no code implementations • CVPR 2023 • Dahyun Kang, Piotr Koniusz, Minsu Cho, Naila Murray
For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels.
1 code implementation • 1 Nov 2022 • Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella, Tim Rocktäschel, Heinrich Küttler, Naila Murray
Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets.
1 code implementation • 30 Oct 2022 • Shan Zhang, Naila Murray, Lei Wang, Piotr Koniusz
To address these drawbacks, we propose a Time-rEversed diffusioN tEnsor Transformer (TENET), which i) forms high-order tensor representations that capture multi-way feature occurrences that are highly discriminative, and ii) uses a transformer that dynamically extracts correlations between the query image and the entire support set, instead of a single average-pooled support embedding.
no code implementations • CVPR 2022 • Shan Zhang, Lei Wang, Naila Murray, Piotr Koniusz
We design a Kernelized Few-shot Object Detector by leveraging kernelized matrices computed over multiple proposal regions, which yield expressive non-linear representations whose model complexity is learned on the fly.
1 code implementation • CVPR 2021 • M. Saquib Sarfraz, Naila Murray, Vivek Sharma, Ali Diba, Luc van Gool, Rainer Stiefelhagen
Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks.
Ranked #1 on Action Segmentation on MPII Cooking 2 Dataset
no code implementations • 29 Jan 2020 • Yohann Cabon, Naila Murray, Martin Humenberger
This paper introduces an updated version of the well-known Virtual KITTI dataset which consists of 5 sequence clones from the KITTI tracking benchmark.
no code implementations • 12 Oct 2019 • César Roberto de Souza, Adrien Gaidon, Yohann Cabon, Naila Murray, Antonio Manuel López
With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for "Procedural Human Action Videos".
1 code implementation • CVPR 2016 • Saumya Jetley, Naila Murray, Eleonora Vig
Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection.
no code implementations • 16 Jan 2018 • Jon Almazan, Bojana Gajic, Naila Murray, Diane Larlus
In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification.
no code implementations • 16 Aug 2017 • Naila Murray, Albert Gordo
Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories.
no code implementations • 24 Nov 2016 • Naila Murray, Hervé Jégou, Florent Perronnin, Andrew Zisserman
The second one involves equalising the match of a single descriptor to the aggregated vector.
no code implementations • ICCV 2015 • Albert Gordo, Jon Almazan, Naila Murray, Florent Perronnin
The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images.
no code implementations • 16 Dec 2014 • Luca Marchesotti, Naila Murray, Florent Perronnin
We then describe how these three key components of AVA - images, scores, and comments - can be effectively leveraged to learn visual attributes.
no code implementations • CVPR 2014 • Naila Murray, Florent Perronnin
Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics, such as the bag-of-visual-words (BOV) and its soft- and sparse-coding extensions.