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Found 17 papers, 3 papers with code
BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge
In a nutshell, our BAVS is designed to eliminate the interference of background noise or off-screen sounds in segmentation by establishing the audio-visual correspondences in an explicit manner.
Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics
However, we observed that prior arts are prone to segment a certain salient object in a video regardless of the audio information.
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels
In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data.
ESceme: Vision-and-Language Navigation with Episodic Scene Memory
Vision-and-language navigation (VLN) simulates a visual agent that follows natural-language navigation instructions in real-world scenes.
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels
The early stopping strategy averts updating CNNs during the early training phase and is widely employed in the presence of noisy labels.
Cross-Modal Contrastive Learning for Robust Reasoning in VQA
For each positive pair, we regard the images from different graphs as negative samples and deduct the version of multi-positive contrastive learning.
CNN-based Local Vision Transformer for COVID-19 Diagnosis
Deep learning technology can be used as an assistive technology to help doctors quickly and accurately identify COVID-19 infections.
Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis
However, in CT and CXR images, the infected area occupies only a small part of the image.
Bypass Network for Semantics Driven Image Paragraph Captioning
Most existing methods model the coherence through the topic transition that dynamically infers a topic vector from preceding sentences.
MSR: Making Self-supervised learning Robust to Aggressive Augmentations
With the training going on, the model begins to overfit noisy pairs.
Visual Superordinate Abstraction for Robust Concept Learning
Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks.
Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging
A modular camera rig arranged in a cylindrical configuration was designed to automatically capture images of the entire skin surface of a subject synchronously from multiple angles.
Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review
Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias.
Video-based cattle identification and action recognition
We demonstrate a working prototype for the monitoring of cow welfare by automatically analysing the animal behaviours.
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting
For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules.
A-CCNN: adaptive ccnn for density estimation and crowd counting
Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community.
A Structural Correlation Filter Combined with A Multi-task Gaussian Particle Filter for Visual Tracking
First, it can detect the tracked target in a large-scale search scope via weak KCF trackers and evaluate the reliability of weak trackers\rq decisions for a Gaussian particle filter to make a strong decision, and hence it can tackle fast motions, appearance variations, occlusions and re-detections.
