论文检索

Yejun Shou, Haocheng Wang, Lingfeng Shen, Qian Zheng, Gang Pan, Yanlong Cao

Point cloud registration is a fundamental task in 3D vision, playing a crucial role in various fields. With the rapid advancement of RGB-D sensors, unsupervised point cloud registration methods based on RGB-D sequences have demonstrated excellent performance. However, existing methods struggle in scenes with low overlap and photometric inconsistency. Low overlap results in numerous correspondence outliers, while photometric inconsistency hinders the model's ability to extract discriminative features. To address these challenges, we first propose the Overlapping Constraint for Inliers Detection (OCID) module, which filters and optimizes the initial correspondence set using an overlappping constraint. This module robustly selects reliable correspondences within the overlapping region while maintaining a balance between accuracy and efficiency. Additionally, we introduce a novel scene representation, 3DGS, which integrates both geometric and texture information, making it particularly well-suited for RGB-D registration tasks. Building on this, we propose the Gaussian Rendering for Photometric Adaptation (GRPA) module, which refines the geometric transformation and enhances the model's adaptability to scenes with inconsistent photometric information. Extensive experiments on ScanNet and ScanNet1500 demonstrate that our method achieves state-of-the-art performance.

Zongyang Ma, Yuxin Chen, Ziqi Zhang, Zhongang Qi, Chunfeng Yuan, Shaojie Zhu, Chengxiang Zhuo, Bing Li, Ye Liu, Zang Li 等

Mathematical problems in real-world scenarios are often presented in a purely vision-form, where textual problem statement and accompanying math figures, e.g., geometry figures and functional graphs, are integrated into a single image. This vision-form problem-solving task requires precise comprehension and reasoning on both textual and graphical elements in the images, posing significant challenge to current Multimodal Large Language Models (MLLMs), which process text and math figures in isolation. In this work, we propose VisionMath, the first exploration for vision-form mathematical problem-solving model, which employs a three-stage progressive multimodal reasoning alignment strategy to systematically enhance task-specific capabilities. Building upon a LLM proficient in unimodal mathematical reasoning, VisionMath first establishes foundational OCR capabilities through capturing rendered mathematical problem images. Subsequently, the model develops comprehensive understanding of figure structures and properties via learning from figure descriptions and mathematical educational videos. Finally, the model's reasoning capacity is activated using carefully constructed visual-form problem-solving datasets VisionMath-IT with chain-of-thought annotations. For comprehensive evaluation, we construct multilingual benchmarks covering diverse problem types, including geometry, algebra, function problems in both English and Chinese. Our model weights, data and code will be made available at https://github.com/mengqiDyangge/VisionMath.

Shiyong Liu, Xiao Tang, Zhihao Li, Yingfan He, Chongjie Ye, Jianzhuang Liu, Binxiao Huang, Shunbo Zhou, Xiaofei Wu

In large-scale scene reconstruction using 3D Gaussian splatting, it is common to partition the scene into multiple smaller regions and reconstruct them individually. However, existing division methods are occlusion-agnostic, meaning that each region may contain areas with severe occlusions. As a result, the cameras within those regions are less correlated, leading to a low average contribution to the overall reconstruction. In this paper, we propose an occlusion-aware scene division strategy that clusters training cameras based on their positions and co-visibilities to acquire multiple regions. Cameras in such regions exhibit stronger correlations and a higher average contribution, facilitating high-quality scene reconstruction. We further propose a region-based rendering technique to accelerate large scene rendering, which culls Gaussians invisible to the region where the viewpoint is located. Such a technique significantly speeds up the rendering without compromising quality. Extensive experiments on multiple large scenes show that our method achieves superior reconstruction results with faster rendering speeds compared to existing state-of-the-art approaches. Project page: https://occlugaussian.github.io.

