosis of bimodal (gray-scale and contrast-enhanced) ultrasound videos, medical domain knowledge such as the way sonographers browse videos, the particular areas they emphasize, and the features they pay special attention to, plays a decisive role in facilitating precise diagnosis.
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets.
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing.
Large-scale Weakly Supervised Learning for Road Extraction from Satellite Imagery
Our extensive experimental results show that the prediction accuracy increases with the amount of the weakly labeled data, as well as the road density in the areas chosen for training.
Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model
Text-driven human motion generation in computer vision is both significant and challenging.
Ranked #22 on
Motion Synthesis
on KIT Motion-Language
Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction
In this work, we propose the first precise hand-object reconstruction method in hyperbolic space, namely Dynamic Hyperbolic Attention Network (DHANet), which leverages intrinsic properties of hyperbolic space to learn representative features.
Attentive Mask CLIP
To address this issue, we propose an attentive token removal approach for CLIP training, which retains tokens with a high semantic correlation to the text description.
Training a universal instance segmentation network for live cell images of various cell types and imaging modalities
We share our recent findings in an attempt to train a universal segmentation network for various cell types and imaging modalities.
Adaptive Discriminative Regularization for Visual Classification
Regarding this hypothesis, we propose a novel regularization to improve discriminative learning.
HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization
Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year.
Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization
Visual pattern recognition over agricultural areas is an important application of aerial image processing.
Large-Scale Unsupervised Person Re-Identification with Contrastive Learning
In particular, most existing unsupervised and domain adaptation ReID methods utilize only the public datasets in their experiments, with labels removed.
Spherical Transformer: Adapting Spherical Signal to CNNs
To this end, the proposed method first uses local structured sampling methods such as HEALPix to construct a transformer grid by using the information of spherical points and its adjacent points, and then transforms the spherical signals to the vectors through the grid.
Key Frame Proposal Network for Efficient Pose Estimation in Videos
Human pose estimation in video relies on local information by either estimating each frame independently or tracking poses across frames.
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
A Variational Approach for Learning from Positive and Unlabeled Data
Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally.
Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery
Deep learning is revolutionizing the mapping industry.
Ranked #5 on
Semantic Segmentation
on Porto
Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming
This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers.
Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold
In this paper we propose a new framework to compare and classify temporal sequences.
Self Scaled Regularized Robust Regression
Linear Robust Regression (LRR) seeks to find the parameters of a linear mapping from noisy data corrupted from outliers, such that the number of inliers (i. e. pairs of points where the fitting error of the model is less than a given bound) is maximized.
Automatic Objects Removal for Scene Completion
With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet.
