GenAI4ELab/papercli-papers huggingface dataset:
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Authors: Guowei Xu; Peng Jin; Ziang Wu; Hao Li; Yibing Song; Lichao Sun; Li Yuan
Venue: ICCV 2025 (main)

Abstract: Large language models have demonstrated substantial advancements in reasoning capabilities. However, current Vision-Language Models (VLMs) often struggle to perform systematic and structured reasoning, especially when handling complex visual question-answering tasks. In this work, we introduce LLaVA-CoT, a large VLM designed to conduct autonomous multistage reasoning. Unlike chain-of-thought prompting, LLaVA-CoT independently engages in sequential stages of summarization, visual interpretation, logical reasoning, and conclusion generation. This structured approach enables LLaVA-CoT to achieve marked improvements on reasoning-intensive tasks. To accomplish this, we construct the LLaVA-CoT-100k dataset, integrating samples from various visual question answering sources and providing structured reasoning annotations. Besides, we propose a test-time stage-wise retracing search method (SWIRES), which enables effective and efficient test-time scaling. Remarkably, with only 100k training samples and test-time scaling, LLaVA-CoT not only outperforms its base model by 9.4% on a wide range of multimodal reasoning benchmarks, but also surpasses the performance of larger and even closed-source models, such as Gemini-1.5-pro, GPT-4o-mini, and Llama-3.2-90B-Vision-Instruct. The code, dataset, and pre-trained weights are publicly available at https://github.com/PKU-YuanGroup/LLaVA-CoT.
O-MaMa: Learning Object Mask Matching between Egocentric and Exocentric Views
Authors: Lorenzo Mur-Labadia; Maria Santos-Villafranca; Jesus Bermudez-Cameo; Alejandro Perez-Yus; Ruben Martinez-Cantin; Jose J. Guerrero
Venue: ICCV 2025 (main)

Abstract: Understanding the world from multiple perspectives is essential for intelligent systems operating together, where segmenting common objects across different views remains an open problem. We introduce a new approach that re-defines cross-image segmentation by treating it as a mask matching task. Our method consists of: (1) A Mask-Context Encoder that pools dense DINOv2 semantic features to obtain discriminative object-level representations from FastSAM mask candidates, (2) an Ego-Exo Cross-Attention that fuses multi-perspective observations, (3) a Mask Matching contrastive loss that aligns cross-view features in a shared latent space, and (4) a Hard Negative Adjacent Mining strategy to encourage the model to better differentiate between nearby objects. O-MaMa achieves the state of the art in the Ego-Exo4D Correspondences benchmark, obtaining relative gains of +22 % and +76 % in the Ego2Exo and Exo2Ego IoU against the official challenge baselines, and a +13 % and +6 % compared with the SOTA with 1 % of the training parameters.
Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities
Authors: Liuyi Wang; Xinyuan Xia; Hui Zhao; Hanqing Wang; Tai Wang; Yilun Chen; Chengju Liu; Qijun Chen; Jiangmiao Pang
Venue: ICCV 2025 (main)

Abstract: Recent Vision-and-Language Navigation (VLN) advancements are promising, but their idealized assumptions about robot movement and control fail to reflect physically embodied deployment challenges. To bridge this gap, we introduce VLN-PE, a physically realistic VLN platform supporting humanoid, quadruped, and wheeled robots. For the first time, we systematically evaluate several ego-centric VLN methods in physical robotic settings across different technical pipelines, including classification models for single-step discrete action prediction, a diffusion model for dense waypoint prediction, and a train-free, map-based large language model (LLM) integrated with path planning. Our results reveal significant performance degradation due to limited robot observation space, environmental lighting variations, and physical challenges like collisions and falls. This also exposes locomotion constraints for legged robots in complex environments. VLN-PE is highly extensible, allowing seamless integration of new scenes beyond MP3D, thereby enabling more comprehensive VLN evaluation. Despite the weak generalization of current models in physical deployment, VLN-PE provides a new pathway for improving cross-embodiment's overall adaptability. We hope our findings and tools inspire the community to rethink VLN limitations and advance robust, practical VLN models. The code is available at https://crystalsixone.github.io/vln_pe.github.io/.
Pseudo-SD: Pseudo Controlled Stable Diffusion for Semi-Supervised and Cross-Domain Semantic Segmentation
Authors: Dong Zhao; Qi Zang; Shuang Wang; Nicu Sebe; Zhun Zhong
Venue: ICCV 2025 (main)

