GenAI4ELab/papercli-papers huggingface dataset:
ViM-VQ: Efficient Post-Training Vector Quantization for Visual Mamba
Authors: Juncan Deng; Shuaiting Li; Zeyu Wang; Kedong Xu; Hong Gu; Kejie Huang
Venue: ICCV 2025 (main)

Abstract: Visual Mamba networks (ViMs) extend the selective state space model (Mamba) to various vision tasks and demonstrate significant potential. As a promising compression technique, vector quantization (VQ) decomposes network weights into codebooks and assignments, significantly reducing memory usage and computational latency, thereby enabling the deployment of ViMs on edge devices. Although existing VQ methods have achieved extremely low-bit quantization (e.g., 3-bit, 2-bit, and 1-bit) in convolutional neural networks and Transformer-based networks, directly applying these methods to ViMs results in unsatisfactory accuracy. We identify several key challenges: 1) The weights of Mamba-based blocks in ViMs contain numerous outliers, significantly amplifying quantization errors. 2) When applied to ViMs, the latest VQ methods suffer from excessive memory consumption, lengthy calibration procedures, and suboptimal performance in the search for optimal codewords. In this paper, we propose ViM-VQ, an efficient post-training vector quantization method tailored for ViMs. ViM-VQ consists of two innovative components: 1) a fast convex combination optimization algorithm that efficiently updates both the convex combinations and the convex hulls to search for optimal codewords, and 2) an incremental vector quantization strategy that incrementally confirms optimal codewords to mitigate truncation errors. Experimental results demonstrate that ViM-VQ achieves state-of-the-art performance in low-bit quantization across various visual tasks.
TorchAdapt: Towards Light-Agnostic Real-Time Visual Perception
Authors: Khurram Azeem Hashmi; Karthik Palyakere Suresh; Didier  Stricker; Muhammad Zeshan  Afzal
Venue: ICCV 2025 (main)

Abstract: Low-light conditions significantly degrade the performance of high-level vision tasks. Existing approaches either enhance low-light images without considering normal illumination scenarios, leading to poor generalization, or are tailored to specific tasks. We propose TorchAdapt, a realtime adaptive feature enhancement framework that generalizes robustly across varying illumination conditions without degrading performance in well-lit scenarios. TorchAdapt consists of two complementary modules: the Torch module enhances semantic features beneficial for downstream tasks, while the Adapt module dynamically modulates these enhancements based on input content. Leveraging a novel light-agnostic learning strategy, TorchAdapt aligns feature representations of enhanced and well-lit images to produce powerful illumination-invariant features. Extensive experiments on multiple high-level vision tasks, including object detection, face detection, instance segmentation, semantic segmentation, and video object detection, demonstrate that TorchAdapt consistently outperforms state-of-the-art lowlight enhancement and task-specific methods in both lowlight and light-agnostic settings. TorchAdapt thus provides a unified, flexible solution for robust visual perception across diverse lighting conditions.
UniversalBooth: Model-Agnostic Personalized Text-to-Image Generation
Authors: Songhua Liu; Ruonan Yu; Xinchao Wang
Venue: ICCV 2025 (main)

Abstract: Given a source image, personalized text-to-image generation produces images preserving the identity and appearance while following the text prompts. Existing methods heavily rely on test-time optimization to achieve this customization. Although some recent works are dedicated to zero-shot personalization, they still require re-training when applied to different text-to-image diffusion models. In this paper, we instead propose a model-agnostic personalized method termed UniversalBooth. At the heart of our approach lies a novel cross-attention mechanism, where different blocks in the same diffusion scale share common square transformation matrices of key and value. In this way, the image encoder is decoupled from the diffusion architecture while maintaining its effectiveness. Moreover, the cross-attention performs hierarchically: the holistic attention first captures the global semantics of user inputs for textual combination with editing prompts, and the fine-grained attention divides the holistic attention scores for various local patches to enhance appearance consistency. To improve the performance when deployed on unseen diffusion models, we further devise an optimal transport prior to the model and encourage the attention scores allocated by cross-attention to fulfill the optimal transport constraint. Experiments demonstrate that our personalized generation model can be generalized to unseen text-to-image diffusion models with a wide spectrum of architectures and functionalities without any additional optimization, while other methods cannot. Meanwhile, it achieves comparable zero-shot personalization performance on seen architectures with existing works.
EVEv2: Improved Baselines for Encoder-Free Vision-Language Models
Authors: Haiwen Diao; Xiaotong Li; Yufeng Cui; Yueze Wang; Haoge Deng; Ting Pan; Wenxuan Wang; Huchuan Lu; Xinlong Wang
Venue: ICCV 2025 (main)

