Search Results for author:
Found 12 papers, 7 papers with code
VideoPrism: A Foundational Visual Encoder for Video Understanding
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model.
VideoGLUE: Video General Understanding Evaluation of Foundation Models
We evaluate existing foundation models video understanding capabilities using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring a foundation model (FM) for a downstream task.
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information
We showcase its application to the landmark recognition domain, presenting a detailed analysis and the final fairer landmark rankings.
Towards A Fairer Landmark Recognition Dataset
To create a more comprehensive and equitable dataset, we start by defining the fair relevance of a landmark to the world population.
Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food
Understanding the nutritional content of food from visual data is a challenging computer vision problem, with the potential to have a positive and widespread impact on public health.
Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
GLDv2 is the largest such dataset to date by a large margin, including over 5M images and 200k distinct instance labels.
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
GLDv2 is the largest such dataset to date by a large margin, including over 5M images and 200k distinct instance labels.
Ranked #1 on
Landmark Recognition
on Google Landmarks Dataset v2 (recognition, validation)
(using extra training data)
CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps
Image geolocalization is the task of identifying the location depicted in a photo based only on its visual information.
Ranked #1 on
Photo geolocation estimation
on Im2GPS
(Reference images metric)
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
Ranked #238 on
Object Detection
on COCO test-dev
Large-Scale Image Retrieval with Attentive Deep Local Features
We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature).
Ranked #2 on
Image Retrieval
on Oxf105k
PlaNet - Photo Geolocation with Convolutional Neural Networks
Is it possible to build a system to determine the location where a photo was taken using just its pixels?
Ranked #1 on
Photo geolocation estimation
on Im2GPS
(Reference images metric)
Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation
We evaluate how different choices of methods and parameters for the individual pipeline steps affect overall system performance and examine their effects for different query categories such as buildings, paintings or sculptures.
