GenAI4ELab/papercli-papers huggingface dataset:
Zero-Shot Composed Image Retrieval via Dual-Stream Instruction-Aware Distillation
Authors: Wenliang Zhong; Rob Barton; Weizhi An; Feng Jiang; Hehuan Ma; Yuzhi Guo; Abhishek Dan; Shioulin Sam; Karim Bouyarmane; Junzhou Huang
Venue: ICCV 2025 (main)

Abstract: Composed Image Retrieval (CIR) targets the retrieval of images conditioned on a reference image and a textual modification, but constructing labeled triplets (reference image, textual modification, target image) is inherently challenging. Existing Zero-Shot CIR (ZS-CIR) approaches often rely on well-aligned vision-language models (VLMs) to combine visual and textual inputs, or use large language models (LLMs) for richer modification understanding. While LLM-based methods excel in capturing textual details, they are computationally costly, slow to infer, and often restricted by proprietary constraints. In this paper, we argue that the superior performance of LLM-based ZS-CIR methods primarily stems from their capacity to follow instructions, an aspect largely missing in more efficient projection-based models built upon VLMs. To bridge this gap, we introduce DistillCIR, a dual-stream distillation framework that transfers LLMs' instruction-following capability into compact, projection-based architectures. By synthesizing triplet data with an LLM and incorporating a novel reasoning process, DistillCIR learns both composed retrieval and instruction awareness. In addition, we train an open-source multimodal LLM on the generated data, and further distill its instruction-aware embeddings into the projection-based model. Without any reliance on LLMs at inference, DistillCIR significantly surpasses state-of-the-art ZS-CIR methods in both performance and efficiency, offering a promising direction for instruction-aware, lightweight CIR.
Underwater Visual SLAM with Depth Uncertainty and Medium Modeling
Authors: Rui Liu; Sheng Fan; Wenguan Wang; Yi Yang
Venue: ICCV 2025 (main)

Abstract: Underwater visual simultaneous localization and mapping (SLAM) faces critical challenges in light attenuation and degraded geometric consistency. Despite recent advances of visual SLAM in indoor and urban scenes, these approaches typically assume a clear medium and neglect medium-light interactions, leading to performance degradation in underwater environments. To overcome these limitations, we propose DUV-SLAM, a dense underwater visual SLAM framework that integrates uncertainty-aware geometry estimation with physics-inspired neural scattering modeling. Our method introduces two core innovations: i) depth uncertainty quantification derived from differentiable bundle adjustment, which propagates geometric confidence to guide mapping optimization; and ii) a neural-Gaussian hybrid representation that combines adaptive 3D Gaussians for underwater reconstruction with a neural field capturing wavelength-dependent medium properties, optimized using a combination of photometric, geometric, and distribution losses. Experiments on synthetic and real-world datasets demonstrate that DUV-SLAM achieves high-quality monocular reconstruction while maintaining real-time efficiency and robust tracking accuracy.
Blind Video Super-Resolution based on Implicit Kernels
Authors: Qiang Zhu; Yuxuan Jiang; Shuyuan Zhu; Fan Zhang; David Bull; Bing Zeng
Venue: ICCV 2025 (main)

Abstract: Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Existing approaches typically predict blur kernels that are spatially invariant in each video frame or even the entire video. These methods do not consider potential spatio-temporal varying degradations in videos, resulting in suboptimal BVSR performance. In this context, we propose a novel BVSR model based on Implicit Kernels, BVSR-IK, which constructs a multi-scale kernel dictionary parameterized by implicit neural representations. It also employs a newly designed recurrent Transformer to predict the coefficient weights for accurate filtering in both frame correction and feature alignment. Experimental results have demonstrated the effectiveness of the proposed BVSR-IK, when compared with four state-of-the-art BVSR models on three commonly used datasets, with BVSR-IK outperforming the second best approach, FMA-Net, by up to 0.59 dB in PSNR. Source code will be available at https://github.com/QZ1-boy/BVSR-IK.
AMDANet: Attention-Driven Multi-Perspective Discrepancy Alignment for RGB-Infrared Image Fusion and Segmentation
Authors: Haifeng Zhong; Fan Tang; Zhuo Chen; Hyung Jin Chang; Yixing Gao
Venue: ICCV 2025 (main)

