Search Results for author:
Found 7 papers, 4 papers with code
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction
We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD).
Interpretable Visual Question Answering via Reasoning Supervision
Transformer-based architectures have recently demonstrated remarkable performance in the Visual Question Answering (VQA) task.
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines
In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions.
An Incremental Learning framework for Large-scale CTR Prediction
In this work we introduce an incremental learning framework for Click-Through-Rate (CTR) prediction and demonstrate its effectiveness for Taboola's massive-scale recommendation service.
From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery
This paper proposes a novel paradigm for the unsupervised learning of object landmark detectors.
Unsupervised Learning of Object Landmarks via Self-Training Correspondence
This paper addresses the problem of unsupervised discovery of object landmarks.
Convolutive Audio Source Separation using Robust ICA and an intelligent evolving permutation ambiguity solution
Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones.
