60 citations · 204 across the 16 of their papers we have counts for
16 papers
Imagine yourself: Tuning-Free Personalized Image Generation
Zecheng He, Bo Sun, Felix Juefei-Xu +14
Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for p…
Learning from Children: Improving Image-Caption Pretraining via Curriculum
Hammad A. Ayyubi, Rahul Lokesh, Alireza Zareian +2
Image-caption pretraining has been quite successfully used for downstream vision tasks like zero-shot image classification and object detection. However, image-caption pretraining…
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning
Huseyin Coskun, Alireza Zareian, Joshua L. Moore +2
Clustering is a ubiquitous tool in unsupervised learning. Most of the existing self-supervised representation learning methods typically cluster samples based on visually dominant…
SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning
Zhecan Wang, Haoxuan You, Liunian Harold Li +5
Answering complex questions about images is an ambitious goal for machine intelligence, which requires a joint understanding of images, text, and commonsense knowledge, as well as…
Open-Vocabulary Object Detection Using Captions
Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu +1
Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object…
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions
Liunian Harold Li, Haoxuan You, Zhecan Wang +3
Pre-trained contextual vision-and-language (V&L) models have achieved impressive performance on various benchmarks. However, existing models require a large amount of parallel imag…
Analogical Reasoning for Visually Grounded Language Acquisition
Bo Wu, Haoyu Qin, Alireza Zareian +2
Children acquire language subconsciously by observing the surrounding world and listening to descriptions. They can discover the meaning of words even without explicit language kno…
Learning Visual Commonsense for Robust Scene Graph Generation
Alireza Zareian, Zhecan Wang, Haoxuan You +1
Scene graph generation models understand the scene through object and predicate recognition, but are prone to mistakes due to the challenges of perception in the wild. Perception e…
Cross-media Structured Common Space for Multimedia Event Extraction
Manling Li, Alireza Zareian, Qi Zeng +4
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents. We develop the first benchmark and collect…
Weakly Supervised Visual Semantic Parsing
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 su…
Bridging Knowledge Graphs to Generate Scene Graphs
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…
General Partial Label Learning via Dual Bipartite Graph Autoencoder
Brian Chen, Bo Wu, Alireza Zareian +2
We formulate a practical yet challenging problem: General Partial Label Learning (GPLL). Compared to the traditional Partial Label Learning (PLL) problem, GPLL relaxes the supervis…
CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation
Jiawei Ma, Zheng Shou, Alireza Zareian +3
Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements. For example, air quality monit…
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Hang Gao, Zheng Shou, Alireza Zareian +2
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been rec…
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos
Zheng Shou, Jonathan Chan, Alireza Zareian +2
Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need…
Reconstruction of Sub-Nyquist Random Sampling for Sparse and Multi-Band Signals
Amir Zandieh, Alireza Zareian, Masoumeh Azghani +1
As technology grows, higher frequency signals are required to be processed in various applications. In order to digitize such signals, conventional analog to digital convertors are…