Abstract: The challenge of multimodal semantic segmentation lies in establishing semantically consistent and segmentable multimodal fusion features under conditions of significant visual feature discrepancies. Existing methods commonly construct cross-modal self-attention fusion frameworks or introduce additional multimodal fusion loss functions to establish fusion features. However, these approaches often overlook the challenge caused by feature discrepancies between modalities during the fusion process. To achieve precise segmentation, we propose an Attention-Driven Multimodal Discrepancy Alignment Network (AMDANet). AMDANet reallocates weights to reduce the saliency of discrepant features and utilizes low-weight features as cues to mitigate discrepancies between modalities, thereby achieving multimodal feature alignment. Furthermore, to simplify the feature alignment process, a semantic consistency inference mechanism is introduced to reveal the network's inherent bias toward specific modalities, thereby compressing cross-modal feature discrepancies from the foundational level. Extensive experiments on the FMB, MFNet, and PST900 datasets demonstrate that AMDANet achieves mIoU improvements of 3.6%, 3.0%, and 1.6%, respectively, significantly outperforming state-of-the-art methods.
Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer
Authors: Md Ashiqur Rahman; Chiao-An Yang; Michael N. Cheng; Lim Jun Hao; Jeremiah Jiang; Teck-Yian Lim; Raymond A. Yeh
Venue: ICCV 2025 (main)

Abstract: Scale variation is a fundamental challenge in computer vision. Objects of the same class can have different sizes, and their perceived size is further affected by the distance from the camera. These variations are local to the objects, i.e., different object sizes may change differently within the same image. To effectively handle scale variations, we present a deep equilibrium canonicalizer (DEC) to improve the local scale equivariance of a model. DEC can be easily incorporated into existing network architectures and can be adapted to a pre-trained model. Notably, we show that on the competitive ImageNet benchmark, DEC improves both model performance and local scale consistency across four popular pre-trained deep-nets, e.g., ViT, DeiT, Swin, and BEiT.
Auxiliary Prompt Tuning of Vision-Language Models for Few-Shot Out-of-Distribution Detection
Authors: Wenjun Miao; Guansong Pang; Zihan Wang; Jin Zheng; Xiao Bai
Venue: ICCV 2025 (main)

Abstract: Recent advancements in CLIP-based out-of-distribution (OOD) detection have shown promising results via regularization on prompt tuning, leveraging background features extracted from a few in-distribution (ID) samples as proxies for OOD features.However, these methods suffer from an inherent limitation: a lack of diversity in the extracted OOD features from the few-shot ID data.To address this issue, we propose to leverage external datasets as auxiliary outlier data (i.e., pseudo OOD samples) to extract rich, diverse OOD features, with the features from not only background regions but also foreground object regions, thereby supporting more discriminative prompt tuning for OOD detection. We further introduce Auxiliary Prompt Tuning (APT), a novel framework that can be used as a plug-in module to enable existing prompt tuning-based methods to utilize the auxiliary data for more accurate OOD detection.There are two key challenges of utilizing those auxiliary data in prompt tuning, including I) foreground-background decomposition of unlabeled auxiliary data with diverse outlying objects and II) optimization of foreground OOD features. APT tackles challenge I with an adaptive logit-based Kullback-Leibler divergence method and challenge II by constructing foreground-background pairs for each foreground region to enable effective exploitation of foreground OOD features. Extensive experiments on standard and hard OOD benchmarks show that APT achieves state-of-the-art performance, obtaining significant improvements in challenging scenarios, e.g., hard OOD and 1-shot detection.
TACO: Taming Diffusion for in-the-wild Video Amodal Completion
Authors: Ruijie Lu; Yixin Chen; Yu Liu; Jiaxiang Tang; Junfeng Ni; Diwen Wan; Gang Zeng; Siyuan Huang
Venue: ICCV 2025 (main)