Abstract: Pseudo-labeling is a key technique of semi-supervised and cross-domian semantic segmentation, yet its efficacy is often hampered by the intrinsic noise of pseudo-labels. This study introduces Pseudo-SD, a novel framework that redefines the utilization of pseudo-label knowledge through Stable Diffusion (SD). Our Pseudo-SD innovatively combines pseudo-labels and its text prompts to fine-tune SD models, facilitating the generation of high-quality, diverse synthetic images that closely mimic target data characteristics. Within this framework, two novel mechanisms, i.e., partial attention manipulation, and structured pseudo-labeling, are proposed to effectively spread text-to-image corresponding during SD fine-tuning process and to ensure controllable high-quality image synthesis respectively. Extensive results demonstrate that Pseudo-SD significantly improves the performance on semi-supervised and cross-domain segmentation scenarios. By injecting our Pseudo-SD into current methods, we establish new state-of-the-arts in different datasets, offering a new way for the exploration of effective pseudo-label utilization. The source code is available at \href https://github.com/DZhaoXd/Pseudo-SD  https://github.com/DZhaoXd/Pseudo-SD .
MaTVLM: Hybrid Mamba-Transformer for Efficient Vision-Language Modeling
Authors: Yingyue Li; Bencheng Liao; Wenyu Liu; Xinggang Wang
Venue: ICCV 2025 (main)

Abstract: With the advancement of RNN models with linear complexity, the quadratic complexity challenge of transformers has the potential to be overcome. Notably, the emerging Mamba-2 has demonstrated competitive performance, bridging the gap between RNN models and transformers. However, due to sequential processing and vanishing gradients, RNN models struggle to capture long-range dependencies, limiting contextual understanding. This results in slow convergence, high resource demands, and poor performance on downstream understanding and complex reasoning tasks. In this work, we present MaTVLM, a method for distilling pre-trained vision-language models (VLMs) into an efficient Mamba-Transformer hybrid architecture. Specifically, we construct this hybrid architecture by replacing a portion of the transformer decoder layers in the pre-trained VLM with Mamba-2 layers. Building on this design, we employ a single-stage distillation process, incorporating a clever initialization strategy, leveraging the inherent relationship between attention mechanisms and Mamba-2, and initialize Mamba-2 with corresponding attention weights, which notably accelerates convergence. With the pre-trained VLM serving as the teacher model, this distillation process further boosts both convergence speed and model performance. Furthermore, we investigate the impact of differential distillation loss within our training framework. We evaluate MaTVLM on multiple benchmarks, demonstrating competitive performance against the teacher model and existing VLMs while surpassing both Mamba-based VLMs and models of comparable parameter scales. Remarkably, MaTVLM achieves up to 4.3 times faster inference than the teacher model while reducing GPU memory consumption by 27.5%, all without compromising performance. Code and models are released at https://github.com/hustvl/MaTVLM.
ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness
Authors: Boqian Li; Haiwen Feng; Zeyu Cai; Michael J. Black; Yuliang Xiu
Venue: ICCV 2025 (main)