Zhangjun Zhou, Yiping Li, Chunlin Zhong, Jianuo Huang, Jialun Pei, Hua Li, He Tang

While the human visual system employs distinct mechanisms to perceive salient and camouflaged objects, existing models struggle to disentangle these tasks. Specifically, salient object detection (SOD) models frequently misclassify camouflaged objects as salient, while camouflaged object detection (COD) models conversely misinterpret salient objects as camouflaged. We hypothesize that this can be attributed to two factors: (i) the specific annotation paradigm of current SOD and COD datasets, and (ii) the lack of explicit aspect relationship modeling in current models. Prevalent SOD/COD datasets enforce a mutual exclusivity constraint, assuming scenes contain either salient or camouflaged objects, which poorly aligns with the real world. Furthermore, current SOD/COD methods are primarily designed for these highly constrained datasets and lack explicit modeling of the relationship between salient and camouflaged objects. In this paper, to promote the development of unconstrained salient and camouflaged object detection, we construct a large-scale dataset, USC12K, which features comprehensive labels and four different scenes that cover all possible logical existence scenarios of both salient and camouflaged objects. To explicitly model the relationship between salient and camouflaged objects, we propose a model called USCNet, which introduces two distinct prompt query mechanisms for modeling inter-sample and intra-sample aspect relationships. Additionally, to assess the model's ability to distinguish between salient and camouflaged objects, we design an evaluation metric called CSCS. The proposed method achieves state-of-the-art performance across all scenes in various metrics. Code and dataset are available at https://github.com/ssecv/USCNet.

Chirui Chang, Jiahui Liu, Zhengzhe Liu, Xiaoyang Lyu, Yi-Hua Huang, Xin Tao, Pengfei Wan, Di Zhang, Xiaojuan Qi

Recent advancements in video diffusion models enable the generation of photorealistic videos with impressive 3D consistency and temporal coherence. However, the extent to which these AI-generated videos simulate the 3D visual world remains underexplored. In this paper, we introduce Learned 3D Evaluation (L3DE), an objective, quantifiable, and interpretable method for assessing AI-generated videos' ability to simulate the real world in terms of 3D visual qualities and consistencies, without requiring manually labeled defects or quality annotations. Instead of relying on 3D reconstruction, which is prone to failure with in-the-wild videos, L3DE employs a 3D convolutional network, trained on monocular 3D cues of motion, depth, and appearance, to distinguish real from synthetic videos. Confidence scores from L3DE quantify the gap between real and synthetic videos in terms of 3D visual coherence, while a gradient-based visualization pinpoints unrealistic regions, improving interpretability. We validate L3DE through extensive experiments, demonstrating strong alignment with 3D reconstruction quality and human judgments. Our evaluations on leading generative models (e.g., Sora, MiniMax, and Kling) reveal persistent simulation gaps and subtle inconsistencies. Beyond generative video assessment, L3DE extends to broader applications: benchmarking video generation models, serving as a deepfake detector, and enhancing video synthesis by inpainting flagged inconsistencies.

Deepayan Das, Davide Talon, Yiming Wang, Massimiliano Mancini, Elisa Ricci

Vision Language Models (VLMs) have lead to major improvements in multimodal reasoning, yet they still struggle to understand user-specific concepts. Existing personalization methods address this limitation butheavily rely on training procedures, that can be either costly or unpleasant to individual users.We depart from existing work, and for the first time explore the training-free setting in the context of personalization. We propose a novel method, Retrieval and Reasoning for Personalization (R2P), leveraging internal knowledge of VLMs. First, we leverage VLMs to extract the concept fingerprint, i.e., key attributes uniquely defining the concept within its semantic class. When a query arrives, the most similar fingerprints are retrieved and scored via chain of thought reasoning. To reduce the risk of hallucinations, the scores are validated through cross-modal verification at the attribute level:in case of a discrepancy between the scores, R2P refines the concept association viapairwise multimodal matching, where the retrieved fingerprints and their images aredirectly compared with the query.We validate R2P on two publicly available benchmarks and a newly introduced dataset, Personal Concepts with Visual Ambiguity (PerVA), for concept identification highlighting challenges in visual ambiguity. R2P consistently outperforms state-of-the-art approaches on various downstream tasks across all benchmarks.

Ge Zheng, Jiaye Qian, Jiajin Tang, Sibei Yang

Large Vision-Language Models (LVLMs) have made significant progress in recent years but are also prone to hallucination issues. They exhibit more hallucinations in longer, free-form responses, often attributed to accumulated uncertainties. In this paper, we ask: Does increased hallucination result solely from length-induced errors, or is there a deeper underlying mechanism? After a series of preliminary experiments and findings, we suggest that the risk of hallucinations is not caused by length itself but by the increased reliance on context for coherence and completeness in longer responses. Building on these insights, we propose a novel "induce-detect-suppress" framework that actively induces hallucinations through deliberately designed contexts, leverages induced instances for early detection of high-risk cases, and ultimately suppresses potential object-level hallucinations during actual decoding. Our approach achieves consistent, significant improvements across all benchmarks, demonstrating its efficacy. The strong detection and improved hallucination mitigation not only validate our framework but, more importantly, re-validate our hypothesis on context. Rather than solely pursuing performance gains, this study aims to provide new insights and serves as a first step toward a deeper exploration of hallucinations in LVLMs' longer responses.