Abstract: Humans can infer complete shapes and appearances of objects from limited visual cues, relying on extensive prior knowledge of the physical world. However, completing partially observable objects while ensuring consistency across video frames remains challenging for existing models, especially for unstructured, in-the-wild videos. This paper tackles the task of Video Amodal Completion (VAC), which aims to generate the complete object consistently throughout the video given a visual prompt specifying the object of interest. Leveraging the rich, consistent manifolds learned by pre-trained video diffusion models, we propose a conditional diffusion model, TACO, that repurposes these manifolds for VAC. To enable its effective and robust generalization to challenging in-the-wild scenarios, we curate a large-scale synthetic dataset with multiple difficulty levels by systematically imposing occlusions onto un-occluded videos. Building on this, we devise a progressive fine-tuning paradigm that starts with simpler recovery tasks and gradually advances to more complex ones. We demonstrate TACO's versatility on a wide range of in-the-wild videos from Internet, as well as on diverse, unseen datasets commonly used in autonomous driving, robotic manipulation, and scene understanding. Moreover, we show that TACO can be effectively applied to various downstream tasks like object reconstruction and pose estimation, highlighting its potential to facilitate physical world understanding and reasoning.
UniGS: Modeling Unitary 3D Gaussians for Novel View Synthesis from Sparse-view Images
Authors: Jiamin Wu; Kenkun Liu; Xiaoke Jiang; Yuan Yao; Lei Zhang
Venue: ICCV 2025 (main)

Abstract: In this work, we introduce UniGS, a novel 3D Gaussian reconstruction and novel view synthesis model that predicts a high-fidelity representation of 3D Gaussians from arbitrary number of posed sparse-view images.Previous methods often regress 3D Gaussians locally on a per-pixel basis for each view and then transfer them to world space and merge them through point concatenation.In contrast, Our approach involves modeling unitary 3D Gaussians in world space and updating them layer by layer.To leverage information from multi-view inputs for updating the unitary 3D Gaussians, we develop a DETR (DEtection TRansformer)-like framework, which treats 3D Gaussians as queries and updates their parameters by performing multi-view cross-attention (MVDFA) across multiple input images, which are treated as keys and values.This approach effectively avoids 'ghosting' issue and allocates more 3D Gaussians to complex regions.Moreover, since the number of 3D Gaussians used as decoder queries is independent of the number of input views, our method allows arbitrary number of multi-view images as input without causing memory explosion or requiring retraining.Extensive experiments validate the advantages of our approach, showcasing superior performance over existing methods quantitatively (improving PSNR by 4.2 dB when trained on Objaverse and tested on the GSO benchmark) and qualitatively. Codes are at https://github.com/jwubz123/UNIG.
ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting
Authors: Ruijie Zhu; Mulin Yu; Linning Xu; Lihan Jiang; Yixuan Li; Tianzhu Zhang; Jiangmiao Pang; Bo Dai
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian Splatting is renowned for its high-fidelity reconstructions and real-time novel view synthesis, yet its lack of semantic understanding limits object-level perception. In this work, we propose ObjectGS, an object-aware framework that unifies 3D scene reconstruction with semantic understanding. Instead of treating the scene as a unified whole, ObjectGS models individual objects as local anchors that generate neural Gaussians and share object IDs, enabling precise object-level reconstruction. During training, we dynamically grow or prune these anchors and optimize their features, while a one-hot ID encoding with a classification loss enforces clear semantic constraints. We show through extensive experiments that ObjectGS not only outperforms state-of-the-art methods on open-vocabulary and panoptic segmentation tasks, but also integrates seamlessly with applications like mesh extraction and scene editing.
SP2T: Sparse Proxy Attention for Dual-stream Point Transformer
Authors: Jiaxu Wan; Hong Zhang; Ziqi He; Yangyan Deng; Qishu Wang; Ding Yuan; Yifan Yang
Venue: ICCV 2025 (main)

