21 citations · 28 across the 30 of their papers we have counts for
31 papers
Cut-ViT: Task-Specific Model Pruning via Gram Anchoring Subspace Consistency
Jianjian Yin, Liulei Li, Tao Chen +3
Pruning visual foundation models has attracted considerable attention. However, existing methods focus on rigid point-to-point token alignment on a single dataset for pruning, suff…
From Reasoning to Pixels: Grounded Medical Multimodal LLMs for VQA and Segmentation
Haowen Gu, Gensheng Pei, Junzhu Mao +3
Although Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in Medical Visual Question Answering (Med-VQA), their reliance on global image features o…
MedFG-VQA: Low-Frequency Memory and Graph Attention for Lightweight Medical VQA
Haowen Gu, Gensheng Pei, Zeren Sun +4
Medical Visual Question Answering (Med-VQA) holds significant promise for clinical decision support, yet faces challenges due to limited annotated data and the high computational d…
Rethinking Point Clouds as Sequences: A Causal Next-Token Predictive Learning Framework
Yumeng Yao, Jingzhi Dong, Haowen Gu +4
With the rapid progress of multimodal foundation models and predictive pre-training, an important open question is how to equip 3D point clouds with a pre-training paradigm that is…
PEARL: Geometry Aligns Semantics for Training-Free Open-Vocabulary Semantic Segmentation
Gensheng Pei, Xiruo Jiang, Xinhao Cai +3
Training-free open-vocabulary semantic segmentation (OVSS) promises rapid adaptation to new label sets without retraining. Yet, many methods rely on heavy post-processing or handle…
Efficiency Follows Global-Local Decoupling
Zhenyu Yang, Gensheng Pei, Tao Chen +4
Modern vision models must capture image-level context without sacrificing local detail while remaining computationally affordable. We revisit this tradeoff and advance a simple pri…
Iris: Bringing Real-World Priors into Diffusion Model for Monocular Depth Estimation
Xinhao Cai, Gensheng Pei, Zeren Sun +3
In this paper, we propose \textbf{Iris}, a deterministic framework for Monocular Depth Estimation (MDE) that integrates real-world priors into the diffusion model. Conventional fee…
PCA-Seg: Revisiting Cost Aggregation for Open-Vocabulary Semantic and Part Segmentation
Jianjian Yin, Tao Chen, Yi Chen +4
Recent advances in vision-language models (VLMs) have garnered substantial attention in open-vocabulary semantic and part segmentation (OSPS). However, existing methods extract ima…
PKINet-v2: Towards Powerful and Efficient Poly-Kernel Remote Sensing Object Detection
Xinhao Cai, Liulei Li, Gensheng Pei +3
Object detection in remote sensing images (RSIs) is challenged by the coexistence of geometric and spatial complexity: targets may appear with diverse aspect ratios, while spanning…
Towards Remote Sensing Change Detection with Neural Memory
Zhenyu Yang, Gensheng Pei, Yazhou Yao +3
Remote sensing change detection is essential for environmental monitoring, urban planning, and related applications. However, current methods often struggle to capture long-range d…
Taming SAM3 in the Wild: A Concept Bank for Open-Vocabulary Segmentation
Gensheng Pei, Xiruo Jiang, Yazhou Yao +3
The recent introduction of \texttt{SAM3} has revolutionized Open-Vocabulary Segmentation (OVS) through \textit{promptable concept segmentation}, which grounds pixel predictions in…
Combating Noisy Labels through Fostering Self- and Neighbor-Consistency
Zeren Sun, Yazhou Yao, Tongliang Liu +3
Label noise is pervasive in various real-world scenarios, posing challenges in supervised deep learning. Deep networks are vulnerable to such label-corrupted samples due to the mem…
AbductiveMLLM: Boosting Visual Abductive Reasoning Within MLLMs
Boyu Chang, Qi Wang, Xi Guo +3
Visual abductive reasoning (VAR) is a challenging task that requires AI systems to infer the most likely explanation for incomplete visual observations. While recent MLLMs develop…
PGP-DiffSR: Phase-Guided Progressive Pruning for Efficient Diffusion-based Image Super-Resolution
Zhongbao Yang, Jiangxin Dong, Yazhou Yao +2
Although diffusion-based models have achieved impressive results in image super-resolution, they often rely on large-scale backbones such as Stable Diffusion XL (SDXL) and Diffusio…
Unbiased Object Detection Beyond Frequency with Visually Prompted Image Synthesis
Xinhao Cai, Liulei Li, Gensheng Pei +4
This paper presents a generation-based debiasing framework for object detection. Prior debiasing methods are often limited by the representation diversity of samples, while naive g…
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning
Jianjian Yin, Tao Chen, Gensheng Pei +3
Consistency regularization has prevailed in semi-supervised semantic segmentation and achieved promising performance. However, existing methods typically concentrate on enhancing t…
Relating CNN-Transformer Fusion Network for Change Detection
Yuhao Gao, Gensheng Pei, Mengmeng Sheng +3
While deep learning, particularly convolutional neural networks (CNNs), has revolutionized remote sensing (RS) change detection (CD), existing approaches often miss crucial feature…
Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric
Xiruo Jiang, Yazhou Yao, Xili Dai +3
Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. Prior literature pre…
Foster Adaptivity and Balance in Learning with Noisy Labels
Mengmeng Sheng, Zeren Sun, Tao Chen +3
Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised models due to its effect in hurting the generalization performance of deep neural netw…
Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation
Tao Chen, XiRuo Jiang, Gensheng Pei +3
Though adversarial erasing has prevailed in weakly supervised semantic segmentation to help activate integral object regions, existing approaches still suffer from the dilemma of u…
Universal Organizer of SAM for Unsupervised Semantic Segmentation
Tingting Li, Gensheng Pei, Xinhao Cai +3
Unsupervised semantic segmentation (USS) aims to achieve high-quality segmentation without manual pixel-level annotations. Existing USS models provide coarse category classificatio…
A Light-weight Transformer-based Self-supervised Matching Network for Heterogeneous Images
Wang Zhang, Tingting Li, Yuntian Zhang +3
Matching visible and near-infrared (NIR) images remains a significant challenge in remote sensing image fusion. The nonlinear radiometric differences between heterogeneous remote s…
Dual Dynamic Threshold Adjustment Strategy for Deep Metric Learning
Xiruo Jiang, Yazhou Yao, Sheng Liu +3
Loss functions and sample mining strategies are essential components in deep metric learning algorithms. However, the existing loss function or mining strategy often necessitate th…
Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation
Gensheng Pei, Yazhou Yao, Jianbo Jiao +3
Conventional video object segmentation (VOS) methods usually necessitate a substantial volume of pixel-level annotated video data for fully supervised learning. In this paper, we p…