Abstract: Fitting a body to a 3D clothed human point cloud is a common yet challenging task. Traditional optimization-based approaches use multi-stage pipelines that are sensitive to pose initialization, while recent learning-based methods often struggle with generalization across diverse poses and garment types. We propose Equivariant Tightness Fitting for Clothed Humans, or ETCH, a novel pipeline that estimates cloth-to-body surface mapping through locally approximate SE(3) equivariance, encoding tightness as displacement vectors from the cloth surface to the underlying body. Following this mapping, pose-invariant body features regress sparse body markers, simplifying clothed human fitting into an inner-body marker fitting task. Extensive experiments on CAPE and 4D-Dress show that ETCH significantly outperforms state-of-the-art methods -- both tightness-agnostic and tightness-aware -- in body fitting accuracy on loose clothing (16.7%   69.5%) and shape accuracy (average 49.9%). Our equivariant tightness design can even reduce directional errors by (67.2%   89.8%) in one-shot (or out-of-distribution) settings ( 1% data). Qualitative results demonstrate strong generalization of ETCH, regardless of challenging poses, unseen shapes, loose clothing, and non-rigid dynamics. We will release the code and models soon for research purposes at boqian-li.github.io/ETCH.
SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning
Authors: Zhi Chen; Zecheng Zhao; Jingcai Guo; Jingjing Li; Zi Huang
Venue: ICCV 2025 (main)

Abstract: Zero-shot learning (ZSL) aims to recognize unseen classes without labeled training examples by leveraging class-level semantic descriptors such as attributes. A fundamental challenge in ZSL is semantic misalignment, where semantic-unrelated information involved in visual features introduce ambiguity to visual-semantic interaction. Unlike existing methods that suppress semantic-unrelated information post hoc either in the feature space or the model space, we propose addressing this issue at the input stage, preventing semantic-unrelated patches from propagating through the network. To this end, we introduce Semantically contextualized VIsual Patches (SVIP) for ZSL, a transformer-based framework designed to enhance visual-semantic alignment. Specifcally, we propose a self-supervised patch selection mechanism that preemptively learns to identify semantic-unrelated patches in the input space. This is trained with the supervision from aggregated attention scores across all transformer layers, which estimate each patch's semantic score. As removing semantic-unrelated patches from the input sequence may disrupt object structure, we replace them with learnable patch embeddings. With initialization from word embeddings, we can ensure they remain semantically meaningful throughout feature extraction. Extensive experiments on ZSL benchmarks demonstrate that SVIP achieves state-of-the-art performance results while providing more interpretable and semantically rich feature representations. Code is available at https://github.com/uqzhichen/SVIP.
MamTiff-CAD: Multi-Scale Latent Diffusion with Mamba+ for Complex Parametric Sequence
Authors: Liyuan Deng; Yunpeng Bai; Yongkang Dai; Xiaoshui Huang; Hongping Gan; Dongshuo Huang; Hao Jiacheng; Yilei Shi
Venue: ICCV 2025 (main)

Abstract: Parametric Computer-Aided Design (CAD) is crucial in industrial applications, yet existing approaches often struggle to generate long sequence parametric commands due to complex CAD models' geometric and topological constraints. To address this challenge, we propose MamTiff-CAD, a novel CAD parametric command sequences generation framework that leverages a Transformer-based diffusion model for multi-scale latent representations. Specifically, we design a novel autoencoder that integrates Mamba+ and Transformer, to transfer parameterized CAD sequences into latent representations. The Mamba+ block incorporates a forget gate mechanism to effectively capture long-range dependencies. The non-autoregressive Transformer decoder reconstructs the latent representations. A diffusion model based on multi-scale Transformer is then trained on these latent embeddings to learn the distribution of long sequence commands. In addition, we also construct a dataset that consists of long parametric sequences, which is up to 256 commands for a single CAD model. Experiments demonstrate that MamTiff-CAD achieves state-of-the-art performance on both reconstruction and generation tasks, confirming its effectiveness for long sequence (60-256) CAD model generation.
Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation
Authors: Qi Guo; Zhen Tian; Minghao Yao; Saiyu Qi; Yong Qi; Bingyi Liu
Venue: ICCV 2025 (main)