Abstract: Point transformers have demonstrated remarkable progress in 3D understanding through expanded receptive fields (RF), but further expanding the RF leads to dilution in group attention and decreases detailed feature extraction capability. Proxy, which serves as abstract representations for simplifying feature maps, enables global RF. However, existing proxy-based approaches face critical limitations: Global proxies incur quadratic complexity for large-scale point clouds and suffer positional ambiguity, while local proxy alternatives struggle with 1) Unreliable sampling from the geometrically diverse point cloud, 2) Inefficient proxy interaction computation, and 3) Imbalanced local-global information fusion; To address these challenges, we propose Sparse Proxy Point Transformer (SP^ 2 T) -- a local proxy-based dual-stream point transformer with three key innovations: First, for reliable sampling, spatial-wise proxy sampling with vertex-based associations enables robust sampling on geometrically diverse point clouds. Second, for efficient proxy interaction, sparse proxy attention with a table-based relative bias effectively achieves the interaction with efficient map-reduce computation. Third, for local-global information fusion, our dual-stream architecture maintains local-global balance through parallel branches. Comprehensive experiments reveal that SP^ 2 T sets state-of-the-art results with acceptable latency on indoor and outdoor 3D comprehension benchmarks, demonstrating marked improvement (+3.8% mIoU vs. SPoTr@S3DIS, +22.9% mIoU vs. PointASNL@Sem.KITTI) compared to other proxy-based point cloud methods.
Scoring, Remember, and Reference: Catching Camouflaged Objects in Videos
Authors: Yu'ang Feng; Shuyong Gao; Fuzhen Yan; Yicheng Song; Lingyi Hong; Junjie Hu; Wenqiang Zhang
Venue: ICCV 2025 (main)

Abstract: Video Camouflaged Object Detection (VCOD) aims to segment objects whose appearances closely resemble their surroundings, posing a challenging and emerging task. Existing vision models often struggle in such scenarios due to the indistinguishable appearance of camouflaged objects and the insufficient exploitation of dynamic information in videos. To address these challenges, we propose an end-to-end VCOD framework inspired by human memory-recognition, which leverages historical video information by integrating memory reference frames for camouflaged sequence processing. Specifically, we design a dual-purpose decoder that simultaneously generates predicted masks and scores, enabling reference frame selection based on scores while introducing auxiliary supervision to enhance feature extraction. Furthermore, this study introduces a novel reference-guided multilevel asymmetric attention mechanism, effectively integrating long-term reference information with short-term motion cues for comprehensive feature extraction. By combining these modules, we develop the Scoring, Remember, and Reference (SRR) framework, which efficiently extracts information to locate targets and employs memory guidance to improve subsequent processing. With its optimized module design and effective utilization of video data, our model achieves significant performance improvements, surpassing existing approaches by approximately 10% on benchmark datasets while requiring fewer parameters (54M) and only a single pass through the video. The code will be made publicly available.
Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers
Authors: An-Lun Liu; Yu-Wei Chao; Yi-Ting Chen
Venue: ICCV 2025 (main)

Abstract: In this paper, we study task-oriented human grasp synthesis, a new grasp synthesis task that demands both task and context awareness. At the core of our method is the task-aware contact maps. Unlike traditional contact maps that only reason about the manipulated object and its relation with the hand, our enhanced maps take into account scene and task information. This comprehensive map is critical for hand-object interaction, enabling accurate grasping poses that align with the task. We propose a two-stage pipeline that first constructs a task-aware contact map informed by the scene and task. In the subsequent stage, we use this contact map to synthesize task-oriented human grasps. We introduce a new dataset and metric for the proposed task to evaluate our approach. Our experiments validate the importance of modeling both scene and task, demonstrating significant improvements over existing methods in both grasp quality and task performance.
3D Change Localization and Captioning From Dynamic Scans of Indoor Scenes
Authors: Yue Qiu; Shintaro Yamamoto; Ryosuke Yamada; Ryota Suzuki; Hirokatsu Kataoka; Kenji Iwata; Yutaka Satoh
Venue: WACV 2023 (main)

