406 citations · 3.1k across the 87 of their papers we have counts for
15 papers · 1 filter
Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language
Hassan Akbari, Hamid Palangi, Jianwei Yang +6
Neuro-symbolic representations have proved effective in learning structure information in vision and language. In this paper, we propose a new model architecture for learning multi…
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…
Uncertainty-Aware Few-Shot Image Classification
Zhizheng Zhang, Cuiling Lan, Wenjun Zeng +2
Few-shot image classification learns to recognize new categories from limited labelled data. Metric learning based approaches have been widely investigated, where a query sample is…
Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding
Long Chen, Wenbo Ma, Jun Xiao +2
The prevailing framework for solving referring expression grounding is based on a two-stage process: 1) detecting proposals with an object detector and 2) grounding the referent to…
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…
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang, Manling Li, Xuan Wang +24
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and rel…
Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification
Zhizheng Zhang, Cuiling Lan, Wenjun Zeng +2
Person Re-identification (ReID) aims at matching a person of interest across images. In convolutional neural network (CNN) based approaches, loss design plays a vital role in pulli…
Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds
Bo Xu, Xu Zhang, Zhixin Li +3
3D urban reconstruction of buildings from remotely sensed imagery has drawn significant attention during the past two decades. While aerial imagery and LiDAR provide higher resolut…
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…
Unifying Specialist Image Embedding into Universal Image Embedding
Yang Feng, Futang Peng, Xu Zhang +7
Deep image embedding provides a way to measure the semantic similarity of two images. It plays a central role in many applications such as image search, face verification, and zero…
Training with Streaming Annotation
Tongtao Zhang, Heng Ji, Shih-Fu Chang +1
In this paper, we address a practical scenario where training data is released in a sequence of small-scale batches and annotation in earlier phases has lower quality than the late…
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…