Search Results for author:
Found 23 papers, 14 papers with code
Optimizing LIGO with LISA forewarnings to improve black-hole spectroscopy
The early inspiral of massive stellar-mass black-hole binaries merging in LIGO's sensitivity band will be detectable at low frequencies by the upcoming space mission LISA.
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena
Deep Association Learning for Unsupervised Video Person Re-identification
In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training.
Ranked #6 on
Person Re-Identification
on PRID2011
Unsupervised Person Re-Identification
Video-Based Person Re-Identification
Semi-Supervised Deep Learning with Memory
We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data.
Multiband gravitational-wave event rates and stellar physics
We use population synthesis simulations of isolated binary stars to explore some of the stellar physics that could be constrained with multiband events, and we show that specific formation pathways might be overrepresented in multiband events compared to ground-only detections.
High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology
Instance-Guided Context Rendering for Cross-Domain Person Re-Identification
To tackle this limitation, we propose a novel Instance-Guided Context Rendering scheme, which transfers the source person identities into diverse target domain contexts to enable supervised re-id model learning in the unlabelled target domain.
Image Search With Text Feedback by Visiolinguistic Attention Learning
In this work, we tackle this task by a novel Visiolinguistic Attention Learning (VAL) framework.
Ranked #17 on
Image Retrieval
on Fashion IQ
Uncertainty-aware Generalized Adaptive CycleGAN
Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner.
Distilling Audio-Visual Knowledge by Compositional Contrastive Learning
Having access to multi-modal cues (e. g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality.
Where and When: Space-Time Attention for Audio-Visual Explanations
Recent advances in XAI provide explanations for models trained on still images.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Uncertainty-Guided Progressive GANs for Medical Image Translation
However, the state-of-the-art GAN-based frameworks do not estimate the uncertainty in the predictions made by the network that is essential for making informed medical decisions and subsequent revision by medical experts and has recently been shown to improve the performance and interpretability of the model.
Robustness via Uncertainty-aware Cycle Consistency
Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs.
Probabilistic Compositional Embeddings for Multimodal Image Retrieval
Without bells and whistles, we show that our probabilistic model formulation significantly outperforms existing related methods on multimodal image retrieval while generalizing well to query with different amounts of inputs given in arbitrary visual and (or) textual modalities.
Attention Consistency on Visual Corruptions for Single-Source Domain Generalization
Generalizing visual recognition models trained on a single distribution to unseen input distributions (i. e. domains) requires making them robust to superfluous correlations in the training set.
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks
Moreover, many of the high-performing deep learning models that are already trained and deployed are non-Bayesian in nature and do not provide uncertainty estimates.
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey
Semi-supervised learning and unsupervised learning offer promising paradigms to learn from an abundance of unlabeled visual data.
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information.
Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment
In this work, we formulate a new knowledge distillation framework to transfer the knowledge from self-supervised pre-trained models to any other student network by a novel approach named Embedding Graph Alignment.
ScaleDet: A Scalable Multi-Dataset Object Detector
Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50. 7 on LVIS, 58. 8 on COCO, 46. 8 on Objects365, 76. 2 on OpenImages, and 71. 8 on ODinW, surpassing state-of-the-art detectors with the same backbone.
Ranked #1 on
Object Detection
on OpenImages-v6
(using extra training data)
Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity
This paper introduces innovative benchmarks to evaluate Vision-Language Models (VLMs) in real-world zero-shot recognition tasks, focusing on the granularity and specificity of prompting text.
Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation
First, we introduce a denoising scheme to generate reliable pseudo-heatmaps as targets for learning from unlabeled data.
Hyperbolic Learning with Synthetic Captions for Open-World Detection
Specifically, we bootstrap dense synthetic captions using pre-trained VLMs to provide rich descriptions on different regions in images, and incorporate these captions to train a novel detector that generalizes to novel concepts.
Self-Supervised Multi-Object Tracking with Path Consistency
In this paper, we propose a novel concept of path consistency to learn robust object matching without using manual object identity supervision.
Learning Joint Visual Semantic Matching Embeddings for Language-guided Retrieval
Interactive image retrieval is an emerging research topic with the objective of integrating inputs from multiple modalities as query for retrieval, e. g., textual feedback from users to guide, modify or refine image retrieval.