Abstract: Daily indoor scenes often involve constant changes due to human activities. To recognize scene changes, existing change captioning methods focus on describing changes from two images of a scene. However, to accurately perceive and appropriately evaluate physical changes and then identify the geometry of changed objects, recognizing and localizing changes in 3D space is crucial. Therefore, we propose a task to explicitly localize changes in 3D bounding boxes from two point clouds and describe detailed scene changes, including change types, object attributes, and spatial locations. Moreover, we create a simulated dataset with various scenes, allowing generating data without labor costs. We further propose a framework that allows different 3D object detectors to be incorporated in the change detection process, after which captions are generated based on the correlations of different change regions. The proposed framework achieves promising results in both change detection and captioning. Furthermore, we also evaluated on data collected from real scenes. The experiments show that pretraining on the proposed dataset increases the change detection accuracy by +12.8% (mAP0.25) when applied to real-world data. We believe that our proposed dataset and discussion could provide both a new benchmark and insights for future studies in scene change understanding.
Panoptic-Aware Image-to-Image Translation
Authors: Liyun Zhang; Photchara Ratsamee; Bowen Wang; Zhaojie Luo; Yuki Uranishi; Manabu Higashida; Haruo Takemura
Venue: WACV 2023 (main)

Abstract: Despite remarkable progress in image translation, the complex scene with multiple discrepant objects remains a challenging problem. The translated images have low fidelity and tiny objects in fewer details causing unsatisfactory performance in object recognition. Without thorough object perception (i.e., bounding boxes, categories, and masks) of images as prior knowledge, the style transformation of each object will be difficult to track in translation. We propose panoptic-aware generative adversarial networks (PanopticGAN) for image-to-image translation together with a compact panoptic segmentation dataset. The panoptic perception (i.e., foreground instances and background semantics of the image scene) is extracted to achieve alignment between object content codes of the input domain and panoptic-level style codes sampled from the target style space, then refined by a proposed feature masking module for sharping object boundaries. The image-level combination between content and sampled style codes is also merged for higher fidelity image generation. Our proposed method was systematically compared with different competing methods and obtained significant improvement in both image quality and object recognition performance.
Partially Calibrated Semi-Generalized Pose From Hybrid Point Correspondences
Authors: Snehal Bhayani; Torsten Sattler; Viktor Larsson; Janne Heikkilä; Zuzana Kukelova
Venue: WACV 2023 (main)

Abstract: In this paper we study the problem of estimating the semi-generalized pose of a partially calibrated camera, i.e., the pose of a perspective camera with unknown focal length w.r.t. a generalized camera, from a hybrid set of 2D-2D and 2D-3D point correspondences. We study all possible camera configurations within the generalized camera system. To derive practical solvers to previously unsolved challenging configurations, we test different parameterizations as well as different solving strategies based on the state-of-the-art methods for generating efficient polynomial solvers. We evaluate the three most promising solvers, i.e., the H51f solver with five 2D-2D correspondences and one 2D-3D correspondence viewed by the same camera inside generalized camera, the H32f solver with three 2D-2D and two 2D-3D correspondences, and the H13f solver with one 2D-2D and three 2D-3D correspondences, on synthetic and real data. We show that in the presence of noise in the 3D points these solvers provide better estimates than the corresponding absolute pose solvers.
Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation
Authors: Alper Kayabaşı; Gülin Tüfekci; İlkay Ulusoy
Venue: WACV 2023 (main)

Abstract: Few-shot segmentation aims to devise a generalizing model that segments query images from unseen classes during training with the guidance of a few support images whose class tally with the class of the query. There exist two domain-specific problems mentioned in the previous works, namely spatial inconsistency and bias towards seen classes. Taking the former problem into account, our method compares the support feature map with the query feature map at multi scales to become scale-agnostic. As a solution to the latter problem, a supervised model, called as base learner, is trained on available classes to accurately identify pixels belonging to seen classes. Hence, subsequent meta learner has a chance to discard areas belonging to seen classes with the help of an ensemble learning model that coordinates meta learner with the base learner. We simultaneously address these two vital problems for the first time and achieve state-of-the-art performances on both PASCAL-5i and COCO-20i datasets.