Liping Yi, Han Yu, Gang Wang, Xiaoguang Liu, Xiaoxiao Li

Model-heterogeneous federated learning (MHFL) is a challenging FL paradigm designed to allow FL clients to train structurally heterogeneous models under the coordination of an FL server. Existing MHFL methods face significant limitations when it comes to transferring global knowledge to clients as a result of sharing only partial homogeneous model parameters or calculating distance loss, leading to inferior model generalization. To bridge this gap, we propose a novel model-heterogeneous Federated learning method with Representation Angle Learning (FedRAL). It consists of three innovative designs: (1) We first introduce representation angle learning into MHFL. Specifically, we embed a homogeneous square matrix into the local heterogeneous model of each client, which learns the angle information of local representations. These homogeneous representation angle square matrices are aggregated on the server to fuse representation angle knowledge shared by clients for enhancing the generalization of local representations. (2) As different clients might have heterogeneous system resources, we propose an adaptive diagonal sparsification strategy to reduce the numbers of the parameters of representation angle square matrices uploaded to the server, to improve FL communication efficiency. (3) To enable the effective fusion of sparsified homogeneous local representation angle square matrices, we design an element-wise weighted aggregation approach. Experiments on 4 benchmark datasets under 2 types of non-IID divisions over 6 state-of-the-art baselines demonstrate that FedRAL achieves the best performance. It improves test accuracy, communication efficiency and computational efficiency by up to 5.03%, 12.43x and 6.49x, respectively.

Jefferson Hernandez, Jing Shi, Simon Jenni, Vicente Ordonez, Kushal Kafle

Traditional alignment methods for Large Vision and Language Models (LVLMs) primarily rely on human-curated preference data. Human-generated preference data is costly; machine-generated preference data is limited in quality; and self-supervised preference data often introduces hallucinations. To overcome these limitations, we propose a novel Panel-of-Peers learning framework inspired by collaborative learning among humans. This approach leverages a panel of LVLMs, each evaluating and learning from their collective outputs through an iterative self-improvement process. By simulating a peer review system, our models generate, assess, and refine outputs in response to a curated set of prompts, mimicking a classroom learning environment. We demonstrate that this methodology enhances model performance without requiring extensive human-labeled datasets. Our experiments show significant improvement across multiple benchmarks, demonstrating the potential of peer evaluations as a scalable alternative to self-supervised alignment. Notably, we show that Panel-of-Peers increases the average score on fifteen benchmarks from 48% to 57%.

Shaojin Wu, Mengqi Huang, Wenxu Wu, Yufeng Cheng, Fei Ding, Qian He

Although subject-driven generation has been extensively explored in image generation due to its wide applications, it still has challenges in data scalability and subject expansibility. For the first challenge, moving from curating single-subject datasets to multiple-subject ones and scaling them is particularly difficult. For the second, most recent methods center on single-subject generation, making it hard to apply when dealing with multi-subject scenarios. In this study, we propose a highly-consistent data synthesis pipeline to tackle these challenges. This pipeline harnesses the intrinsic in-context generation capabilities of diffusion transformers and generates high-consistent multi-subject paired data. Additionally, we introduce UNO, a multi-subject driven customization architecture based on a diffusion transformer. UNO incorporates a progressive cross-modal alignment training paradigm that progresses from simpler single-subject conditioning to more complex multi-subject conditioning. Along with this, a universal rotary position embedding (UnoPE) adjusts the position indices. Extensive experiments show that our method can achieve high consistency while ensuring controllability in both single-subject and multi-subject driven generation. Code and model: https://github.com/bytedance/UNO.

