348 citations · 852 across the 24 of their papers we have counts for
24 papers
BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs
Yang Zhao, Zhijie Lin, Daquan Zhou +3
LLMs have demonstrated remarkable abilities at interacting with humans through language, especially with the usage of instruction-following data. Recent advancements in LLMs, such…
Disentangled Pre-training for Image Matting
Yanda Li, Zilong Huang, Gang Yu +3
Image matting requires high-quality pixel-level human annotations to support the training of a deep model in recent literature. Whereas such annotation is costly and hard to scale,…
SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition
Qiang Wan, Zilong Huang, Jiachen Lu +2
Since the introduction of Vision Transformers, the landscape of many computer vision tasks (e.g., semantic segmentation), which has been overwhelmingly dominated by CNNs, recently…
Executing your Commands via Motion Diffusion in Latent Space
Xin Chen, Biao Jiang, Wen Liu +5
We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or…
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Andrey Ignatov, Grigory Malivenko, Radu Timofte +36
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…
Coordinates Are NOT Lonely -- Codebook Prior Helps Implicit Neural 3D Representations
Fukun Yin, Wen Liu, Zilong Huang +3
Implicit neural 3D representation has achieved impressive results in surface or scene reconstruction and novel view synthesis, which typically uses the coordinate-based multi-layer…
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation
Wenqiang Zhang, Zilong Huang, Guozhong Luo +5
Although vision transformers (ViTs) have achieved great success in computer vision, the heavy computational cost hampers their applications to dense prediction tasks such as semant…
SeMask: Semantically Masked Transformers for Semantic Segmentation
Jitesh Jain, Anukriti Singh, Nikita Orlov +4
Finetuning a pretrained backbone in the encoder part of an image transformer network has been the traditional approach for the semantic segmentation task. However, such an approach…
Shuffle Transformer with Feature Alignment for Video Face Parsing
Rui Zhang, Yang Han, Zilong Huang +4
This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR…
Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer
Zilong Huang, Youcheng Ben, Guozhong Luo +3
Very recently, Window-based Transformers, which computed self-attention within non-overlapping local windows, demonstrated promising results on image classification, semantic segme…
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Grigory Malivenko, David Plowman +35
Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually…
Half-Real Half-Fake Distillation for Class-Incremental Semantic Segmentation
Zilong Huang, Wentian Hao, Xinggang Wang +4
Despite their success for semantic segmentation, convolutional neural networks are ill-equipped for incremental learning, \ie, adapting the original segmentation model as new class…
Human De-occlusion: Invisible Perception and Recovery for Humans
Qiang Zhou, Shiyin Wang, Yitong Wang +2
In this paper, we tackle the problem of human de-occlusion which reasons about occluded segmentation masks and invisible appearance content of humans. In particular, a two-stage fr…
High-Resolution Deep Image Matting
Haichao Yu, Ning Xu, Zilong Huang +2
Image matting is a key technique for image and video editing and composition. Conventionally, deep learning approaches take the whole input image and an associated trimap to infer…
Deep Learning-Based Automated Image Segmentation for Concrete Petrographic Analysis
Yu Song, Zilong Huang, Chuanyue Shen +2
The standard petrography test method for measuring air voids in concrete (ASTM C457) requires a meticulous and long examination of sample phase composition under a stereomicroscope…
The 1st Agriculture-Vision Challenge: Methods and Results
Mang Tik Chiu, Xingqian Xu, Kai Wang +39
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially f…
AlignSeg: Feature-Aligned Segmentation Networks
Zilong Huang, Yunchao Wei, Xinggang Wang +3
Aggregating features in terms of different convolutional blocks or contextual embeddings has been proven to be an effective way to strengthen feature representations for semantic s…
Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
Mang Tik Chiu, Xingqian Xu, Yunchao Wei +12
The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its appli…
SPGNet: Semantic Prediction Guidance for Scene Parsing
Bowen Cheng, Liang-Chieh Chen, Yunchao Wei +6
Multi-scale context module and single-stage encoder-decoder structure are commonly employed for semantic segmentation. The multi-scale context module refers to the operations to ag…
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation
Qiang Zhou, Zilong Huang, Lichao Huang +5
Video object segmentation (VOS) aims at pixel-level object tracking given only the annotations in the first frame. Due to the large visual variations of objects in video and the la…
CCNet: Criss-Cross Attention for Semantic Segmentation
Zilong Huang, Xinggang Wang, Yunchao Wei +4
Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a Criss-Cross Network (CCNet) for obtaining full-im…
Devil in the Details: Towards Accurate Single and Multiple Human Parsing
Tao Ruan, Ting Liu, Zilong Huang +4
Human parsing has received considerable interest due to its wide application potentials. Nevertheless, it is still unclear how to develop an accurate human parsing system in an eff…
Point Linking Network for Object Detection
Xinggang Wang, Kaibing Chen, Zilong Huang +2
Object detection is a core problem in computer vision. With the development of deep ConvNets, the performance of object detectors has been dramatically improved. The deep ConvNets…
Deep Patch Learning for Weakly Supervised Object Classification and Discovery
Peng Tang, Xinggang Wang, Zilong Huang +2
Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background.…