8 citations · 27 across the 17 of their papers we have counts for
18 papers
xHC: Expanded Hyper-Connections
Xiangdong Zhang, Xiaohan Qin, Sunan Zou +10
Hyper-Connections (HC) expand the residual stream of Transformers into parallel streams, providing a form of memory scaling beyond model width and depth. Manifold-Constrained H…
ReactSim-Bench: Benchmarking Reactive Behavior World Model Simulation in Autonomous Driving
Zhiyuan Zhang, Yanlun Peng, Jianing Zhang +7
Reactive capability is a key property of data-driven behavior world model simulators for autonomous driving simulation systems. With this capability, simulated world agents can res…
Resolving Representation Ambiguity in Feedforward Novel View Synthesis Transformer via Semantic-Spatial Decoupling
Yihang Wu, Yihang Sun, Shaofeng Zhang +4
Transformer-based models have advanced feedforward novel view synthesis (NVS). Current architectures such as GS-LRM and LVSM mix semantic information (e.g., RGB) and spatial inform…
Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations
Zhiyuan Zhang, Zhenghao Jin, Yanlun Peng +8
Robustness is a critical requirement for deploying autonomous driving systems in the real world. Existing robustness benchmarks for autonomous driving have made important progress…
NITP: Next Implicit Token Prediction for LLM Pre-training
Xiangdong Zhang, Debing Zhang, Shaofeng Zhang +3
Standard next-token prediction (NTP) supervises language models solely through discrete labels in the output logit space. We argue that this sparse one-hot supervision leaves the l…
Dual-Branch Center-Surrounding Contrast: Rethinking Contrastive Learning for 3D Point Clouds
Shaofeng Zhang, Xuanqi Chen, Xiangdong Zhang +2
Most existing self-supervised learning (SSL) approaches for 3D point clouds are dominated by generative methods based on Masked Autoencoders (MAE). However, these generative method…
Spatial Retrieval Augmented Autonomous Driving
Xiaosong Jia, Chenhe Zhang, Yule Jiang +8
Existing autonomous driving systems rely on onboard sensors (cameras, LiDAR, IMU, etc) for environmental perception. However, this paradigm is limited by the drive-time perception…
Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views
Xiangdong Zhang, Shaofeng Zhang, Junchi Yan
Point cloud learning, especially in a self-supervised way without manual labels, has gained growing attention in both vision and learning communities due to its potential utility i…
VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models
Xiangdong Zhang, Jiaqi Liao, Shaofeng Zhang +4
Recent advancements in text-to-video (T2V) diffusion models have enabled high-fidelity and realistic video synthesis. However, current T2V models often struggle to generate physica…
Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances
Yi Yu, Botao Ren, Peiyuan Zhang +6
With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning OOD from point annotations has gained gre…
Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation
Yan Li, Weiwei Guo, Xue Yang +5
In recent years, aerial object detection has been increasingly pivotal in various earth observation applications. However, current algorithms are limited to detecting a set of pre-…
PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
Xiangdong Zhang, Shaofeng Zhang, Junchi Yan
Masked autoencoder has been widely explored in point cloud self-supervised learning, whereby the point cloud is generally divided into visible and masked parts. These methods typic…
Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach
Shaofeng Zhang, Jinfa Huang, Qiang Zhou +4
Image outpainting aims to generate the content of an input sub-image beyond its original boundaries. It is an important task in content generation yet remains an open problem for g…
On the Evaluation and Refinement of Vision-Language Instruction Tuning Datasets
Ning Liao, Shaofeng Zhang, Renqiu Xia +3
There is an emerging line of research on multimodal instruction tuning, and a line of benchmarks has been proposed for evaluating these models recently. Instead of evaluating the m…
RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension
Qiang Zhou, Chaohui Yu, Shaofeng Zhang +3
In this work, we investigate extending the comprehension of Multi-modal Large Language Models (MLLMs) to regional objects. To this end, we propose to extract features corresponding…
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning
Shaofeng Zhang, Feng Zhu, Rui Zhao +1
We propose ADCLR: A ccurate and D ense Contrastive Representation Learning, a novel self-supervised learning framework for learning accurate and dense vision representation. To ext…
Localized Contrastive Learning on Graphs
Hengrui Zhang, Qitian Wu, Yu Wang +3
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its qu…
DOTIN: Dropping Task-Irrelevant Nodes for GNNs
Shaofeng Zhang, Feng Zhu, Junchi Yan +2
Scalability is an important consideration for deep graph neural networks. Inspired by the conventional pooling layers in CNNs, many recent graph learning approaches have introduced…