GenAI4ELab/papercli-papers huggingface dataset:
3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection
Authors: Yung-Hsu Yang; Luigi Piccinelli; Mattia Segu; Siyuan Li; Rui Huang; Yuqian Fu; Marc Pollefeys; Hermann Blum; Zuria Bauer
Venue: ICCV 2025 (main)

Abstract: Monocular 3D object detection is valuable for various applications such as robotics and AR/VR. Existing methods are confined to closed-set settings, where the training and testing sets consist of the same scenes and/or object categories. However, real-world applications often introduce new environments and novel object categories, posing a challenge to these methods. In this paper, we address monocular 3D object detection in an open-set setting and introduce the first end-to-end 3D Monocular Open-set Object Detector (3D-MOOD). We propose to lift the open-set 2D detection into 3D space through our designed 3D bounding box head, enabling end-to-end joint training for both 2D and 3D tasks to yield better overall performance. We condition the object queries with geometry prior and overcome the generalization for 3D estimation across diverse scenes. To further improve performance, we design the canonical image space for more efficient cross-dataset training. We evaluate 3D-MOOD on both closed-set settings (Omni3D) and open-set settings (Omni3D - Argoverse 2, ScanNet), and achieve new state-of-the-art results. Code and models are available at royyang0714.github.io/3D-MOOD.
LLM-enhanced Action-aware Multi-modal Prompt Tuning for Image-Text Matching
Authors: Mengxiao Tian; Xinxiao Wu; Shuo Yang
Venue: ICCV 2025 (main)

Abstract: Driven by large-scale contrastive vision-language pre-trained models such as CLIP, recent advancements in the image-text matching task have achieved remarkable success in representation learning. Due to image-level visual-language alignment, CLIP falls short in understanding fine-grained details such as object attributes and spatial relationships between objects. Recent efforts have attempted to compel CLIP to acquire structured visual representations by introducing prompt learning to achieve object-level alignment. While achieving promising results, they still lack the capability to perceive actions, which are crucial for describing the states or relationships between objects. Therefore, we propose to endow CLIP with fine-grained action-level understanding by introducing an LLM-enhanced action-aware multi-modal prompt-tuning method, incorporating the action-related external knowledge generated by large language models (LLMs). Specifically, we design an action triplet prompt and an action state prompt to exploit compositional semantic knowledge and state-related causal knowledge implicitly stored in LLMs. Subsequently, we propose an adaptive interaction module to aggregate attentive visual features conditioned on action-aware prompted knowledge for establishing discriminative and action-aware visual representations, which further improves the performance. Comprehensive experimental results on two benchmark datasets demonstrate the effectiveness of our method.
PointGAC: Geometric-Aware Codebook for Masked Point Modeling
Authors: Abiao Li; Chenlei Lv; Yuming Fang; Yifan Zuo; Jian Zhang; Guofeng Mei
Venue: ICCV 2025 (main)

Abstract: Most masked point cloud modeling (MPM) methods follow a regression paradigm to reconstruct the coordinate or feature of masked regions. However, they tend to over-constrain the model to learn the details of the masked region, resulting in failure to capture generalized features. To address this limitation, we propose PointGAC, a novel clustering-based MPM method that aims to align the feature distribution of masked regions. Specially, it features an online codebook-guided teacher-student framework. Firstly, it presents a geometry-aware partitioning strategy to extract initial patches. Then, the teacher model updates a codebook via online K-means based on features extracted from the complete patches. This procedure facilitates codebook vectors to become cluster centers. Afterward, we assigns the unmasked features to their corresponding cluster centers, and the student model aligns the assignment for the reconstructed masked features. This strategy focuses on identifying the cluster centers to which the masked features belong, enabling the model to learn more generalized feature representations. Benefiting from a proposed codebook maintenance mechanism, codebook vectors are actively updated, which further increases the efficiency of semantic feature learning. Experiments validate the effectiveness of the proposed method on various downstream tasks. Code is available at https://github.com/LAB123-tech/PointGAC.
Unbiased Region-Language Alignment for Open-Vocabulary Dense Prediction
Authors: Yunheng Li; Yuxuan Li; Quan-Sheng Zeng; Wenhai Wang; Qibin Hou; Ming-Ming Cheng
Venue: ICCV 2025 (main)