Yexin Huang, Yongbin Lin, Lishengsa Yue, Zhihong Yao, Jie Wang

Human-machine interaction technology requires not only the distribution of human visual attention but also the prediction of the gaze point trajectory. We introduce PILOT, a programmatic imitation learning approach that predicts a driver's eye movements based on a set of rule-based conditions. These conditions--derived from driving operations and traffic flow characteristics--define how gaze shifts occur. They are initially identified through incremental synthesis, a heuristic search method, and then refined via L-BFGS, a numerical optimization technique. These human-readable rules enable us to understand drivers' eye movement patterns and make efficient and explainable predictions. We also propose DATAD, a dataset that covers 12 types of autonomous driving takeover scenarios, collected from 60 participants and comprising approximately 600,000 frames of gaze point data. Compared to existing eye-tracking datasets, DATAD includes additional driving metrics and surrounding traffic flow characteristics, providing richer contextual information for modeling gaze behavior. Experimental evaluations of PILOT on DATAD demonstrate superior accuracy and faster prediction speeds compared to four baseline models. Specifically, PILOT reduces the MSE of predicted trajectories by 38.59% to 88.02% and improves the accuracy of gaze object predictions by 6.90% to 55.06%. Moreover, PILOT achieves these gains with approximately 30% lower prediction time, offering both more accurate and more efficient eye movement prediction.

Ihab Asaad, Maha Shadaydeh, Joachim Denzler

Machine learning classification models trained with empirical risk minimization (ERM) often inadvertently rely on spurious correlations. When absent in the test data, these unintended associations between non-target attributes and target labels lead to poor generalization. This paper addresses this problem from a model optimization perspective and proposes a novel method, Gradient Extrapolation for Debiased Representation Learning (GERNE), designed to learn debiased representations in both known and unknown attribute training cases. GERNE uses two distinct batches with different amounts of spurious correlations and defines the target gradient as a linear extrapolation of the gradients computed from each batch's loss. Our analysis shows that when the extrapolated gradient points toward the batch gradient with fewer spurious correlations, it effectively guides training toward learning a debiased model. GERNE serves as a general framework for debiasing, encompassing ERM and Resampling methods as special cases. We derive the theoretical upper and lower bounds of the extrapolation factor employed by GERNE. By tuning this factor, GERNE can adapt to maximize either Group-Balanced Accuracy (GBA) or Worst-Group Accuracy (WGA). We validate GERNE on five vision and one NLP benchmarks, demonstrating competitive and often superior performance compared to state-of-the-art baselines. The project page is available at: https://gerne-debias.github.io/.

Xinyu Mao, Xiaohan Xing, Fei Meng, Jianbang Liu, Fan Bai, Qiang Nie, Max Meng

Polyp segmentation is vital for early colorectal cancer detection, yet traditional fully supervised methods struggle with morphological variability and domain shifts, requiring frequent retraining. Additionally, reliance on large-scale annotations is a major bottleneck due to the time-consuming and error-prone nature of polyp boundary labeling. Recently, vision foundation models like Segment Anything Model (SAM) have demonstrated strong generalizability and fine-grained boundary detection with sparse prompts, effectively addressing key polyp segmentation challenges. However, SAM's prompt-dependent nature limits automation in medical applications, since manually inputting prompts for each image is labor-intensive and time-consuming. We propose OP-SAM, a One-shot Polyp segmentation framework based on SAM that automatically generates prompts from a single annotated image, ensuring accurate and generalizable segmentation without additional annotation burdens. Our method introduces Correlation-based Prior Generation (CPG) for semantic label transfer and Scale-cascaded Prior Fusion (SPF) to adapt to polyp size variations as well as filter out noisy transfers. Instead of dumping all prompts at once, we devise Euclidean Prompt Evolution (EPE) for iterative prompt refinement, progressively enhancing segmentation quality. Extensive evaluations across five datasets validate OP-SAM's effectiveness. Notably, on Kvasir, it achieves 76.93% IoU, surpassing the state-of-the-art by 11.44%.