Abstract: Existing encoder-free vision-language models (VLMs) are rapidly narrowing the performance gap with their encoder-based counterparts, highlighting the promising potential for unified multimodal systems with structural simplicity and efficient deployment. We systematically clarify the performance gap between VLMs using pre-trained vision encoders, discrete tokenizers, and minimalist visual layers from scratch, deeply excavating the under-examined characteristics of encoder-free VLMs. We develop efficient strategies for encoder-free VLMs that rival mainstream encoder-based ones. After an in-depth investigation, we launch EVEv2.0, a new and improved family of encoder-free VLMs. We show that: (i) Properly decomposing and hierarchically associating vision and language within a unified model reduces interference between modalities. (ii) A well-designed training strategy enables effective optimization for encoder-free VLMs. Through extensive evaluation, our EVEv2.0 represents a thorough study for developing a decoder-only architecture across modalities, demonstrating superior data efficiency and strong vision-reasoning capability. Code is publicly available at: https://github.com/baaivision/EVE.
End-to-End Driving with Online Trajectory Evaluation via BEV World Model
Authors: Yingyan Li; Yuqi Wang; Yang Liu; Jiawei He; Lue Fan; Zhaoxiang Zhang
Venue: ICCV 2025 (main)

Abstract: End-to-end autonomous driving has achieved remarkable progress by integrating perception, prediction, and planning into a fully differentiable framework. Yet, to fully realize its potential, an effective online trajectory evaluation is indispensable to ensure safety. By forecasting the future outcomes of a given trajectory, trajectory evaluation becomes much more effective. This goal can be achieved by employing a world model to capture environmental dynamics and predict future states. Therefore, we propose an end-to-end driving framework **WoTE**, which leverages a BEV **Wo**rld model to predict future BEV states for **T**rajectory **E**valuation. The proposed BEV world model is latency-efficient compared to image-level world models and can be seamlessly supervised using off-the-shelf BEV-space traffic simulators. We validate our framework on both the NAVSIM benchmark and the closed-loop Bench2Drive benchmark based on the CARLA simulator, achieving state-of-the-art performance. Code is released at https://github.com/liyingyanUCAS/WoTE.
EEGMirror: Leveraging EEG Data in the Wild via Montage-Agnostic Self-Supervision for EEG to Video Decoding
Authors: Xuan-Hao Liu; Bao-Liang Lu; Wei-Long Zheng
Venue: ICCV 2025 (main)

Abstract: Generating high fidelity video from brain activity is an important milestone in brain decoding research. Previous works were mostly based on functional Magnetic Resonance Imaging (fMRI), whose low temporal resolution confines the ability of faithfully reflecting rapid brain activity, motivating us to turn to high temporal resolution brain signals like electroencephalography (EEG). However, EEG-to-video is challenging due to the complexity and nonstationarity of EEG signals and the scarcity of data annotations. Addressing these issues, we present **EEGMirror**. Firstly, we adopt neural quantization for converting nonstationary raw EEG signals into robust discrete representation. Afterwards, a masked self-supervision method with montage-agnostic position embedding (MAPE) is introduced. By MAPE, EEGMirror can process EEG data with various montages (number and position of channels) and thus can flexibly leverage different EEG datasets to acquire an effective EEG encoder, mitigating the lack of well-annotated EEG data. Next, multimodal contrastive learning is applied to align brain modality with dynamic changes and semantic information. Lastly, a fine-tuned inflated Stable Diffusion model is adopted to reconstruct video stimuli guided by visual and semantic information decoded from EEG signals. We show that EEGMirror outperforms the state-of-the-art performance in both semantic (82.1% vs 79.8%) and pixel (0.261 vs 0.256) levels. An exhaustive ablation study is also conducted to analyze our framework. Code: https://github.com/XuanhaoLiu/EEGMirror.
Edit360: 2D Image Edits to 3D Assets from Any Angle
Authors: Junchao Huang; Xinting Hu; Shaoshuai Shi; Zhuotao Tian; Li Jiang
Venue: ICCV 2025 (main)

Abstract: Recent advances in diffusion models have significantly improved image generation and editing, but extending these capabilities to 3D assets remains challenging, especially for fine-grained edits that require multi-view consistency. Existing methods typically restrict editing to predetermined viewing angles, severely limiting their flexibility and practical applications.We introduce Edit360, a tuning-free framework that extends 2D modifications to multi-view consistent 3D editing. Built upon video diffusion models, Edit360 enables user-specific editing from arbitrary viewpoints while ensuring structural coherence across all views. The framework selects anchor views for 2D modifications and propagates edits across the entire 360-degree range. To achieve this, Edit360 introduces a novel Anchor-View Editing Propagation mechanism, which effectively aligns and merges multi-view information within the latent and attention spaces of diffusion models. The resulting edited multi-view sequences facilitate the reconstruction of high-quality 3D assets, enabling customizable 3D content creation.
VehicleMAE: View-asymmetry Mutual Learning for Vehicle Re-identification Pre-training via Masked AutoEncoders
Authors: Qi Wang; Zeyu Zhang; Dong Wang; Di Gai; Xin Xiong; Jiyang Xu; Ruihua Zhou
Venue: ICCV 2025 (main)

Abstract: Large-scale pre-training technology has achieved remarkable performance in diversified object re-identification (Re-ID) downstream tasks. Nevertheless, to our best knowledge, the pre-training model specifically for vehicle Re-ID, which focuses on tackling the challenge of multi-view variations, has not been fully investigated. In this paper, we first leverage a diffusion model to build a large-scale vehicle Re-ID benchmark dataset, dubbed "DiffVERI", containing over 1700K images from abundant multi-view annotations. In terms of this dataset, we further present VehicleMAE, a novel masked image modeling pre-training paradigm that learns view-invariant representations by performing mutual-distillation in a self-supervised manner. To be specific, the pipeline of VehicleMAE unfolds two core modules, i.e., view-asymmetry masked image modeling (VMIM) and past-to-present mutual-distillation (PPMD). Technically, VMIM consists of two homogeneous masked autoencoders (MAE) that simultaneously reconstruct the RGB pixels and multi-view semantic information of the specific vehicle body region via paired asymmetric mask sampling strategies. To progressively distill the knowledge of the model itself, PPMD considers the two MAEs in the current epoch and the previous one as the student models and the teacher models, respectively, which leverages the knowledge learned by the current student and the historical teacher for mutual feature-level distillation. Extensive experimental results have verified that the proposed pre-training paradigm on DiffVERI gains compelling downstream task performance for vehicle Re-ID.
MiDSummer: Multi-Guidance Diffusion for Controllable Zero-Shot Immersive Gaussian Splatting Scene Generation
Authors: Anjun Hu; Richard Tomsett; Valentin Gourmet; Massimo Camplani; Jas Kandola; Hanting Xie
Venue: ICCV 2025 (main)

Abstract: We present MiDSummer, a two-stage framework for generating immersive Gaussian Splatting scenes that leverages multiple diffusion guidance signals to enable structured layout control, enhanced physical realism, and improved visual quality.While 3D scene generation has seen significant recent advances, current approaches could benefit from: (1) achieving precise, reliable layout control while preserving open-world generalization and physical plausibility, (2) balancing high-level semantic reasoning with low-level, directly controllable geometric constraints, and (3) effectively utilizing layout knowledge for visual refinement. Our work addresses these challenges through a structured two-stage planning-assembly framework. For planning, we introduce a dual layout diffusion guidance approach to bridge semantic reasoning and geometric controllability. Our approach uniquely integrates LLMs' open-vocabulary reasoning with Graph Diffusion Models' (GDM) geometric precision by incorporating multi-level self-consistency scores over scene graph structures and layout bounding box parameters. This fusion enables fine-grained control over scene composition while ensuring physical plausibility and faithful prompt interpretation. For assembly, we propose a layout-guided optimization technique for scene refinement. We effectively incorporate layout priors obtained during the planning stage into a Stable Diffusion (SD)-based refinement process that jointly optimizes camera trajectories and scene splats. This layout-aware joint optimization, constrained by multi-view consistency, produces visually compelling immersive scenes that are structurally coherent and controllable.
AV-Link: Temporally-Aligned Diffusion Features for Cross-Modal Audio-Video Generation
Authors: Moayed Haji-Ali; Willi Menapace; Aliaksandr Siarohin; Ivan Skorokhodov; Alper Canberk; Kwot Sin Lee; Vicente Ordonez; Sergey Tulyakov
Venue: ICCV 2025 (main)

Abstract: We propose AV-Link, a unified framework for Video-to-Audio (A2V) and Audio-to-Video (A2V) generation that leverages the activations of frozen video and audio diffusion models for temporally-aligned cross-modal conditioning. The key to our framework is a Fusion Block that facilitates bidirectional information exchange between video and audio diffusion models through temporally-aligned self-attention operations. Unlike prior work that uses dedicated models for A2V and V2A tasks and relies on pretrained feature extractors, AV-Link achieves both tasks in a single self-contained framework, directly leveraging features obtained by the complementary modality (i.e. video features to generate audio, or audio features to generate video). Extensive evaluations demonstrate that AV-Link achieves substantial improvements in audio-video synchronization, outperforming more expensive baselines such as MovieGen V2A model.
RANKCLIP: Ranking-Consistent Language-Image Pretraining
Authors: Yiming Zhang; Zhuokai Zhao; Zhaorun Chen; Zhili Feng; Zenghui Ding; Yining Sun
Venue: ICCV 2025 (main)

Abstract: Self-supervised contrastive learning models, such as CLIP, have set new benchmarks for vision-language models in many downstream tasks. However, their dependency on rigid one-to-one mappings overlooks the complex and often multifaceted relationships between and within texts and images. To this end, we introduce RankCLIP, a novel pretraining method that extends beyond the rigid one-to-one matching framework of CLIP and its variants. By extending the traditional pair-wise loss to list-wise, and leveraging both in-modal and cross-modal ranking consistency, RankCLIP improves the alignment process, enabling it to capture the nuanced many-to-many relationships between and within each modality. Through comprehensive experiments, we demonstrate the effectiveness of RankCLIP in various downstream tasks, notably achieving significant gains in zero-shot classifications over state-of-the-art methods, underscoring the importance of this enhanced learning process.
Is Visual in-Context Learning for Compositional Medical Tasks within Reach?
Authors: Simon Reiß; Zdravko Marinov; Alexander Jaus; Constantin Seibold; M. Saquib Sarfraz; Erik Rodner; Rainer Stiefelhagen
Venue: ICCV 2025 (main)

Abstract: In this paper, we explore the potential of visual in-context learning to enable a single model to handle multiple tasks and adapt to new tasks during test time without re-training. Unlike previous approaches, our focus is on training in-context learners to adapt to sequences of tasks, rather than individual tasks. Our goal is to solve complex tasks that involve multiple intermediate steps using a single model, allowing users to define entire vision pipelines flexibly at test time. To achieve this, we first examine the properties and limitations of visual in-context learning architectures, with a particular focus on the role of codebooks. We then introduce a novel method for training in-context learners using a synthetic compositional task generation engine. This engine bootstraps task sequences from arbitrary segmentation datasets, enabling the training of visual in-context learners for compositional tasks. Additionally, we investigate different masking-based training objectives to gather insights into how to train models better for solving complex, compositional tasks. Our exploration not only provides important insights especially for multi-modal medical task sequences but also highlights challenges that need to be addressed.
Details Matter for Indoor Open-vocabulary 3D Instance Segmentation
Authors: Sanghun Jung; Jingjing Zheng; Ke Zhang; Nan Qiao; Albert Y. C. Chen; Lu Xia; Chi Liu; Yuyin Sun; Xiao Zeng; Hsiang-Wei Huang; Byron Boots; Min Sun; Cheng-Hao Kuo
Venue: ICCV 2025 (main)

Abstract: Unlike closed-vocabulary 3D instance segmentation that is often trained end-to-end, open-vocabulary 3D instance segmentation (OV-3DIS) often leverages vision-language models (VLMs) to generate 3D instance proposals and classify them. While various concepts have been proposed from existing research, we observe that these individual concepts are not mutually exclusive but complementary. In this paper, we propose a new state-of-the-art solution for OV-3DIS by carefully designing a recipe to combine the concepts together and refining them to address key challenges. Our solution follows the two-stage scheme: 3D proposal generation and instance classification. We employ robust 3D tracking-based proposal aggregation to generate 3D proposals and remove overlapped or partial proposals by iterative merging/removal. For the classification stage, we replace the standard CLIP model with Alpha-CLIP, which incorporates object masks as an alpha channel to reduce background noise and obtain object-centric representation. Additionally, we introduce the standardized maximum similarity (SMS) score to normalize text-to-proposal similarity, effectively filtering out false positives and boosting precision. Our framework achieves state-of-the-art performance on ScanNet200 and S3DIS across all AP and AR metrics, even surpassing an end-to-end closed-vocabulary method.
Differentially Private Fine-Tuning of Diffusion Models
Authors: Yu-Lin Tsai; Yizhe Li; Chia-Mu Yu; Xuebin Ren; Po-Yu Chen; Zekai Chen; Francois Buet-Golfouse
Venue: ICCV 2025 (main)

Abstract: Generative AI models, particularly diffusion models (DMs), have demonstrated exceptional capabilities in high-quality image synthesis. However, their large memorization capacity raises significant privacy concerns, especially when trained on sensitive datasets. This paper introduces DP-LoRA, a surprisingly simple yet effective framework for differentially private fine-tuning of latent diffusion models (LDMs) using Low-Rank Adaptation (LoRA). By fine-tuning only a small subset of parameters, DP-LoRA achieves state-of-the-art (SoTA) performance in privacy-preserving image generation while significantly improving the privacy-utility trade-off. DP-LoRA leverages pre-trained LDMs and integrates LoRA modules into attention blocks and projection layers, enabling parameter-efficient fine-tuning under Differential Privacy (DP) constraints. Extensive experiments on benchmarks such as CelebA-HQ demonstrate that DP-LoRA outperforms existing methods, achieving competitive Frechet Inception Distance (FID) scores with strict privacy budgets (e.g., epsilon \leq 10). Additionally, we provide a comprehensive analysis of the impact of LoRA rank, noise multiplicity, and trainable components on model performance. Our results highlight the potential of parameter-efficient techniques to scale privacy-preserving generative models to real-world applications, paving the way for safer deployment of diffusion models in sensitive domains. Anonymous codes available at \href https://github.com/EzzzLi/DP-LORA  https://github.com/EzzzLi/DP-LORA .
Large Learning Rates Simultaneously Achieve Robustness to Spurious Correlations and Compressibility
Authors: Melih Barsbey; Lucas Prieto; Stefanos Zafeiriou; Tolga Birdal
Venue: ICCV 2025 (main)

Abstract: Robustness and resource-efficiency are two highly desirable properties for modern machine learning models. However, achieving them jointly remains a challenge. In this paper, we identify high learning rates as a facilitator for simultaneously achieving robustness to spurious correlations and network compressibility. We demonstrate that large learning rates also produce desirable representation properties such as invariant feature utilization, class separation, and activation sparsity. Our findings indicate that large learning rates compare favorably to other hyperparameters and regularization methods, in consistently satisfying these properties in tandem. In addition to demonstrating the positive effect of large learning rates across diverse spurious correlation datasets, models, and optimizers, we also present strong evidence that the previously documented success of large learning rates in standard classification tasks is related to addressing hidden/rare spurious correlations in the training dataset. Our investigation of the mechanisms underlying this phenomenon reveals the importance of confident mispredictions of bias-conflicting samples under large learning rates.