315 citations · 2.4k across the 76 of their papers we have counts for
80 papers
Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!
Zaid Khan, Vijay Kumar BG, Samuel Schulter +3
Finetuning a large vision language model (VLM) on a target dataset after large scale pretraining is a dominant paradigm in visual question answering (VQA). Datasets for specialized…
Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction
Yi Xu, Armin Bazarjani, Hyung-gun Chi +2
Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed s…
NeRFInvertor: High Fidelity NeRF-GAN Inversion for Single-shot Real Image Animation
Yu Yin, Kamran Ghasedi, HsiangTao Wu +3
Nerf-based Generative models have shown impressive capacity in generating high-quality images with consistent 3D geometry. Despite successful synthesis of fake identity images rand…
Towards Layer-wise Image Vectorization
Xu Ma, Yuqian Zhou, Xingqian Xu +5
Image rasterization is a mature technique in computer graphics, while image vectorization, the reverse path of rasterization, remains a major challenge. Recent advanced deep learni…
Generating Topological Structure of Floorplans from Room Attributes
Yin Yu, Hutchcroft Will, Khosravan Naji +3
Analysis of indoor spaces requires topological information. In this paper, we propose to extract topological information from room attributes using what we call Iterative and adapt…
Adaptive Trajectory Prediction via Transferable GNN
Yi Xu, Lichen Wang, Yizhou Wang +1
Pedestrian trajectory prediction is an essential component in a wide range of AI applications such as autonomous driving and robotics. Existing methods usually assume the training…
Single-Stream Multi-Level Alignment for Vision-Language Pretraining
Zaid Khan, Vijay Kumar BG, Xiang Yu +3
Self-supervised vision-language pretraining from pure images and text with a contrastive loss is effective, but ignores fine-grained alignment due to a dual-stream architecture tha…
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
Xu Ma, Can Qin, Haoxuan You +2
Point cloud analysis is challenging due to irregularity and unordered data structure. To capture the 3D geometries, prior works mainly rely on exploring sophisticated local geometr…
Adversarial Memory Networks for Action Prediction
Zhiqiang Tao, Yue Bai, Handong Zhao +3
Action prediction aims to infer the forthcoming human action with partially-observed videos, which is a challenging task due to the limited information underlying early observation…
Semi-supervised Domain Adaptive Structure Learning
Can Qin, Lichen Wang, Qianqian Ma +3
Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift ac…
The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces
Joseph P. Robinson, Can Qin, Ming Shao +3
Recognizing Families In the Wild (RFIW), held as a data challenge in conjunction with the 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG), is a la…
SLAP: Self-supervised Anomaly Detection with Adversarial Perturbation
Yizhou Wang, Can Qin, Rongzhe Wei +3
Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. In this work, we propose a novel and powerful framework, dubbed…
Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble
Songyao Jiang, Bin Sun, Lichen Wang +3
Sign language is commonly used by deaf or mute people to communicate but requires extensive effort to master. It is usually performed with the fast yet delicate movement of hand ge…
Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space Translation
Zaid Khan, Yun Fu
Multimodal target/aspect sentiment classification combines multimodal sentiment analysis and aspect/target sentiment classification. The goal of the task is to combine vision and l…
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging
Jiamian Wang, Yulun Zhang, Xin Yuan +2
The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way. It is generally implemented by two components: an op…
Rethinking Adam: A Twofold Exponential Moving Average Approach
Yizhou Wang, Yue Kang, Can Qin +4
Adaptive gradient methods, e.g. \textsc{Adam}, have achieved tremendous success in machine learning. Scaling the learning rate element-wisely by a certain form of second moment est…
Dynamical Isometry: The Missing Ingredient for Neural Network Pruning
Huan Wang, Can Qin, Yue Bai +1
Several recent works [40, 24] observed an interesting phenomenon in neural network pruning: A larger finetuning learning rate can improve the final performance significantly. Unfor…
Skeleton Aware Multi-modal Sign Language Recognition
Songyao Jiang, Bin Sun, Lichen Wang +3
Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master. Sign Language Recognition (SLR) aims to bridge the gap be…
RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks
Kai Li, Curtis Wigington, Chris Tensmeyer +5
Cross-Domain Detection (XDD) aims to train an object detector using labeled image from a source domain but have good performance in the target domain with only unlabeled images. Ex…
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution
Xiaoyu Xiang, Yapeng Tian, Yulun Zhang +3
In this paper, we address the space-time video super-resolution, which aims at generating a high-resolution (HR) slow-motion video from a low-resolution (LR) and low frame rate (LF…
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation
Kai Li, Chang Liu, Handong Zhao +2
This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled sample…
Balancing Biases and Preserving Privacy on Balanced Faces in the Wild
Joseph P Robinson, Can Qin, Yann Henon +2
There are demographic biases present in current facial recognition (FR) models. To measure these biases across different ethnic and gender subgroups, we introduce our Balanced Face…
VINS: Visual Search for Mobile User Interface Design
Sara Bunian, Kai Li, Chaima Jemmali +3
Searching for relative mobile user interface (UI) design examples can aid interface designers in gaining inspiration and comparing design alternatives. However, finding such design…
One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision
Zaid Khan, Yun Fu
Computer vision is widely deployed, has highly visible, society altering applications, and documented problems with bias and representation. Datasets are critical for benchmarking…