37 citations · 43 across the 9 of their papers we have counts for
9 papers
Audio-Visual Grounding Referring Expression for Robotic Manipulation
Yefei Wang, Kaili Wang, Yi Wang +3
Referring expressions are commonly used when referring to a specific target in people's daily dialogue. In this paper, we develop a novel task of audio-visual grounding referring e…
MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization
Kaili Wang, Jose Oramas, Tinne Tuytelaars
One of the most common problems of weakly supervised object localization is that of inaccurate object coverage. In the context of state-of-the-art methods based on Class Activation…
Towards Human-Understandable Visual Explanations:Imperceptible High-frequency Cues Can Better Be Removed
Kaili Wang, Jose Oramas, Tinne Tuytelaars
Explainable AI (XAI) methods focus on explaining what a neural network has learned - in other words, identifying the features that are the most influential to the prediction. In th…
Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing
Kaili Wang, Jose Oramas, Tinne Tuytelaars
Given a really low-resolution input image of a face (say 16x16 or 8x8 pixels), the goal of this paper is to reconstruct a high-resolution version thereof. This, by itself, is an il…
Information Compensation for Deep Conditional Generative Networks
Zehao Wang, Kaili Wang, Tinne Tuytelaars +1
In recent years, unsupervised/weakly-supervised conditional generative adversarial networks (GANs) have achieved many successes on the task of modeling and generating data. However…
In Defense of LSTMs for Addressing Multiple Instance Learning Problems
Kaili Wang, Jose Oramas, Tinne Tuytelaars
LSTMs have a proven track record in analyzing sequential data. But what about unordered instance bags, as found under a Multiple Instance Learning (MIL) setting? While not often us…