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Found 9 papers, 1 papers with code
Performance of computer vision algorithms for fine-grained classification using crowdsourced insect images
The field of computer vision offers a wide range of algorithms, each with its strengths and weaknesses; how do we identify the algorithm that is in line with our application?
Comparison between transformers and convolutional models for fine-grained classification of insects
Although we observe high performances with all three families of models, our analysis shows that the hybrid model outperforms the fully convolutional-base and fully transformer-base models on accuracy performance and the fully transformer-base model outperforms the others on inference speed and, these prove the transformer to be robust to the shortage of samples and to be faster at inference time.
UW-ProCCaps: UnderWater Progressive Colourisation with Capsules
The progressive training gives the ground for a generative adversarial routine focused on the refining of colours giving the image bright and saturated colours which bring the image back to life.
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization
The degradation in the underwater images is due to wavelength-dependent light attenuation, scattering, and to the diversity of the water types in which they are captured.
TUCaN: Progressively Teaching Colourisation to Capsules
We performed a user study to quantify the perceptual realism of the colourisation results demonstrating: that progressive learning let the TUCaN achieve better colours than the end to end scheme; and pointing out the limitations of the existing evaluation metrics.
Collaboration among Image and Object Level Features for Image Colourisation
Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum.
Is It a Plausible Colour? UCapsNet for Image Colourisation
Different from existing works relying on convolutional neural network models pre-trained with supervision, we cast such colourisation problem as a self-supervised learning task.
WhoAmI: An Automatic Tool for Visual Recognition of Tiger and Leopard Individuals in the Wild
We develop automatic algorithms that are able to detect animals, identify the species of animals and to recognize individual animals for two species.
Machine learning approaches for identifying prey handling activity in otariid pinnipeds
Systems developed in wearable devices with sensors onboard are widely used to collect data of humans and animals activities with the perspective of an on-board automatic classification of data.
