31 citations · 62 across the 21 of their papers we have counts for
25 papers
GeoNest: Learning to Select Failure-Aware Neighborhoods for the Irregular Knapsack Problem in a Circular Container
Zhongman Du, Huiming Zhang, Linlin Yang +2
The two-dimensional irregular knapsack problem in a fixed circular container is an important combinatorial optimization problem for maximizing material utilization in manufacturing…
GraphHCA: Closed-Form Hindsight Credit Assignment for Long-Horizon LLM Agents
Haodong Zhu, Yangyang Ren, Changbai Li +4
Group-based reinforcement learning (RL) has advanced large language models (LLMs) and is increasingly extending to agentic tasks, where sparse terminal rewards make step-level cred…
Cross-Rollout Bellman Closure for Long-Horizon Agentic Reinforcement Learning
Yangyang Ren, Haodong Zhu, Linlin Yang +3
Group-based reinforcement learning such as GRPO trains LLM agents by comparing rollouts sampled for each task, without a learned critic. In long-horizon settings, these rollouts re…
MaPP: A Unified Marginalized Posterior-Predictive Framework for Data-Efficient RLVR
Yangyang Ren, Haodong Zhu, Sheng Xu +4
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but incurs substantial costs from rollouts and policy updates. On…
Kalman Meets Curriculum: Efficient Dynamic Prompt Selection for Adaptive RL Finetuning
Haodong Zhu, Yangyang Ren, Yanjing Li +4
Reinforcement learning (RL) finetuning significantly enhances the reasoning capabilities of large language models (LLMs), yet its effectiveness critically depends on selecting prom…
FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models
Haoyu Huang, Linlin Yang, Sheng Xu +5
Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a "stability lag" where early decisions remain fragile even after being w…
InfraNet: Quality-Aware RGB Guidance for Efficient Infrared Object Detection
Zichao Feng, Haodong Zhu, Jingying Yang +8
Robust object detection under adverse visual conditions remains a long-standing challenge for multi-modal perception systems. Existing fusion-based methods typically require both R…
PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion
Yichen Yang, Hong Li, Haodong Zhu +4
Existing autoregressive (AR) methods for generating artist-designed meshes struggle to balance global structural consistency with high-fidelity local details, and are susceptible t…
Semantic-E2VID: a Semantic-Enriched Paradigm for Event-to-Video Reconstruction
Jingqian Wu, Yunbo Jia, Shengpeng Xu +1
Event cameras provide a promising sensing modality for high-speed and high-dynamic-range vision by asynchronously capturing brightness changes. A fundamental task in event-based vi…
Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method
Qingcheng Zhu, Yangyang Ren, Linlin Yang +6
Deploying large language models (LLMs) is challenging due to their massive parameters and high computational costs. Ultra low-bit quantization can significantly reduce storage and…
MaskGaussian: Adaptive 3D Gaussian Representation from Probabilistic Masks
Yifei Liu, Zhihang Zhong, Yifan Zhan +2
While 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and real-time rendering, the high memory consumption due to the use of millions o…
CCExpert: Advancing MLLM Capability in Remote Sensing Change Captioning with Difference-Aware Integration and a Foundational Dataset
Zhiming Wang, Mingze Wang, Sheng Xu +2
Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of surface changes between multi-temporal remote sensing images, detailing the categor…
RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model
Mingze Wang, Lili Su, Cilin Yan +4
The intelligent interpretation of buildings plays a significant role in urban planning and management, macroeconomic analysis, population dynamics, etc. Remote sensing image buildi…
Representation Disparity-aware Distillation for 3D Object Detection
Yanjing Li, Sheng Xu, Mingbao Lin +3
In this paper, we focus on developing knowledge distillation (KD) for compact 3D detectors. We observe that off-the-shelf KD methods manifest their efficacy only when the teacher m…
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs
Yanjing Li, Sheng Xu, Xianbin Cao +4
Neural architecture search (NAS) proves to be among the effective approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by…
Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Yanjing Li, Sheng Xu, Mingbao Lin +4
Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices. Fully-binarized ViTs (Bi-ViT) th…
Q-DETR: An Efficient Low-Bit Quantized Detection Transformer
Sheng Xu, Yanjing Li, Mingbao Lin +4
The recent detection transformer (DETR) has advanced object detection, but its application on resource-constrained devices requires massive computation and memory resources. Quanti…
Implicit Diffusion Models for Continuous Super-Resolution
Sicheng Gao, Xuhui Liu, Bohan Zeng +6
Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and mos…
Resilient Binary Neural Network
Sheng Xu, Yanjing Li, Teli Ma +5
Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of reducing storage burden as well as quickening inference time. However, there is…
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer
Yanjing Li, Sheng Xu, Baochang Zhang +3
The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems…
IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors
Sheng Xu, Yanjing Li, Bohan Zeng +5
Knowledge distillation (KD) has been proven to be useful for training compact object detection models. However, we observe that KD is often effective when the teacher model and stu…
Recurrent Bilinear Optimization for Binary Neural Networks
Sheng Xu, Yanjing Li, Tiancheng Wang +6
Binary Neural Networks (BNNs) show great promise for real-world embedded devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation plays an esse…
TerViT: An Efficient Ternary Vision Transformer
Sheng Xu, Yanjing Li, Teli Ma +4
Vision transformers (ViTs) have demonstrated great potential in various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-con…
POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing
Sheng Xu, Yanjing Li, Junhe Zhao +2
Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices. To address…