Feedback-Driven Vision-Language Alignment with Minimal Human Supervision
Vision-language models (VLMs) have demonstrated remarkable potential in integrating visual and linguistic information, but their performance is often constrained by the need for extensive,...
GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of...
Single Image Deraining using Scale-Aware Multi-Stage Recurrent Network
Given a single input rainy image, our goal is to visually remove rain streaks and the veiling effect caused by scattering and transmission of rain streaks and rain droplets. We are particularly...
Object Tracking Using Spatio-Temporal Future Prediction
Occlusion is a long-standing problem that causes many modern tracking methods to be erroneous. In this paper, we address the occlusion problem by exploiting the current and future possible...
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy
Semi-supervised classification is a great focus of interest, as in real-world scenarios obtaining labels is expensive, time-consuming and might require expert knowledge. This has motivated the...
Robust Optical Flow Estimation in Rainy Scenes
Optical flow estimation in the rainy scenes is challenging due to background degradation introduced by rain streaks and rain accumulation effects in the scene. Rain accumulation effect refers to...
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning
GraphX $$^\mathbf{\small NET } -$$ -Chest X-Ray Classification Under Extreme Minimal Supervision
Estimating Reflectance Layer from a Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning
AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation
Few-shot image generation (FSIG) aims to learn to generate new and diverse images given few (e.g., 10) training samples. Recent work has addressed FSIG by leveraging a GAN pre-trained on a...
Single image reflection removal via learning with multi-image constraints
Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass. The problem of removing reflection artifacts is important but...
Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning
Estimating the reflectance layer from a single image is a challenging task. It becomes more challenging when the input image contains shadows or specular highlights, which often render an...
Realistic Large-Scale Fine-Depth Dehazing Dataset from 3D Videos
Image dehazing is one of the important and popular topics in computer vision and machine learning. A reliable real-time dehazing method with reliable performance is highly desired for many...