6.5k citations · 10.4k across the 118 of their papers we have counts for
11 papers · 2 filters
Multi-source Few-shot Domain Adaptation
Xiangyu Yue, Zangwei Zheng, Colorado Reed +3
Multi-source Domain Adaptation (MDA) aims to transfer predictive models from multiple, fully-labeled source domains to an unlabeled target domain. However, in many applications, re…
How Much Can CLIP Benefit Vision-and-Language Tasks?
Sheng Shen, Liunian Harold Li, Hao Tan +5
Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to pe…
Scene-aware Learning Network for Radar Object Detection
Zangwei Zheng, Xiangyu Yue, Kurt Keutzer +1
Object detection is essential to safe autonomous or assisted driving. Previous works usually utilize RGB images or LiDAR point clouds to identify and localize multiple objects in s…
Affective Image Content Analysis: Two Decades Review and New Perspectives
Sicheng Zhao, Xingxu Yao, Jufeng Yang +5
Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, ext…
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
Chenfeng Xu, Shijia Yang, Tomer Galanti +7
3D point-clouds and 2D images are different visual representations of the physical world. While human vision can understand both representations, computer vision models designed fo…
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference
Zhen Dong, Yizhao Gao, Qijing Huang +3
Automatic algorithm-hardware co-design for DNN has shown great success in improving the performance of DNNs on FPGAs. However, this process remains challenging due to the intractab…
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
Xiangyu Yue, Zangwei Zheng, Shanghang Zhang +4
Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully-labeled source domain to an unlabeled target domain. In some applications, however, it is expensive ev…
Self-Supervised Pretraining Improves Self-Supervised Pretraining
Colorado J. Reed, Xiangyu Yue, Ani Nrusimha +9
While self-supervised pretraining has proven beneficial for many computer vision tasks, it requires expensive and lengthy computation, large amounts of data, and is sensitive to da…
A Survey of Quantization Methods for Efficient Neural Network Inference
Amir Gholami, Sehoon Kim, Zhen Dong +3
As soon as abstract mathematical computations were adapted to computation on digital computers, the problem of efficient representation, manipulation, and communication of the nume…
Region Similarity Representation Learning
Tete Xiao, Colorado J Reed, Xiaolong Wang +2
We present Region Similarity Representation Learning (ReSim), a new approach to self-supervised representation learning for localization-based tasks such as object detection and se…
Hessian-Aware Pruning and Optimal Neural Implant
Shixing Yu, Zhewei Yao, Amir Gholami +4
Pruning is an effective method to reduce the memory footprint and FLOPs associated with neural network models. However, existing structured-pruning methods often result in signific…