Abstract: Federated Unlearning (FU) should satisfy three key requirements: a guarantee of data erasure, preservation of model utility, and reduction of unlearning time. Recent studies focus on identifying and modifying original model parameters relevant to unlearning data. While they can achieve faster unlearning, they degrade the model performance on remaining data or fail to forget unlearning data due to the difficulty in isolating specific parameters of the unlearning data. By revisiting the representation distribution of the optimal unlearning models (i.e., the retrained models), we observe that unlearning data tends to cluster within semantically related categories of remaining data. This inspired us to transform the distribution of unlearning data to fuse with similar categories in the remaining data for effective FU. Based on this insight, we propose a novel framework, named FUCRT, to achieve Federated Unlearning via Class-aware Representation Transformation. FUCRT consists of two key components: (1) a transformation class identification strategy (TCI) that leverages the original model to identify appropriate transformation classes for unlearning data, and (2) a targeted transformation learning process (TTL) with cross-class fusion mechanism to ensure effective and consistent transformation. Extensive experiments on four datasets demonstrate that FUCRT not only achieves 100% of data erasure but also outperforms state-of-the-art methods by an average of 2.96% and 3.78% in utility preservation under IID and Non-IID settings, respectively. Moreover, it reduces unlearning time by 19.13%~ 96.38%. Codes are at https://github.com/zhentian777/FUCRT.
ReasonVQA: A Multi-hop Reasoning Benchmark with Structural Knowledge for Visual Question Answering
Authors: Duong T. Tran; Trung-Kien Tran; Manfred Hauswirth; Danh Le Phuoc
Venue: ICCV 2025 (main)

Abstract: In this paper, we propose a new dataset, ReasonVQA, for the Visual Question Answering (VQA) task. Our dataset is automatically integrated with structured encyclopedic knowledge and constructed using a low-cost framework, which is capable of generating complex, multi-hop questions. We evaluated state-of-the-art VQA models on ReasonVQA, and the empirical results demonstrate that ReasonVQA poses significant challenges to these models, highlighting its potential for benchmarking and advancing the field of VQA. Additionally, our dataset can be easily scaled with respect to input images; the current version surpasses the largest existing datasets requiring external knowledge by more than an order of magnitude.
HiNeuS: High-fidelity Neural Surface Mitigating Low-texture and Reflective Ambiguity
Authors: Yida Wang; Xueyang Zhang; Kun Zhan; Peng Jia; Xianpeng Lang
Venue: ICCV 2025 (main)

Abstract: Neural surface reconstruction faces persistent challenges in reconciling geometric fidelity with photometric consistency under complex scene conditions. We present HiNeuS, a unified framework that holistically addresses three core limitations in existing approaches: multi-view radiance inconsistency, missing keypoints in textureless regions, and structural degradation from over-enforced Eikonal constraints during joint optimization. To resolve these issues through a unified pipeline, we introduce: 1) Differential visibility verification through SDF-guided ray tracing, resolving reflection ambiguities via continuous occlusion modeling; 2) Planar-conformal regularization via ray-aligned geometry patches that enforce local surface coherence while preserving sharp edges through adaptive appearance weighting; and 3) Physically-grounded Eikonal relaxation that dynamically modulates geometric constraints based on local radiance gradients, enabling detail preservation without sacrificing global regularity. Unlike prior methods that handle these aspects through sequential optimizations or isolated modules, our approach achieves cohesive integration where appearance-geometry constraints evolve synergistically throughout training. Comprehensive evaluations across synthetic and real-world datasets demonstrate SotA performance, including a 21.4% reduction in Chamfer distance over reflection-aware baselines and 2.32 dB PSNR improvement against neural rendering counterparts. Qualitative analyses reveal superior capability in recovering specular instruments, urban layouts with centimeter-scale infrastructure, and low-textured surfaces without local patch collapse. The method's generalizability is further validated through successful application to inverse rendering tasks, including material decomposition and view-consistent relighting. Project hosted at https://wangyida.github.io/posts/hineus, where the urban and vehicle reconstruction related modules are excluded from open-sourced codes due to legal concerns.
Rethinking DPO-style Diffusion Aligning Frameworks
Authors: Xun Wu; Shaohan Huang; Lingjie Jiang; Furu Wei
Venue: ICCV 2025 (main)

