Search Results for author: Richard J. Radke

Found 13 papers, 4 papers with code

Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation

no code implementations10 Jul 2022 Ashraful Islam, Ben Lundell, Harpreet Sawhney, Sudipta Sinha, Peter Morales, Richard J. Radke

We evaluate our SSL approach on two downstream tasks -- object detection and semantic segmentation, using COCO, PASCAL VOC, and CityScapes datasets.

object-detection Object Detection +3

Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

1 code implementation NeurIPS 2021 Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Richard J. Radke

As the base dataset and unlabeled dataset are from different domains, projecting the target images in the class-domain of the base dataset with a fixed pretrained model might be sub-optimal.

cross-domain few-shot learning

Towards Visually Explaining Similarity Models

no code implementations13 Aug 2020 Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J. Radke, Ziyan Wu

We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without these constraints.

Image Retrieval Metric Learning +3

Weakly Supervised Temporal Action Localization Using Deep Metric Learning

1 code implementation21 Jan 2020 Ashraful Islam, Richard J. Radke

We propose a classification module to generate action labels for each segment in the video, and a deep metric learning module to learn the similarity between different action instances.

Metric Learning Temporal Localization +3

Multi-person Spatial Interaction in a Large Immersive Display Using Smartphones as Touchpads

no code implementations26 Nov 2019 Gyanendra Sharma, Richard J. Radke

In this paper, we present a multi-user interaction interface for a large immersive space that supports simultaneous screen interactions by combining (1) user input via personal smartphones and Bluetooth microphones, (2) spatial tracking via an overhead array of Kinect sensors, and (3) WebSocket interfaces to a webpage running on the large screen.

Visual Similarity Attention

no code implementations18 Nov 2019 Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J. Radke, Ziyan Wu

While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i. e., explaining why the input set of images is similar or dissimilar.

Image Retrieval Person Re-Identification +2

Towards Visually Explaining Variational Autoencoders

2 code implementations CVPR 2020 Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam, Ziyan Wu, Bir Bhanu, Richard J. Radke, Octavia Camps

We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions.


Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization

no code implementations ACL 2019 Manling Li, Lingyu Zhang, Heng Ji, Richard J. Radke

Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused.

Meeting Summarization Text Generation

Measuring the Temporal Behavior of Real-World Person Re-Identification

no code implementations16 Aug 2018 Meng Zheng, Srikrishna Karanam, Richard J. Radke

Designing real-world person re-identification (re-id) systems requires attention to operational aspects not typically considered in academic research.

Person Re-Identification

Rank Persistence: Assessing the Temporal Performance of Real-World Person Re-Identification

no code implementations2 Jun 2017 Srikrishna Karanam, Eric Lam, Richard J. Radke

Designing useful person re-identification systems for real-world applications requires attention to operational aspects not typically considered in academic research.

Person Re-Identification

A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets

3 code implementations31 May 2016 Srikrishna Karanam, Mengran Gou, Ziyan Wu, Angels Rates-Borras, Octavia Camps, Richard J. Radke

To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques.

Metric Learning Person Re-Identification

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