17 citations · 74 across the 13 of their papers we have counts for
13 papers
SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
Lingchen Meng, Shiyi Lan, Hengduo Li +3
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the…
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
Yilun Chen, Zhiding Yu, Yukang Chen +4
False negatives (FN) in 3D object detection, {\em e.g.}, missing predictions of pedestrians, vehicles, or other obstacles, can lead to potentially dangerous situations in autonomou…
FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation
Zhiqi Li, Zhiding Yu, David Austin +4
This technical report summarizes the winning solution for the 3D Occupancy Prediction Challenge, which is held in conjunction with the CVPR 2023 Workshop on End-to-End Autonomous D…
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning
Zhuolin Yang, Wei Ping, Zihan Liu +13
Augmenting pretrained language models (LMs) with a vision encoder (e.g., Flamingo) has obtained the state-of-the-art results in image-to-text generation. However, these models stor…
Vision Transformers Are Good Mask Auto-Labelers
Shiyi Lan, Xitong Yang, Zhiding Yu +3
We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images…
1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track
Junfei Xiao, Zhichao Xu, Shiyi Lan +3
This report describes the winning solution to the Robust Vision Challenge (RVC) semantic segmentation track at ECCV 2022. Our method adopts the FAN-B-Hybrid model as the encoder an…
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
Lingchen Meng, Hengduo Li, Bor-Chun Chen +4
Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of vision tasks recently. While achieving excellent performance…
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
Shiyi Lan, Zhiding Yu, Christopher Choy +5
We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensem…
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
Tianrui Guan, Jun Wang, Shiyi Lan +4
We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales bas…
InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling
Jun Wang, Shiyi Lan, Mingfei Gao +1
Real-time 3D object detection is crucial for autonomous cars. Achieving promising performance with high efficiency, voxel-based approaches have received considerable attention. How…
SaccadeNet: A Fast and Accurate Object Detector
Shiyi Lan, Zhou Ren, Yi Wu +2
Object detection is an essential step towards holistic scene understanding. Most existing object detection algorithms attend to certain object areas once and then predict the objec…
Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN
Shiyi Lan, Ruichi Yu, Gang Yu +1
Recent advances in deep convolutional neural networks (CNNs) have motivated researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local structure has been…
FastMask: Segment Multi-scale Object Candidates in One Shot
Hexiang Hu, Shiyi Lan, Yuning Jiang +2
Objects appear to scale differently in natural images. This fact requires methods dealing with object-centric tasks (e.g. object proposal) to have robust performance over variances…