27 citations · 93 across the 15 of their papers we have counts for
8 papers · 1 filter
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk +4
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reas…
Revisiting Image-Language Networks for Open-ended Phrase Detection
Bryan A. Plummer, Kevin J. Shih, Yichen Li +4
Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image. In this paper we address a more…
Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential Fixations
Hanxiao Wang, Venkatesh Saligrama, Stan Sclaroff +1
We consider the problem of fine-grained classification on an edge camera device that has limited power. The edge device must sparingly interact with the cloud to minimize communica…
Hashing with Binary Matrix Pursuit
Fatih Cakir, Kun He, Stan Sclaroff
We propose theoretical and empirical improvements for two-stage hashing methods. We first provide a theoretical analysis on the quality of the binary codes and show that, under mil…
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
Andrea Zunino, Sarah Adel Bargal, Pietro Morerio +3
We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths. In this work, we utilize…
Local Descriptors Optimized for Average Precision
Kun He, Yan Lu, Stan Sclaroff
Extraction of local feature descriptors is a vital stage in the solution pipelines for numerous computer vision tasks. Learning-based approaches improve performance in certain task…