Abstract: Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot recognition capability, but still underperform in dense prediction tasks. Self-distillation recently is emerging as a promising approach for fine-tuning VLMs to better adapt to local regions without requiring extensive annotations. However, previous state-of-the-art approaches often suffer from significant `foreground bias', where models tend to wrongly identify background regions as foreground objects. To alleviate this issue, we propose DenseVLM, a framework designed to learn unbiased region-language alignment from powerful pre-trained VLM representations. To alleviate this issue, we propose DenseVLM, a framework designed to learn unbiased region-language alignment from powerful pre-trained VLM representations. DenseVLM leverages the pre-trained VLM to retrieve categories for unlabeled regions and then decouples the interference between foreground and background features. We show that DenseVLM can directly replace the original VLM in open-vocabulary object detection and image segmentation methods, leading to notable performance improvements. Furthermore, it exhibits promising zero-shot scalability when training on more extensive and diverse datasets. Our code is publicly available https://github.com/HVision-NKU/DenseVLM.
CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation
Authors: Lin Sun; Jiale Cao; Jin Xie; Xiaoheng Jiang; Yanwei Pang
Venue: ICCV 2025 (main)

Abstract: Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on image-level tasks, leading to the research to adapt CLIP for open-vocabulary semantic segmentation without training. The key is to improve spatial representation of image-level CLIP, such as replacing self-attention map at last layer with self-self attention map or vision foundation model based attention map. In this paper, we present a novel hierarchical framework, named CLIPer, that hierarchically improves spatial representation of CLIP. The proposed CLIPer includes an early-layer fusion and a fine-grained compensation. We observe that, the embeddings and attention maps at early layers can preserve spatial structural information. Inspired by this, we design the early-layer fusion module to generate segmentation map with better spatial coherence. Afterwards, we employ a fine-grained compensation module to compensate local details using the self-attention maps of diffusion model. We conduct the experiments on eight segmentation datasets. Our CLIPer achieves the state-of-the-art performance on these datasets. With ViT-L and sliding-window inference, CLIPer has the mIoU of 72.2% and 44.7% on VOC and Object, outperforming ProxyCLIP by 11.6% and 5.5%. Our code is available at https://github.com/linsun449/cliper.code.
Adversarial Reconstruction Feedback for Robust Fine-grained Generalization
Authors: Shijie Wang; Jian Shi; Haojie Li
Venue: ICCV 2025 (main)

Abstract: Existing fine-grained image retrieval (FGIR) methods predominantly rely on supervision from predefined categories to learn discriminative representations for retrieving fine-grained objects. However, they inadvertently introduce category-specific semantics into the retrieval representation, creating semantic dependencies on predefined classes that critically hinder generalization to unseen categories. To tackle this, we propose AdvRF, a novel adversarial reconstruction feedback framework aimed at learning category-agnostic discrepancy representations. Specifically, AdvRF reformulates FGIR as a visual discrepancy reconstruction task via synergizing category-aware discrepancy localization from retrieval models with category-agnostic feature learning from reconstruction models. The reconstruction model exposes residual discrepancies overlooked by the retrieval model, forcing it to improve localization accuracy, while the refined signals from the retrieval model guide the reconstruction model to improve its reconstruction ability. Consequently, the retrieval model localizes visual differences, while the reconstruction model encodes these differences into category-agnostic representations. This representation is then transferred to the retrieval model through knowledge distillation for efficient deployment. Quantitative and qualitative evaluations demonstrate that our AdvRF achieves impressive performance on both widely-used fine-grained and coarse-grained datasets.
ConceptSplit: Decoupled Multi-Concept Personalization of Diffusion Models via Token-wise Adaptation and Attention Disentanglement
Authors: Habin Lim; Yeongseob Won; Juwon Seo; Gyeong-Moon Park
Venue: ICCV 2025 (main)

