74 citations · 226 across the 53 of their papers we have counts for
16 papers · 1 filter
WeaQA: Weak Supervision via Captions for Visual Question Answering
Pratyay Banerjee, Tejas Gokhale, Yezhou Yang +1
Methodologies for training visual question answering (VQA) models assume the availability of datasets with human-annotated \textit{Image-Question-Answer} (I-Q-A) triplets. This has…
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations
Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura +3
While existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real-world sett…
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering
Tejas Gokhale, Pratyay Banerjee, Chitta Baral +1
While progress has been made on the visual question answering leaderboards, models often utilize spurious correlations and priors in datasets under the i.i.d. setting. As such, eva…
Decentralized Attribution of Generative Models
Changhoon Kim, Yi Ren, Yezhou Yang
Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement. One solution to these threats is model attributi…
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling
Xin Ye, Yezhou Yang
Despite the significant success at enabling robots with autonomous behaviors makes deep reinforcement learning a promising approach for robotic object search task, the deep reinfor…
Low to High Dimensional Modality Hallucination using Aggregated Fields of View
Kausic Gunasekar, Qiang Qiu, Yezhou Yang
Real-world robotics systems deal with data from a multitude of modalities, especially for tasks such as navigation and recognition. The performance of those systems can drastically…
Weak Supervision and Referring Attention for Temporal-Textual Association Learning
Zhiyuan Fang, Shu Kong, Zhe Wang +2
A system capturing the association between video frames and textual queries offer great potential for better video analysis. However, training such a system in a fully supervised w…
Resisting Crowd Occlusion and Hard Negatives for Pedestrian Detection in the Wild
Zhe Wang, Jun Wang, Yezhou Yang
Pedestrian detection has been heavily studied in the last decade due to its wide application. Despite incremental progress, crowd occlusion and hard negatives are still challenging…
Learning hierarchical behavior and motion planning for autonomous driving
Jingke Wang, Yue Wang, Dongkun Zhang +2
Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve th…
ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language
Zhe Wang, Zhiyuan Fang, Jun Wang +1
Person search by natural language aims at retrieving a specific person in a large-scale image pool that matches the given textual descriptions. While most of the current methods tr…
Visuo-Linguistic Question Answering (VLQA) Challenge
Shailaja Keyur Sampat, Yezhou Yang, Chitta Baral
Understanding images and text together is an important aspect of cognition and building advanced Artificial Intelligence (AI) systems. As a community, we have achieved good benchma…
memeBot: Towards Automatic Image Meme Generation
Aadhavan Sadasivam, Kausic Gunasekar, Hasan Davulcu +1
Image memes have become a widespread tool used by people for interacting and exchanging ideas over social media, blogs, and open messengers. This work proposes to treat automatic i…
Enabling Incremental Knowledge Transfer for Object Detection at the Edge
Mohammad Farhadi Bajestani, Mehdi Ghasemi, Sarma Vrudhula +1
Object detection using deep neural networks (DNNs) involves a huge amount of computation which impedes its implementation on resource/energy-limited user-end devices. The reason fo…
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
Zhiyuan Fang, Tejas Gokhale, Pratyay Banerjee +2
Captioning is a crucial and challenging task for video understanding. In videos that involve active agents such as humans, the agent's actions can bring about myriad changes in the…
From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN)
Xin Ye, Yezhou Yang
Visual Indoor Navigation (VIN) task has drawn increasing attention from the data-driven machine learning communities especially with the recently reported success from learning-bas…
VQA-LOL: Visual Question Answering under the Lens of Logic
Tejas Gokhale, Pratyay Banerjee, Chitta Baral +1
Logical connectives and their implications on the meaning of a natural language sentence are a fundamental aspect of understanding. In this paper, we investigate whether visual que…