Search Results for author:
Found 12 papers, 5 papers with code
Towards Automated Testing and Robustification by Semantic Adversarial Data Generation
Constrained adversarial optimization of object appearance through this synthesizer produces rare/difficult versions of an object which fool the target object detector.
Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate new data.
Multi-Source Unsupervised Domain Adaptation
Object Detection
+1
Diverse and Relevant Visual Storytelling with Scene Graph Embeddings
A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text.
Ranked #5 on
Visual Storytelling
on VIST
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing
Despite significant success in Visual Question Answering (VQA), VQA models have been shown to be notoriously brittle to linguistic variations in the questions.
Not Using the Car to See the Sidewalk -- Quantifying and Controlling the Effects of Context in Classification and Segmentation
Importance of visual context in scene understanding tasks is well recognized in the computer vision community.
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation
We propose a method to quantify the sensitivity of black-box vision models to visual context by editing images to remove selected objects and measuring the response of the target models.
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets.
$A^{4}NT$: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation
In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ($A^4NT$), to combat such text-based adversaries.
Paying Attention to Descriptions Generated by Image Captioning Models
To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene.
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
While strong progress has been made in image captioning over the last years, machine and human captions are still quite distinct.
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation
We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset.
Video captioning with recurrent networks based on frame- and video-level features and visual content classification
In this paper, we describe the system for generating textual descriptions of short video clips using recurrent neural networks (RNN), which we used while participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015.
