7 citations · 22 across the 14 of their papers we have counts for
13 papers · 1 filter
Will It Zero-Shot?: Predicting Zero-Shot Classification Performance For Arbitrary Queries
Kevin Robbins, Xiaotong Liu, Yu Wu +4
Vision-Language Models like CLIP create aligned embedding spaces for text and images, making it possible for anyone to build a visual classifier by simply naming the classes they w…
QuARI: Query Adaptive Retrieval Improvement
Eric Xing, Abby Stylianou, Robert Pless +1
Massive-scale pretraining has made vision-language models increasingly popular for image-to-image and text-to-image retrieval across a broad collection of domains. However, these m…
ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval
Eric Xing, Pranavi Kolouju, Robert Pless +2
Composed image retrieval (CIR) is the task of retrieving a target image specified by a query image and a relative text that describes a semantic modification to the query image. Ex…
good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval
Pranavi Kolouju, Eric Xing, Robert Pless +2
Composed image retrieval (CIR) enables users to search images using a reference image combined with textual modifications. Recent advances in vision-language models have improved C…
Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning
Xin Xing, Zhexiao Xiong, Abby Stylianou +3
This paper presents a novel approach to Single-Positive Multi-label Learning. In general multi-label learning, a model learns to predict multiple labels or categories for a single…
What Does TERRA-REF's High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community?
David LeBauer, Max Burnette, Noah Fahlgren +3
A core objective of the TERRA-REF project was to generate an open-access reference dataset for the evaluation of sensing technologies to study plants under field conditions. The TE…
Multi-resolution Outlier Pooling for Sorghum Classification
Chao Ren, Justin Dulay, Gregory Rolwes +3
Automated high throughput plant phenotyping involves leveraging sensors, such as RGB, thermal and hyperspectral cameras (among others), to make large scale and rapid measurements o…
The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset
Rashmi Kamath, Gregory Rolwes, Samuel Black +1
Hotel recognition is an important task for human trafficking investigations since victims are often photographed in hotel rooms. Identifying these hotels is vital to trafficking in…
Hard negative examples are hard, but useful
Hong Xuan, Abby Stylianou, Xiaotong Liu +1
Triplet loss is an extremely common approach to distance metric learning. Representations of images from the same class are optimized to be mapped closer together in an embedding s…
TraffickCam: Explainable Image Matching For Sex Trafficking Investigations
Abby Stylianou, Richard Souvenir, Robert Pless
Investigations of sex trafficking sometimes have access to photographs of victims in hotel rooms. These images directly link victims to places, which can help verify where victims…
Improved Embeddings with Easy Positive Triplet Mining
Hong Xuan, Abby Stylianou, Robert Pless
Deep metric learning seeks to define an embedding where semantically similar images are embedded to nearby locations, and semantically dissimilar images are embedded to distant loc…
Hotels-50K: A Global Hotel Recognition Dataset
Abby Stylianou, Hong Xuan, Maya Shende +3
Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have b…
Visualizing Deep Similarity Networks
Abby Stylianou, Richard Souvenir, Robert Pless
For convolutional neural network models that optimize an image embedding, we propose a method to highlight the regions of images that contribute most to pairwise similarity. This w…