Search Results for author:
Found 15 papers, 8 papers with code
Journalistic Guidelines Aware News Image Captioning
The task of news article image captioning aims to generate descriptive and informative captions for news article images.
Weakly Supervised Visual Semantic Parsing
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.
Bridging Knowledge Graphs to Generate Scene Graphs
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.
Flow-Distilled IP Two-Stream Networks for Compressed Video Action Recognition
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.
Detecting and Simulating Artifacts in GAN Fake Images
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.
Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval
We propose an unsupervised hashing method which aims to produce binary codes that preserve the ranking induced by a real-valued representation.
Multi-level Multimodal Common Semantic Space for Image-Phrase Grounding
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.
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Phrase Grounding
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Heated-Up Softmax Embedding
Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples.
Multimodal Social Media Analysis for Gang Violence Prevention
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.
Learning Discriminative and Transformation Covariant Local Feature Detectors
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.
Learning discriminative and transformation covariant local feature detectors.
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.
Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification
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.
Deep Image Set Hashing
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.
A Multi-Camera Image Processing and Visualization System for Train Safety Assessment
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
In this paper, we propose a new, scalable approach for the task of object based image search or object recognition.