Abstract: In recent years, multi-concept personalization for text-to-image (T2I) diffusion models to represent several subjects at an image has gained much more attention. The main challenge of this task is "concept mixing", where multiple learned concepts interfere or blend undesirably in the output image. To address this issue, in this paper, we present ConceptSplit, a novel framework to split the individual concepts through training and inference. Our framework comprises two key components. First, we introduce Token-wise Value Adaptation (ToVA), a merging-free training method that focuses exclusively on adapting the value projection in cross-attention. Based on our empirical analysis, we found that modifying the key projection, a common approach in existing methods, can disrupt the attention mechanism and lead to concept mixing. Second, we propose Latent Optimization for Disentangled Attention (LODA), which alleviates attention entanglement during inference by optimizing the input latent. Through extensive qualitative and quantitative experiments, we demonstrate that ConceptSplit achieves robust multi-concept personalization, mitigating unintended concept interference. Code is available at https://github.com/KU-VGI/ConceptSplit
Consistency Trajectory Matching for One-Step Generative Super-Resolution
Authors: Weiyi You; Mingyang Zhang; Leheng Zhang; Xingyu Zhou; Kexuan Shi; Shuhang Gu
Venue: ICCV 2025 (main)

Abstract: Current diffusion-based super-resolution (SR) approaches achieve commendable performance at the cost of high inference overhead. Therefore, distillation techniques are utilized to accelerate the multi-step teacher model into one-step student model. Nevertheless, these methods significantly raise training costs and constrain the performance of the student model by the teacher model. To overcome these tough challenges, we propose Consistency Trajectory Matching for Super-Resolution (CTMSR), a distillation-free strategy that is able to generate photo-realistic SR results in one step. Concretely, we first formulate a Probability Flow Ordinary Differential Equation (PF-ODE) trajectory to establish a deterministic mapping from low-resolution (LR) images with noise to high-resolution (HR) images. Then we apply the Consistency Training (CT) strategy to directly learn the mapping in one step, eliminating the necessity of pre-trained diffusion model. To further enhance the performance and better leverage the ground-truth during the training process, we aim to align the distribution of SR results more closely with that of the natural images. To this end, we propose to minimize the discrepancy between their respective PF-ODE trajectories from the LR image distribution by our meticulously designed Distribution Trajectory Matching (DTM) loss, resulting in improved realism of our recovered HR images. Comprehensive experimental results demonstrate that the proposed methods can attain comparable or even superior capabilities on both synthetic and real datasets while maintaining minimal inference latency.
SGAD: Semantic and Geometric-aware Descriptor for Local Feature Matching
Authors: Xiangzeng Liu; Chi Wang; Guanglu Shi; Xiaodong Zhang; Qiguang Miao; Miao Fan
Venue: ICCV 2025 (main)

Abstract: Local feature matching remains a fundamental challenge in computer vision. Recent Area to Point Matching (A2PM) methods have improved matching accuracy. However, existing research based on this framework relies on inefficient pixel-level comparisons and complex graph matching that limit scalability. In this work, we introduce the Semantic and Geometric-aware Descriptor Network (SGAD), which fundamentally rethinks area-based matching by generating highly discriminative area descriptors that enable direct matching without complex graph optimization. This approach significantly improves both accuracy and efficiency of area matching. We further improve the performance of area matching through a novel supervision strategy that decomposes the area matching task into classification and ranking subtasks. Finally, we introduce the Hierarchical Containment Redundancy Filter (HCRF) to eliminate overlapping areas by analyzing containment graphs. SGAD demonstrates remarkable performance gains, reducing runtime by 60x(0.82s vs. 60.23s) compared to MESA. Extensive evaluations show consistent improvements across multiple point matchers: SGAD+LoFTR reduces runtime compared to DKM, while achieving higher accuracy (0.82s vs. 1.51s, 65.98 vs. 61.11) in outdoor pose estimation, and SGAD+ROMA delivers +7.39% AUC@5^\circ in indoor pose estimation, establishing a new state-of-the-art.
SpectralAR: Spectral Autoregressive Visual Generation
Authors: Yuanhui Huang; Weiliang Chen; Wenzhao Zheng; Yueqi Duan; Jie Zhou; Jiwen Lu
Venue: ICCV 2025 (main)