Wenlong Luo, Shizhou Zhang, De Cheng, Yinghui Xing, Guoqiang Liang, Peng Wang, Yanning Zhang

Incremental object detection (IOD) is crucial for enabling AI systems to continuously learn new object classes over time while retaining knowledge of previously learned categories, allowing model to adapt to dynamic environments without forgetting prior information.Existing IOD methods primarily employ knowledge distillation to mitigate catastrophic forgetting, yet these approaches overlook class overlap issues, often resulting in suboptimal performance. In this paper, we propose a novel framework for IOD that leverages a decoupled gradient alignment technique on top of the specially proposed pseudo-labeling strategy. Our method employs a Gaussian Mixture Model to accurately estimate pseudo-labels of previously learned objects in current training images, effectively functioning as a knowledge-replay mechanism. This strategy reinforces prior knowledge retention and prevents the misclassification of unannotated foreground objects from earlier classes as background. Furthermore, we introduce an adaptive gradient decomposition and alignment method to maintain model stability while facilitating positive knowledge transfer. By aligning gradients from both old and new classes, our approach preserves previously learned knowledge while enhancing plasticity for new tasks. Extensive experiments on two IOD benchmarks demonstrate the effectiveness of the proposed method, achieving superior performances to state-of-the-art methods.

Anurag Ghosh, Shen Zheng, Robert Tamburo, Khiem Vuong, Juan Alvarez-Padilla, Hailiang Zhu, Michael Cardei, Nicholas Dunn, Christoph Mertz, Srinivasa G. Narasimhan

Perceiving and autonomously navigating through work zones is a challenging and under-explored problem. Open datasets for this long-tailed scenario are scarce. We propose the ROADWork dataset to learn to recognize, observe, analyze, and drive through work zones. State-of-the-art foundation models fail when applied to work zones. Fine-tuning models on our dataset significantly improves perception and navigation in work zones. With ROADWork, we discover new work zone images with higher precision (+32.5%) at a much higher rate (12.8x) around the world. Open-vocabulary methods fail too, whereas fine-tuned detectors improve performance (+32.2 AP).Vision-Language Models (VLMs) struggle to describe work zones, but fine-tuning substantially improves performance (+36.7 SPICE). Beyond fine-tuning, we show the value of simple techniques. Video label propagation provides additional gains (+2.6 AP) for instance segmentation. While reading work zone signs, composing a detector and text spotter via crop-scaling improves performance (+14.2% 1-NED). Composing work zone detections to provide context further reduces hallucinations (+3.9 SPICE) in VLMs. We predict navigational goals and compute drivable paths from work zone videos. Incorporating road work semantics ensures 53.6% goals have angular error (AE) < 0.5 (+9.9%) and 75.3% pathways have AE < 0.5 (+8.1%).

Yi Wang, Zhitong Xiong, Chenying Liu, Adam J. Stewart, Thomas Dujardin, Nikolaos Ioannis Bountos, Angelos Zavras, Franziska Gerken, Ioannis Papoutsis, Laura Leal-Taixé 等

Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downstream applications crucial to our planet. However, most existing efforts remain limited to fixed spectral sensors, focus solely on the Earth's surface, and overlook valuable metadata beyond imagery. In this work, we take a step towards next-generation EO foundation models with three key components: 1) Copernicus-Pretrain, a massive-scale pretraining dataset that integrates 18.7M aligned images from all major Copernicus Sentinel missions, spanning from the Earth's surface to its atmosphere; 2) Copernicus-FM, a unified foundation model capable of processing any spectral or non-spectral sensor modality using extended dynamic hypernetworks and flexible metadata encoding; and 3) Copernicus-Bench, a systematic evaluation benchmark with 15 hierarchical downstream tasks ranging from preprocessing to specialized applications for each Sentinel mission. Our dataset, model, and benchmark greatly improve the scalability, versatility, and multimodal adaptability of EO foundation models, while also creating new opportunities to connect EO, weather, and climate research. Codes at https://github.com/zhu-xlab/Copernicus-FM.

Yuxue Yang, Lue Fan, Zuzeng Lin, Feng Wang, Zhaoxiang Zhang

Traditional animation production decomposes visual elements into discrete layers to enable independent processing for sketching, refining, coloring, and in-betweening. Existing anime generation video methods typically treat animation as a distinct data domain different from real-world videos, lacking fine-grained control at the layer level. To bridge this gap, we introduce LayerAnimate, a novel video diffusion framework with layer-aware architecture that empowers the manipulation of layers through layer-level controls. The development of a layer-aware framework faces a significant data scarcity challenge due to the commercial sensitivity of professional animation assets. To address the limitation, we propose a data curation pipeline featuring Automated Element Segmentation and Motion-based Hierarchical Merging. Through quantitative and qualitative comparisons and user study, we demonstrate that LayerAnimate outperforms current methods in terms of animation quality, control precision, and usability, making it an effective tool for both professional animators and amateur enthusiasts. This framework opens up new possibilities for layer-level animation applications and creative flexibility. Our code is available at https://layeranimate.github.io.

