295 citations · 1.2k across the 31 of their papers we have counts for
25 papers · 1 filter
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning
Kun Ding, Haojian Zhang, Qiang Yu +3
We propose a generalized method for boosting the generalization ability of pre-trained vision-language models (VLMs) while fine-tuning on downstream few-shot tasks. The idea is rea…
Reusable Architecture Growth for Continual Stereo Matching
Chenghao Zhang, Gaofeng Meng, Bin Fan +4
The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks…
PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images
Tao Zhang, Kun Ding, Jinyong Wen +4
Self-supervised learning (SSL) for RGB images has achieved significant success, yet there is still limited research on SSL for infrared images, primarily due to three prominent cha…
Pro-tuning: Unified Prompt Tuning for Vision Tasks
Xing Nie, Bolin Ni, Jianlong Chang +6
In computer vision, fine-tuning is the de-facto approach to leverage pre-trained vision models to perform downstream tasks. However, deploying it in practice is quite challenging,…
Prompt Tuning with Soft Context Sharing for Vision-Language Models
Kun Ding, Ying Wang, Pengzhang Liu +4
Vision-language models have recently shown great potential on many tasks in computer vision. Meanwhile, prior work demonstrates prompt tuning designed for vision-language models co…
Differentiable Convolution Search for Point Cloud Processing
Xing Nie, Yongcheng Liu, Shaohong Chen +6
Exploiting convolutional neural networks for point cloud processing is quite challenging, due to the inherent irregular distribution and discrete shape representation of point clou…
HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition
Jianbo Liu, Ying Wang, Shiming Xiang +1
Previous methods for skeleton-based gesture recognition mostly arrange the skeleton sequence into a pseudo picture or spatial-temporal graph and apply deep Convolutional Neural Net…
Adaptive Remote Sensing Image Attribute Learning for Active Object Detection
Nuo Xu, Chunlei Huo, Jiacheng Guo +3
In recent years, deep learning methods bring incredible progress to the field of object detection. However, in the field of remote sensing image processing, existing methods neglec…
AugFPN: Improving Multi-scale Feature Learning for Object Detection
Chaoxu Guo, Bin Fan, Qian Zhang +2
Current state-of-the-art detectors typically exploit feature pyramid to detect objects at different scales. Among them, FPN is one of the representative works that build a feature…
Learning Where to Focus for Efficient Video Object Detection
Zhengkai Jiang, Yu Liu, Ceyuan Yang +5
Transferring existing image-based detectors to the video is non-trivial since the quality of frames is always deteriorated by part occlusion, rare pose, and motion blur. Previous a…
FontGAN: A Unified Generative Framework for Chinese Character Stylization and De-stylization
Xiyan Liu, Gaofeng Meng, Shiming Xiang +1
Chinese character synthesis involves two related aspects, i.e., style maintenance and content consistency. Although some methods have achieved remarkable success in synthesizing a…
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
Yongcheng Liu, Bin Fan, Gaofeng Meng +3
Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficientl…
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Yongcheng Liu, Bin Fan, Shiming Xiang +1
Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional N…
LFFD: A Light and Fast Face Detector for Edge Devices
Yonghao He, Dezhong Xu, Lifang Wu +3
Face detection, as a fundamental technology for various applications, is always deployed on edge devices which have limited memory storage and low computing power. This paper intro…
Progressive Sparse Local Attention for Video object detection
Chaoxu Guo, Bin Fan, Jie Gu +4
Transferring image-based object detectors to the domain of videos remains a challenging problem. Previous efforts mostly exploit optical flow to propagate features across frames, a…
Joint Neural Architecture Search and Quantization
Yukang Chen, Gaofeng Meng, Qian Zhang +4
Designing neural architectures is a fundamental step in deep learning applications. As a partner technique, model compression on neural networks has been widely investigated to gea…
You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization
Xinbang Zhang, Zehao Huang, Naiyan Wang
Recently Neural Architecture Search (NAS) has aroused great interest in both academia and industry, however it remains challenging because of its huge and non-continuous search spa…
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Yongcheng Liu, Lu Sheng, Jing Shao +3
Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs)…
Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network
Yongcheng Liu, Bin Fan, Lingfeng Wang +3
Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade…
A Performance Evaluation of Local Features for Image Based 3D Reconstruction
Bin Fan, Qingqun Kong, Xinchao Wang +4
This paper performs a comprehensive and comparative evaluation of the state of the art local features for the task of image based 3D reconstruction. The evaluated local features co…
Do We Need Binary Features for 3D Reconstruction?
Bin Fan, Qingqun Kong, Wei Sui +5
Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptor…
Accurate Urban Road Centerline Extraction from VHR Imagery via Multiscale Segmentation and Tensor Voting
Guangliang Cheng, Feiyun Zhu, Shiming Xiang +1
It is very useful and increasingly popular to extract accurate road centerlines from very-high-resolution (VHR) re- mote sensing imagery for various applications, such as road map…
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity
Feiyun Zhu, Ying Wang, Bin Fan +2
Hyperspectral unmixing (HU) plays a fundamental role in a wide range of hyperspectral applications. It is still challenging due to the common presence of outlier channels and the l…
Spectral Unmixing via Data-guided Sparsity
Feiyun Zhu, Ying Wang, Bin Fan +3
Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral…