GenAI4ELab/papercli-papers huggingface dataset:
Diffusion Image Prior
Authors: Hamadi Chihaoui; Paolo Favaro
Venue: ICCV 2025 (main)

Abstract: Zero-shot image restoration (IR) methods based on pretrained diffusion models have recently achieved significant success. These methods typically require at least a parametric form of the degradation model. However, in real-world scenarios, the degradation may be too complex to define explicitly without relying on crude approximations. To handle this general case, we introduce the DIffusion Image Prior (DIIP). We take inspiration from the Deep Image Prior (DIP), since it can be used to remove artifacts without the need for an explicit degradation model. However, in contrast to DIP, we find that pretrained diffusion models offer a much stronger prior, despite being trained without knowledge from corrupted data. We show that, the optimization process in DIIP first reconstructs a clean version of the image before eventually overfitting to the degraded input, but it does so for a broader range of degradations than DIP. In light of this result, we propose a blind image restoration (IR) method based on early stopping, which does not require prior knowledge of the degradation model. We validate DIIP on various degradation-blind IR tasks, including JPEG artifact removal, waterdrop removal, denoising and super-resolution with state-of-the-art results.
Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning
Authors: Xinyao Liu; Diping Song
Venue: ICCV 2025 (main)

Abstract: Multimodal large language models (MLLMs) demonstrate significant potential in the field of medical diagnosis. However, they face critical challenges in specialized domains such as ophthalmology, particularly the fragmentation of annotation granularity and inconsistencies in clinical reasoning logic, which hinder precise cross-modal understanding. This paper introduces FundusExpert, an ophthalmology-specific MLLM with integrated positioning-diagnosis reasoning capabilities, along with FundusGen, a dataset constructed through the intelligent Fundus-Engine system. Fundus-Engine automates localization and leverages MLLM-based semantic expansion to integrate global disease classification, local object detection, and fine-grained feature analysis within a single fundus image. Additionally, by constructing a clinically aligned cognitive chain, it guides the model to generate interpretable reasoning paths. FundusExpert, fine-tuned with instruction data from FundusGen, achieves the best performance in ophthalmic question-answering tasks, surpassing the average accuracy of the 40B MedRegA by 26.6%. It also excels in zero-shot report generation tasks, achieving a clinical consistency of 77.0%, significantly outperforming GPT-4o's 47.6%. Furthermore, we reveal a scaling law between data quality and model capability (L \propto N^ 0.068 ), demonstrating that the cognitive alignment annotations in FundusGen enhance data utilization efficiency. By integrating region-level localization with diagnostic reasoning chains, our work develops a scalable, clinically-aligned MLLM and explores a pathway toward bridging the visual-language gap in specific MLLMs. Our project can be found at https://github.com/MeteorElf/FundusExpert.
MemDistill: Distilling LiDAR Knowledge into Memory for Camera-Only 3D Object Detection
Authors: Donghyeon Kwon; Youngseok Yoon; Hyeongseok Son; Suha Kwak
Venue: ICCV 2025 (main)

Abstract: Camera-based 3D object detection has gained attention for its cost-effectiveness, but it in general lags behind LiDAR-based approaches due to its lack of explicit 3D spatial cues. To take the best of both camera- and LiDAR-based detectors, we propose MemDistill, a novel cross-modal knowledge distillation framework for 3D object detection.MemDistill transfers rich 3D knowledge from a LiDAR-based teacher model to a camera-based student model through a dedicated memory unit and a scene-dependent memory retrieval module.To be specific, our framework distills the teacher's 3D knowledge, optimizes the memory to store that knowledge compactly, and learns the retriever that searches the memory to produce 3D features relevant to the input scene, compensating for the missing LiDAR modality.Experiments on the nuScenes dataset demonstrate that MemDistill significantly improves performance of its camera-only baseline, achieving the state of the art in camera-based 3D object detection.
OpenRSD: Towards Open-prompts for Object Detection in Remote Sensing Images
Authors: Ziyue Huang; Yongchao Feng; Ziqi Liu; Shuai Yang; Qingjie Liu; Yunhong Wang
Venue: ICCV 2025 (main)

Abstract: Remote sensing object detection has made significant progress, but most studies still focus on closed-set detection, limiting generalization across diverse datasets. Open-vocabulary object detection (OVD) provides a solution by leveraging multimodal associations between text prompts and visual features. However, existing OVD methods for remote sensing (RS) images are constrained by small-scale datasets and fail to address the unique challenges of remote sensing interpretation, include oriented object detection and the need for both high precision and real-time performance in diverse scenarios. To tackle these challenges, we propose OpenRSD, a universal open-prompt RS object detection framework. OpenRSD supports multimodal prompts and integrates multi-task detection heads to balance accuracy and real-time requirements. Additionally, we design a multi-stage training pipeline to enhance the generalization of model. Evaluated on seven public datasets, OpenRSD demonstrates superior performance in oriented and horizontal bounding box detection, with real-time inference capabilities suitable for large-scale RS image analysis. Compared to YOLO-World, OpenRSD exhibits an 8.7% higher average precision and achieves an inference speed of 20.8 FPS. Codes and models will be released.
Privacy-centric Deep Motion Retargeting for Anonymization of Skeleton-Based Motion Visualization
Authors: Thomas Carr; Depeng Xu; Shuhan Yuan; Aidong Lu
Venue: ICCV 2025 (main)

Abstract: Capturing and visualizing motion using skeleton-based techniques is a key aspect of computer vision, particularly in virtual reality (VR) settings. Its popularity has surged, driven by the simplicity of obtaining skeleton data and the growing appetite for virtual interaction. Although this skeleton data appears to be non-identifiable, it can be exploited to derive personally identifiable information (PII), posing a risk of inadvertent privacy breaches. In this paper, we explore the application of motion retargeting and its ability to mitigate privacy leakages. Motion retargeting can effectively transfer the motion from an initial user onto a dummy skeleton with the purpose of hiding PII. We propose a Privacy-centric Deep Motion Retargeting model (PMR), which mitigates the PII through adversarial learning. In our evaluation, our proposed model achieves motion retargeting performance on par with the current state-of-the-art models. More importantly, it effectively prevents the attackers from identifying the initial user.
FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution
Authors: Gene Chou; Wenqi Xian; Guandao Yang; Mohamed Abdelfattah; Bharath Hariharan; Noah Snavely; Ning Yu; Paul Debevec
Venue: ICCV 2025 (main)

Abstract: A versatile video depth estimation model should be consistent and accurate across frames, produce high-resolution depth maps, and support real-time streaming. We propose a method, FlashDepth, that satisfies all three requirements, performing depth estimation for a 2044x1148 streaming video at 24 FPS. We show that, with careful modifications to pretrained single-image depth models, these capabilities are enabled with relatively little data and training. We validate our approach across multiple unseen datasets against state-of-the-art depth models, and find that our method outperforms them in terms of boundary sharpness and speed by a significant margin, while maintaining competitive accuracy. We hope our model will enable various applications that require high-resolution depth, such as visual effects editing, and online decision-making, such as robotics. We release all code and model weights at https://github.com/Eyeline-Research/FlashDepth.
Towards Performance Consistency in Multi-Level Model Collaboration
Authors: Qi Li; Runpeng Yu; Xinchao Wang
Venue: ICCV 2025 (main)

Abstract: Parameter-level model merging is an emerging paradigm in multi-task learning with significant promise. Previous research has explored its connections with prediction-level model ensembling--commonly viewed as the upper bound for merging--to reveal the potential of achieving performance consistency between the two. However, this observation relies on certain preconditions, such as being limited to two models, using ViT-based models, and all models are fine-tuned from the same pre-trained checkpoint. To further understand the intrinsic connections between these two paradigms, this paper explores an interesting possibility: If these restrictions are removed, can performance consistency still be achieved between merging and ensembling? To answer this question, we first theoretically establish a performance correlation between merging and ensembling. We find that even when previous restrictions are not met, there is still a way for model merging to attain a near-identical and superior performance similar to that of ensembling. To verify whether our findings are practical, we introduce a validation framework termed \underline Neu ral \underline Lig and (NeuLig). The learning process of NeuLig is meticulously designed with a specialized loss function supported by theoretical foundations. Experimental results demonstrate the robust resilience of NeuLig in terms of both model scale and the number of collaborating models. For instance, for the case involving 5 CLIP-ViT-B/32 models, parameter-level merging achieves the same performance as prediction-level ensembling (merging: 95.44% vs. ensembling: 95.46%).
Polarimetric Neural Field via Unified Complex-Valued Wave Representation
Authors: Chu Zhou; Yixin Yang; Junda Liao; Heng Guo; Boxin Shi; Imari Sato
Venue: ICCV 2025 (main)

Abstract: Polarization has found applications in various computer vision tasks by providing additional physical cues. However, due to the limitations of current imaging systems, polarimetric parameters are typically stored in discrete form, which is non-differentiable and limits their applicability in polarization-based vision. While current neural field methods have shown promise for continuous signal reconstruction, they struggle to model the intrinsic physical interdependencies among polarimetric parameters. In this work, we propose a physics-grounded representation scheme to represent polarimetric parameters as a unified complex-valued wave. Tailored to this scheme, we propose a tuning-free fitting strategy along with a lightweight complex-valued neural network, enabling property-preserved reconstruction. Experimental results show that our method achieves state-of-the-art performance and facilitates smooth polarized image rendering and flexible resolution adjustments.
CLIP-Adapted Region-to-Text Learning for Generative Open-Vocabulary Semantic Segmentation
Authors: Jiannan Ge; Lingxi Xie; Hongtao Xie; Pandeng Li; Sun-Ao Liu; Xiaopeng Zhang; Qi Tian; Yongdong Zhang
Venue: ICCV 2025 (main)

Abstract: In recent years, Open-Vocabulary Semantic Segmentation (OVSS) has been largely advanced. However, existing methods mostly rely on a pre-trained vision-language model (e.g., CLIP) and require a predefined set of classes to guide the semantic segmentation process during the inference. This not only narrows the application scenario but also constrains comprehension within a finite vocabulary. To overcome this, we reformulate OVSS as a text generation task and propose the CLIP-adapted Region-to-Text Network (CRTNet) that achieves vocabulary-free OVSS by generating category names and descriptions upon segmentation masks. The training process consists of two steps to ensure an accurate and detailed interpretation of the masked regions: (i) the initial step adapts CLIP visual features to mask-level proposal features using binarized masks extracted by a trained mask extractor, and (ii) the subsequent step involves selecting and aggregating these features to become text-aware by integrating CLIP text embeddings, effectively aligning visual data with corresponding linguistic data to facilitate region-to-text learning. Furthermore, we introduce a series of parsing and filtering techniques to integrate multiple sources of training data to improve the generalization ability of our model. Experiments demonstrate that our model not only excels in OVSS but also exhibits scalability and can be adapted to various foundation models (e.g., SAM) without being retrained.
Where, What, Why: Towards Explainable Driver Attention Prediction
Authors: Yuchen Zhou; Jiayu Tang; Xiaoyan Xiao; Yueyao Lin; Linkai Liu; Zipeng Guo; Hao Fei; Xiaobo Xia; Chao Gou
Venue: ICCV 2025 (main)

Abstract: Modeling task-driven attention in driving is a fundamental challenge for both autonomous vehicles and cognitive science. Existing methods primarily predict where drivers look by generating spatial heatmaps, but fail to capture the cognitive motivations behind attention allocation in specific contexts, which limits deeper understanding of attention mechanisms. To bridge this gap, we introduce Explainable Driver Attention Prediction, a novel task paradigm that jointly predicts spatial attention regions (where), parses attended semantics (what), and provides cognitive reasoning for attention allocation (why). To support this, we present W3DA, the first large-scale explainable driver attention dataset. It enriches existing benchmarks with detailed semantic and causal annotations across diverse driving scenarios, including normal conditions, safety-critical situations, and traffic accidents. We further propose LLada, a Large Language model-driven framework for driver attention prediction, which unifies pixel modeling, semantic parsing, and cognitive reasoning within an end-to-end architecture. Extensive experiments demonstrate the effectiveness of LLada, exhibiting robust generalization across datasets and driving conditions. This work serves as a key step toward a deeper understanding of driver attention mechanisms, with significant implications for autonomous driving, intelligent driver training, and human-computer interaction.
MeshAnything V2: Artist-Created Mesh Generation with Adjacent Mesh Tokenization
Authors: Yiwen Chen; Yikai Wang; Yihao Luo; Zhengyi Wang; Zilong Chen; Jun Zhu; Chi Zhang; Guosheng Lin
Venue: ICCV 2025 (main)

Abstract: Meshes are the de facto 3D representation in the industry but are labor-intensive to produce. Recently, a line of research has focused on autoregressively generating meshes. This approach processes meshes into a sequence composed of vertices and then generates them vertex by vertex, similar to how a language model generates text. These methods have achieved some success but still struggle to generate complex meshes. One primary reason for this limitation is their inefficient tokenization methods. To address this issue, we introduce MeshAnything V2, an advanced mesh generation model designed to create Artist-Created Meshes that align precisely with specified shapes. A key innovation behind MeshAnything V2 is our novel Adjacent Mesh Tokenization (AMT) method. Unlike traditional approaches that represent each face using three vertices, AMT optimizes this by employing a single vertex wherever feasible, effectively reducing the token sequence length by about half on average. This not only streamlines the tokenization process but also results in more compact and well-structured sequences, enhancing the efficiency of mesh generation. With these improvements, MeshAnything V2 effectively doubles the face limit compared to previous models, delivering superior performance without increasing computational costs. Our extensive experiments across various mesh tokenization methods demonstrate that AMT is pivotal in achieving optimal results in both efficiency and performance.
Visual Intention Grounding for Egocentric Assistants
Authors: Pengzhan Sun; Junbin Xiao; Tze Ho Elden Tse; Yicong Li; Arjun Akula; Angela Yao
Venue: ICCV 2025 (main)

Abstract: Visual grounding associates textual descriptions with objects in an image. Conventional methods target third-person image inputs and named object queries. In applications such as AI assistants, the perspective shifts -- inputs are egocentric, and objects may be referred to implicitly through needs and intentions. To bridge this gap, we introduce EgoIntention, the first dataset for egocentric visual intention grounding. EgoIntention challenges multimodal LLMs to 1) understand and ignore unintended contextual objects and 2) reason about uncommon object functionalities. Benchmark results show that current models misidentify context objects and lack affordance understanding in egocentric views. We also propose Reason-to-Ground (RoG) instruction tuning; it enables hybrid training with normal descriptions and egocentric intentions with a chained intention reasoning and object grounding mechanism. RoG significantly outperforms naive finetuning and hybrid training on EgoIntention, while maintaining or slightly improving naive description grounding. This advancement enables unified visual grounding for egocentric and exocentric visual inputs while handling explicit object queries and implicit human intentions.
Analyzing Finetuning Representation Shift for Multimodal LLMs Steering
Authors: Pegah Khayatan; Mustafa Shukor; Jayneel Parekh; Arnaud Dapogny; Matthieu Cord
Venue: ICCV 2025 (main)

Abstract: Multimodal LLMs (MLLMs) have reached remarkable levels of proficiency in understanding multimodal inputs. However, understanding and interpreting the behavior of such complex models is a challenging task, not to mention the dynamic shifts that may occur during fine-tuning, or due to covariate shift between datasets. In this work, we apply concept-level analysis towards MLLM understanding. More specifically, we propose to map hidden states to interpretable visual and textual concepts. This enables us to more efficiently compare certain semantic dynamics, such as the shift from an original and fine-tuned model, revealing concept alteration and potential biases that may occur during fine-tuning. We also demonstrate the use of shift vectors to capture these concepts changes. These shift vectors allow us to recover fine-tuned concepts by applying simple, computationally inexpensive additive concept shifts in the original model. Finally, our findings also have direct applications for MLLM steering, which can be used for model debiasing as well as enforcing safety in MLLM output. All in all, we propose a novel, training-free, ready-to-use framework for MLLM behavior interpretability and control. Our implementation is publicly available.
TeethGenerator: A two-stage framework for paired pre- and post-orthodontic 3D dental data generation
Authors: Changsong Lei; Yaqian Liang; Shaofeng Wang; Jiajia Dai; Yong-Jin Liu
Venue: ICCV 2025 (main)

Abstract: Digital orthodontics represents a prominent and critical application of computer vision technology in the medical field. So far, the labor-intensive process of collecting clinical data, particularly in acquiring paired 3D orthodontic teeth models, constitutes a crucial bottleneck for developing tooth arrangement neural networks. Although numerous general 3D shape generation methods have been proposed, most of them focus on single-object generation and are insufficient for generating anatomically structured teeth models, each comprising 24-32 segmented teeth. In this paper, we propose TeethGenerator, a novel two-stage framework designed to synthesize paired 3D teeth models pre- and post-orthodontic, aiming to facilitate the training of downstream tooth arrangement networks. Specifically, our approach consists of two key modules: (1) a teeth shape generation module that leverages a diffusion model to learn the distribution of morphological characteristics of teeth, enabling the generation of diverse post-orthodontic teeth models; and (2) a teeth style generation module that synthesizes corresponding pre-orthodontic teeth models by incorporating desired styles as conditional inputs. Extensive qualitative and quantitative experiments demonstrate that our synthetic dataset aligns closely with the distribution of real orthodontic data, and promotes tooth alignment performance significantly when combined with real data for training. The code and dataset are available at https://github.com/lcshhh/teeth generator.
Enhancing Zero-shot Object Counting via Text-guided Local Ranking and Number-evoked Global Attention
Authors: Shiwei Zhang; Qi Zhou; Wei Ke
Venue: ICCV 2025 (main)

Abstract: Text-guided zero-shot object counting leverages vision-language models (VLMs) to count objects of an arbitrary class given by a text prompt. Existing approaches for this challenging task only utilize local patch-level features to fuse with text feature, ignoring the important influence of the global image-level feature. In this paper, we propose a universal strategy that can exploit both local patch-level features and global image-level feature simultaneously. Specifically, to improve the localization ability of VLMs, we propose Text-guided Local Ranking. Depending on the prior knowledge that foreground patches have higher similarity with the text prompt, a new local-text rank loss is designed to increase the differences between the similarity scores of foreground and background patches which push foreground and background patches apart. To enhance the counting ability of VLMs, Number-evoked Global Attention is introduced to first align global image-level feature with multiple number-conditioned text prompts. Then, the one with the highest similarity is selected to compute cross-attention with the global image-level feature. Through extensive experiments on widely used datasets and methods, the proposed approach has demonstrated superior advancements in performance, generalization, and scalability. Furthermore, to better evaluate text-guided zero-shot object counting methods, we propose a dataset named ZSC-8K, which is larger and more challenging, to establish a new benchmark. Codes and ZSC-8K dataset will be available.
Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval
Authors: Dohwan Ko; Ji Soo Lee; Minhyuk Choi; Zihang Meng; Hyunwoo J. Kim
Venue: ICCV 2025 (main)

Abstract: Text-Video Retrieval aims to find the most relevant text (or video) candidate given a video (or text) query from large-scale online databases. Recent work leverages multi-modal large language models (MLLMs) to improve retrieval, especially for long or complex query-candidate pairs. However, we observe that the naive application of MLLMs, i.e., retrieval based on candidate likelihood, introduces candidate prior bias, favoring candidates with inherently higher priors over those more relevant to the query. To this end, we propose a novel retrieval framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which leverages both query and candidate likelihoods by training the model to generate text from a given video as well as video features from a given text. Furthermore, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module designed to mitigate candidate prior bias in candidate likelihood. On four Text-Video Retrieval benchmarks, our BLiM equipped with CPN outperforms previous state-of-the-art models by 6.4 R@1 on average, effectively alleviating candidate prior bias and emphasizing query-candidate relevance. Our in-depth analysis across various multi-modal tasks beyond retrieval highlights the broad applicability of CPN which enhances visual understanding by reducing reliance on textual priors. Code is available at \href https://github.com/mlvlab/BLiM  https://github.com/mlvlab/BLiM .