Yingjie Zhou, Jiezhang Cao, Zicheng Zhang, Farong Wen, Yanwei Jiang, Jun Jia, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai

Speech-driven methods for portraits are figuratively known as "Talkers" because of their capability to synthesize speaking mouth shapes and facial movements. Especially with the rapid development of the Text-to-Image (T2I) models, AI-Generated Talking Heads (AGTHs) have gradually become an emerging digital human media. However, challenges persist regarding the quality of these talkers and AGTHs they generate, and comprehensive studies addressing these issues remain limited. To address this gap, this paper presents the largest AGTH quality assessment dataset THQA-10K to date, which selects 12 prominent T2I models and 14 advanced talkers to generate AGTHs for 14 prompts. After excluding instances where AGTH generation is unsuccessful, the THQA-10K dataset contains 10,457 AGTHs, which provides rich material for AGTH quality assessment. Then, volunteers are recruited to subjectively rate the AGTHs and give the corresponding distortion categories. In our analysis for subjective experimental results, we evaluate the performance of talkers in terms of generalizability and quality, and also expose the distortions of existing AGTHs. Finally, an objective quality assessment method based on the first frame, Y-T slice and tone-lip consistency is proposed. Experimental results show that this method can achieve state-of-the-art (SOTA) performance in AGTH quality assessment. The work is released at https://github.com/zyj-2000/Talker.

Shengyuan Ding, Shenxi Wu, Xiangyu Zhao, Yuhang Zang, Haodong Duan, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Dahua Lin, Jiaqi Wang

The Instruction Following (IF) ability measures how well Multi-modal Large Language Models (MLLMs) understand exactly what users are telling them and doing it right.Existing multimodal instruction following training data is scarce, the benchmarks are simple with atomic instructions, and the evaluation strategies are imprecise for tasks demanding exact output constraints.To address this, we present MM-IFEngine, an effective pipeline to generate high-quality image-instruction pairs.Our MM-IFEngine pipeline yields large-scale, diverse, and high-quality training data MM-IFInstruct-23k, which is suitable for Supervised Fine-Tuning (SFT) and extended as MM-IFDPO-23k for Direct Preference Optimization (DPO).We further introduce MM-IFEval, a challenging and diverse multi-modal instruction-following benchmark that includes (1) both textual constraints for output responses and visual constraints tied to the input images, and (2) a comprehensive evaluation pipeline incorporating rule-based assessment and LLM-as-a-Judge evaluation.We conduct SFT and DPO experiments and demonstrate that fine-tuning MLLMs on MM-IFInstruct-23k and MM-IFDPO-23k achieve notable gains on various IF benchmarks, such as MM-IFEval (+11.8%), MIA (+7.7%), and IFEval (+10.5%).

Yiting Yang, Hao Luo, Yuan Sun, Qingsen Yan, Haokui Zhang, Wei Dong, Guoqing Wang, Peng Wang, Yang Yang, Hengtao Shen

A prevalent approach in Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViT) involves freezing the majority of the backbone parameters and solely learning low-rank adaptation weight matrices to accommodate downstream tasks. These low-rank matrices are commonly derived through the multiplication structure of down-projection and up-projection matrices, exemplified by methods such as LoRA and Adapter. In this study, we observe an approximate orthogonality among any two row or column vectors within any weight matrix of the backbone parameters; however, this property is absent in the vectors of the down/up-projection matrices. Approximate orthogonality implies a reduction in the upper bound of the model's generalization error, signifying that the model possesses enhanced generalization capability. If the fine-tuned down/up-projection matrices were to exhibit this same property as the pre-trained backbone matrices, could the generalization capability of fine-tuned ViTs be further augmented? To address this question, we propose an Approximately Orthogonal Fine-Tuning (AOFT) strategy for representing the low-rank weight matrices. This strategy employs a single learnable vector to generate a set of approximately orthogonal vectors, which form the down/up-projection matrices, thereby aligning the properties of these matrices with those of the backbone. Extensive experimental results demonstrate that our method achieves competitive performance across a range of downstream image classification tasks, confirming the efficacy of the enhanced generalization capability embedded in the down/up-projection matrices. Our code is available at link.