Abstract: Direct preference optimization (DPO) has shown success in aligning diffusion models with human preference. However, We identify two potential risks for existing DPO algorithms: First, current DPO methods for estimating the rewards of step-wise intermediate samples are biased, leading to inaccurate preference ordering for step-wise optimization. Second, existing DPO methods may inadvertently increase the sampling probabilities of dispreferred samples, potentially introducing application risks. To address these issues, we propose Revised Direct Preference Optimization (RDPO), a simple but effective step-wise DPO-based text-to-image diffusion model alignment method. By designing a more theoretically grounded and efficient intermediate-step reward estimation and introducing an additional regularization terms to constrain the sampling probability of dispreferred samples, RDPO can achieve more effective and stable text-to-image alignment performance. Our experiments on two datasets, with base models including Stable Diffusion v1.5 and SDXL, demonstrate that RDPO can effectively learn and construct reward signals for each step of the model, improving alignment performance while ensuring better generalization.
VSC: Visual Search Compositional Text-to-Image Diffusion Model
Authors: Do Huu Dat; Nam Hyeon-Woo; Po-Yuan Mao; Tae-Hyun Oh
Venue: ICCV 2025 (main)

Abstract: Text-to-image diffusion models have shown impressive capabilities in generating realistic visuals from natural-language prompts, yet they often struggle with accurately binding attributes to corresponding objects, especially in prompts containing multiple attribute-object pairs. This challenge primarily arises from the limitations of commonly used text encoders, such as CLIP, which can fail to encode complex linguistic relationships and modifiers effectively. Existing approaches have attempted to mitigate these issues through attention map control during inference and the use of layout information or fine-tuning during training, yet they face performance drops with increased prompt complexity. In this work, we introduce a novel compositional generation method that leverages pairwise image embeddings to improve attribute-object binding. Our approach decomposes complex prompts into sub-prompts, generates corresponding images, and computes visual prototypes that fuse with text embeddings to enhance representation. By applying segmentation-based localization training, we address cross-attention misalignment, achieving improved accuracy in binding multiple attributes to objects. Our approaches outperform existing compositional text-to-image diffusion models on the benchmark T2I CompBench, achieving better image quality, evaluated by humans, and emerging robustness under scaling the number of binding pairs in the prompt.
FRET: Feature Redundancy Elimination for Test Time Adaptation
Authors: Linjing You; Jiabao Lu; Xiayuan Huang; Xiangli Nie
Venue: ICCV 2025 (main)

Abstract: Test-Time Adaptation (TTA) aims to enhance the generalization of deep learning models when faced with test data that exhibits distribution shifts from the training data. In this context, only a pre-trained model and unlabeled test data are available, making it particularly relevant for privacy-sensitive applications. In practice, we observe that feature redundancy in embeddings tends to increase as domain shifts intensify in TTA. However, existing TTA methods often overlook this redundancy, which can hinder the model's adaptability to new data. To address this issue, we introduce Feature Redundancy Elimination for Test-time Adaptation (FRET), a novel perspective for TTA. A straightforward approach (S-FRET) is to directly minimize the feature redundancy score as an optimization objective to improve adaptation. Despite its simplicity and effectiveness, S-FRET struggles with label shifts, limiting its robustness in real-world scenarios. To mitigate this limitation, we further propose Graph-based FRET (G-FRET), which integrates a Graph Convolutional Network (GCN) with contrastive learning. This design not only reduces feature redundancy but also enhances feature discriminability in both the representation and prediction layers. Extensive experiments across multiple model architectures, tasks, and datasets demonstrate the effectiveness of S-FRET and show that G-FRET achieves state-of-the-art performance. Further analysis reveals that G-FRET enables the model to extract non-redundant and highly discriminative features during inference, thereby facilitating more robust test-time adaptation.