Search Results for author: Svebor Karaman

Found 15 papers, 8 papers with code

Journalistic Guidelines Aware News Image Captioning

1 code implementation EMNLP 2021 Xuewen Yang, Svebor Karaman, Joel Tetreault, Alex Jaimes

The task of news article image captioning aims to generate descriptive and informative captions for news article images.

Image Captioning

Weakly Supervised Visual Semantic Parsing

1 code implementation CVPR 2020 Alireza Zareian, Svebor Karaman, Shih-Fu Chang

Scene Graph Generation (SGG) aims to extract entities, predicates and their semantic structure from images, enabling deep understanding of visual content, with many applications such as visual reasoning and image retrieval.

Graph Generation Image Retrieval +3

Bridging Knowledge Graphs to Generate Scene Graphs

1 code implementation ECCV 2020 Alireza Zareian, Svebor Karaman, Shih-Fu Chang

Scene graphs are powerful representations that parse images into their abstract semantic elements, i. e., objects and their interactions, which facilitates visual comprehension and explainable reasoning.

Graph Generation Knowledge Graphs +1

Flow-Distilled IP Two-Stream Networks for Compressed Video Action Recognition

no code implementations10 Dec 2019 Shiyuan Huang, Xudong Lin, Svebor Karaman, Shih-Fu Chang

Recent works instead use modern compressed video modalities as an alternative to the RGB spatial stream and improve the inference speed by orders of magnitudes.

Action Recognition Optical Flow Estimation +1

Detecting and Simulating Artifacts in GAN Fake Images

1 code implementation15 Jul 2019 Xu Zhang, Svebor Karaman, Shih-Fu Chang

By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN.

GAN image forensics

Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval

no code implementations4 Mar 2019 Svebor Karaman, Xudong Lin, Xuefeng Hu, Shih-Fu Chang

We propose an unsupervised hashing method which aims to produce binary codes that preserve the ranking induced by a real-valued representation.

Image Retrieval Re-Ranking

Multi-level Multimodal Common Semantic Space for Image-Phrase Grounding

1 code implementation CVPR 2019 Hassan Akbari, Svebor Karaman, Surabhi Bhargava, Brian Chen, Carl Vondrick, Shih-Fu Chang

Following dedicated non-linear mappings for visual features at each level, word, and sentence embeddings, we obtain multiple instantiations of our common semantic space in which comparisons between any target text and the visual content is performed with cosine similarity.

Language Modelling Phrase Grounding +1

Heated-Up Softmax Embedding

1 code implementation ICLR 2019 Xu Zhang, Felix Xinnan Yu, Svebor Karaman, Wei zhang, Shih-Fu Chang

Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples.

Metric Learning

Multimodal Social Media Analysis for Gang Violence Prevention

no code implementations23 Jul 2018 Philipp Blandfort, Desmond Patton, William R. Frey, Svebor Karaman, Surabhi Bhargava, Fei-Tzin Lee, Siddharth Varia, Chris Kedzie, Michael B. Gaskell, Rossano Schifanella, Kathleen McKeown, Shih-Fu Chang

In this paper we partnered computer scientists with social work researchers, who have domain expertise in gang violence, to analyze how public tweets with images posted by youth who mention gang associations on Twitter can be leveraged to automatically detect psychosocial factors and conditions that could potentially assist social workers and violence outreach workers in prevention and early intervention programs.

General Classification

Learning discriminative and transformation covariant local feature detectors.

1 code implementation Computer Vision and Pattern Recognition 2017 Xu Zhang, Felix X. Yu, Svebor Karaman, Shih-Fu Chang

Specifically, we extend the covariant constraint proposed by Lenc and Vedaldi [8] by defining the concepts of “standard patch” and “canonical feature” and leverage these to train a novel robust covariant detector.

Image Retrieval

Learning Discriminative and Transformation Covariant Local Feature Detectors

1 code implementation CVPR 2017 Xu Zhang, Felix X. Yu, Svebor Karaman, Shih-Fu Chang

Specifically, we extend the covariant constraint proposed by Lenc and Vedaldi by defining the concepts of "standard patch" and "canonical feature" and leverage these to train a novel robust covariant detector.

Image Retrieval

Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification

no code implementations8 Jul 2016 Giuseppe Lisanti, Svebor Karaman, Iacopo Masi

In this paper we introduce a method to overcome one of the main challenges of person re-identification in multi-camera networks, namely cross-view appearance changes.

Cross-Modal Person Re-Identification

Deep Image Set Hashing

no code implementations16 Jun 2016 Jie Feng, Svebor Karaman, I-Hong Jhuo, Shih-Fu Chang

Learning-based hashing is often used in large scale image retrieval as they provide a compact representation of each sample and the Hamming distance can be used to efficiently compare two samples.

Image Retrieval

A Multi-Camera Image Processing and Visualization System for Train Safety Assessment

no code implementations28 Jul 2015 Giuseppe Lisanti, Svebor Karaman, Daniele Pezzatini, Alberto del Bimbo

In this paper we present a machine vision system to efficiently monitor, analyze and present visual data acquired with a railway overhead gantry equipped with multiple cameras.

Nested Graph Words for Object Recognition

no code implementations14 Jun 2011 Svebor Karaman, Jenny Benois-Pineau, Rémi Mégret

In this paper, we propose a new, scalable approach for the task of object based image search or object recognition.

Image Retrieval Object Recognition

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