Abstract: Autoregressive visual generation has garnered increasing attention due to its scalability and compatibility with other modalities compared with diffusion models. Most existing methods construct visual sequences as spatial patches for autoregressive generation. However, image patches are inherently parallel, contradicting the causal nature of autoregressive modeling. To address this, we propose a Spectral AutoRegressive (SpectralAR) visual generation framework, which realizes causality for visual sequences from the spectral perspective. Specifically, we first transform an image into ordered spectral tokens with Nested Spectral Tokenization, representing lower to higher frequency components. We then perform autoregressive generation in a coarse-to-fine manner with the sequences of spectral tokens. By considering different levels of detail in images, our SpectralAR achieves both sequence causality and token efficiency without bells and whistles. We conduct extensive experiments on ImageNet-1K for image reconstruction and autoregressive generation, and SpectralAR achieves 3.02 gFID with only 64 tokens and 310M parameters. Project page: https://huang-yh.github.io/spectralar/.
RogSplat: Robust Gaussian Splatting via Generative Priors
Authors: Hanyang Kong; Xingyi Yang; Xinchao Wang
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian Splatting (3DGS) has recently emerged as an efficient representation for high-quality 3D reconstruction and rendering. Despite its superior rendering quality and speed, 3DGS heavily relies on the assumption of geometric consistency among input images. In real-world scenarios, violations of this assumption--such as occlusions, dynamic objects, or camera blur--often lead to reconstruction artifacts and rendering inaccuracies. To address these challenges, we introduce RogSplat, a robust framework that leverages generative models to enhance the reliability of 3DGS. Specifically, RogSplat identifies and rectifies occluded regions during the optimization of unstructured scenes. Outlier regions are first detected using our proposed fused features and then accurately inpainted by the proposed RF-Refiner, ensuring reliable reconstruction of occluded areas while preserving the integrity of visible regions. Extensive experiments demonstrate that RogSplat achieves state-of-the-art reconstruction quality on the RobustNeRF and NeRF-on-the-go datasets, significantly outperforming existing methods in challenging real-world scenarios involving dynamic objects.
When Schrodinger Bridge Meets Real-World Image Dehazing with Unpaired Training
Authors: Yunwei Lan; Zhigao Cui; Xin Luo; Chang Liu; Nian Wang; Menglin Zhang; Yanzhao Su; Dong Liu
Venue: ICCV 2025 (main)

Abstract: Recent advancements in unpaired dehazing, particularly those using GANs, show promising performance in processing real-world hazy images. However, these methods tend to face limitations due to the generator's limited transport mapping capability, which hinders the full exploitation of their effectiveness in unpaired training paradigms. To address these challenges, we propose DehazeSB, a novel unpaired dehazing framework based on the Schrodinger Bridge. By leveraging optimal transport (OT) theory, DehazeSB directly bridges the distributions between hazy and clear images. This enables optimal transport mappings from hazy to clear images in fewer steps, thereby generating high-quality results. To ensure the consistency of structural information and details in the restored images, we introduce detail-preserving regularization, which enforces pixel-level alignment between hazy inputs and dehazed outputs. Furthermore, we propose a novel prompt learning to leverage pre-trained CLIP models in distinguishing hazy images and clear ones, by learning a haze-aware vision-language alignment. Extensive experiments on multiple real-world datasets demonstrate our method's superiority.
CARIM: Caption-Based Autonomous Driving Scene Retrieval via Inclusive Text Matching
Authors: Minjoo Ki; Daejung Kim; Kisung Kim; Seon Joo Kim; Jinhan Lee
Venue: ICCV 2025 (main)

