Search Results for author: Naila Murray

Found 14 papers, 4 papers with code

Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation

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.

Few-Shot Image Classification Pseudo Label +1

Dungeons and Data: A Large-Scale NetHack Dataset

1 code implementation1 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.

Decision Making NetHack +2

Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection

1 code implementation30 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.

Few-Shot Object Detection Object +1

Kernelized Few-Shot Object Detection With Efficient Integral Aggregation

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.

Few-Shot Object Detection Object +2

Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation

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.

Action Segmentation Clustering +2

Virtual KITTI 2

no code implementations29 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.

Autonomous Driving Instance Segmentation +2

Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models

no code implementations12 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".

Action Recognition Optical Flow Estimation +3

End-to-End Saliency Mapping via Probability Distribution Prediction

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.

Saliency Prediction Text Detection

Re-ID done right: towards good practices for person re-identification

no code implementations16 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.

Attribute Person Re-Identification

A deep architecture for unified aesthetic prediction

no code implementations16 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.

Interferences in match kernels

no code implementations24 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.

Image Retrieval Retrieval

LEWIS: Latent Embeddings for Word Images and their Semantics

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.


Discovering beautiful attributes for aesthetic image analysis

no code implementations16 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.


Generalized Max Pooling

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.

Image Classification

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