386 citations · 427 across the 18 of their papers we have counts for
19 papers
HaloBlock: Faster and Better Decoding for Diffusion Large Language Models
Zhaohui Zheng, Chenhang He, Jinrui Zhang +2
Diffusion large language models (dLLMs) have attracted increasing research attention due to their bidirectional attention and arbitrary order decoding. However, there are two issue…
DEL: Digit Entropy Loss for Numerical Learning of Large Language Models
Zhaohui Zheng, Chenhang He, Shihao Wang +3
Number prediction stands as a fundamental capability of large language models (LLMs) in mathematical problem-solving and code generation. The widely adopted maximum likelihood esti…
BEVDilation: LiDAR-Centric Multi-Modal Fusion for 3D Object Detection
Guowen Zhang, Chenhang He, Liyi Chen +1
Integrating LiDAR and camera information in the bird's eye view (BEV) representation has demonstrated its effectiveness in 3D object detection. However, because of the fundamental…
Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding
Ruihuang Li, Zhengqiang Zhang, Chenhang He +3
Recent vision-language pre-training models have exhibited remarkable generalization ability in zero-shot recognition tasks. Previous open-vocabulary 3D scene understanding methods…
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models
Yabin Zhang, Wenjie Zhu, Chenhang He +1
Out-of-distribution (OOD) detection is crucial for model reliability, as it identifies samples from unknown classes and reduces errors due to unexpected inputs. Vision-Language Mod…
Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection
Guowen Zhang, Lue Fan, Chenhang He +3
Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D obje…
ScatterFormer: Efficient Voxel Transformer with Scattered Linear Attention
Chenhang He, Ruihuang Li, Guowen Zhang +1
Window-based transformers excel in large-scale point cloud understanding by capturing context-aware representations with affordable attention computation in a more localized manner…
Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution
Xi Yang, Chenhang He, Jianqi Ma +1
Real-world low-resolution (LR) videos have diverse and complex degradations, imposing great challenges on video super-resolution (VSR) algorithms to reproduce their high-resolution…
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations
Weiwei Lin, Chenhang He, Man-Wai Mak +1
Self-supervised learning (SSL) speech models such as wav2vec and HuBERT have demonstrated state-of-the-art performance on automatic speech recognition (ASR) and proved to be extrem…
One-to-Few Label Assignment for End-to-End Dense Detection
Shuai Li, Minghan Li, Ruihuang Li +2
One-to-one (o2o) label assignment plays a key role for transformer based end-to-end detection, and it has been recently introduced in fully convolutional detectors for end-to-end d…
MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences
Chenhang He, Ruihuang Li, Yabin Zhang +2
Point cloud sequences are commonly used to accurately detect 3D objects in applications such as autonomous driving. Current top-performing multi-frame detectors mostly follow a Det…
SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation
Ruihuang Li, Chenhang He, Yabin Zhang +3
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image fea…
DynaMask: Dynamic Mask Selection for Instance Segmentation
Ruihuang Li, Chenhang He, Shuai Li +2
The representative instance segmentation methods mostly segment different object instances with a mask of the fixed resolution, e.g., 28*28 grid. However, a low-resolution mask los…
Masked Surfel Prediction for Self-Supervised Point Cloud Learning
Yabin Zhang, Jiehong Lin, Chenhang He +3
Masked auto-encoding is a popular and effective self-supervised learning approach to point cloud learning. However, most of the existing methods reconstruct only the masked points…
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds
Chenhang He, Ruihuang Li, Shuai Li +1
Transformer has demonstrated promising performance in many 2D vision tasks. However, it is cumbersome to compute the self-attention on large-scale point cloud data because point cl…
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
Ruihuang Li, Shuai Li, Chenhang He +3
Domain adaptive semantic segmentation aims to learn a model with the supervision of source domain data, and produce satisfactory dense predictions on unlabeled target domain. One p…
A Dual Weighting Label Assignment Scheme for Object Detection
Shuai Li, Chenhang He, Ruihuang Li +1
Label assignment (LA), which aims to assign each training sample a positive (pos) and a negative (neg) loss weight, plays an important role in object detection. Existing LA methods…
Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth
Chenhang He, Jianqiang Huang, Xian-Sheng Hua +1
Current geometry-based monocular 3D object detection models can efficiently detect objects by leveraging perspective geometry, but their performance is limited due to the absence o…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…