Abstract: Text-to-video retrieval serves as a powerful tool for navigating vast video databases. This is particularly useful in autonomous driving to retrieve scenes from a text query to simulate and evaluate the driving system in desired scenarios. However, traditional ranking-based retrieval methods often return partial matches that do not satisfy all query conditions. To address this, we introduce Inclusive Text-to-Video Retrieval, which retrieves only videos that meet all specified conditions, regardless of additional irrelevant elements. We propose CARIM, a framework for driving scene retrieval that employs inclusive text matching. By utilizing Vision-Language Model (VLM) and Large Language Model (LLM) to generate compressed captions for driving scenes, we transform text-to-video retrieval into a more efficient text-to-text retrieval problem, eliminating modality mismatches and heavy annotation costs. We introduce a novel positive and negative data curation strategy and an attention-based scoring mechanism tailored for driving scene retrieval. Experimental results on the DRAMA dataset demonstrate that CARIM outperforms state-of-the-art retrieval methods, excelling in edge cases where traditional models fail.
Punching Bag vs. Punching Person: Motion Transferability in Videos
Authors: Raiyaan Abdullah; Jared Claypoole; Michael Cogswell; Ajay Divakaran; Yogesh Rawat
Venue: ICCV 2025 (main)

Abstract: Action recognition models demonstrate strong generalization, but can they effectively transfer high-level motion concepts across diverse contexts, even within similar distributions? For example, can a model recognize the broad action "punching" when presented with an unseen variation such as "punching person"? To explore this, we introduce a motion transferability framework with three datasets: (1) Syn-TA, a synthetic dataset with 3D object motions; (2) Kinetics400-TA; and (3) Something-Something-v2-TA, both adapted from natural video datasets. We evaluate 13 state-of-the-art models on these benchmarks and observe a significant drop in performance when recognizing high-level actions in novel contexts. Our analysis reveals: 1) Multimodal models struggle more with fine-grained unknown actions than with coarse ones; 2) The bias-free Syn-TA proves as challenging as real-world datasets, with models showing greater performance drops in controlled settings; 3) Larger models improve transferability when spatial cues dominate but struggle with intensive temporal reasoning, while reliance on object and background cues hinders generalization. We further explore how disentangling coarse and fine motions can improve recognition in temporally challenging datasets. We believe this study establishes a crucial benchmark for assessing motion transferability in action recognition. Datasets and relevant code: https://github.com/raiyaan-abdullah/Motion-Transfer.
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning
Authors: Marco P. E. Apolinario; Sakshi Choudhary; Kaushik Roy
Venue: ICCV 2025 (main)

Abstract: Continual learning (CL) -- the ability to progressively acquire and integrate new concepts -- is essential to intelligent systems to adapt to dynamic environments. However, deep neural networks struggle with catastrophic forgetting (CF) when learning tasks sequentially, as training for new tasks often overwrites previously learned knowledge. To address this, recent approaches constrain updates to orthogonal subspaces using gradient projection, effectively preserving important gradient directions for previous tasks. While effective in reducing forgetting, these approaches inadvertently hinder forward knowledge transfer (FWT), particularly when tasks are highly correlated. In this work, we propose Conceptor-based gradient projection for Deep Continual Learning (CODE-CL), a novel method that leverages conceptor matrix representations, a form of regularized reconstruction, to adaptively handle highly correlated tasks. CODE-CL mitigates CF by projecting gradients onto pseudo-orthogonal subspaces of previous task feature spaces while simultaneously promoting FWT. It achieves this by learning a linear combination of shared basis directions, allowing efficient balance between stability and plasticity and transfer of knowledge between overlapping input feature representations. Extensive experiments on continual learning benchmarks validate CODE-CL's efficacy, demonstrating superior performance, reduced forgetting, and improved FWT as compared to state-of-the-art methods.