GenAI4ELab/papercli-papers huggingface dataset:
LaCoOT: Layer Collapse through Optimal Transport
Authors: Victor Quétu; Zhu Liao; Nour Hezbri; Fabio Pizzati; Enzo Tartaglione
Venue: ICCV 2025 (main)

Abstract: Although deep neural networks are well-known for their outstanding performance in tackling complex tasks, their hunger for computational resources remains a significant hurdle, posing energy-consumption issues and restricting their deployment on resource-constrained devices, preventing their widespread adoption. In this paper, we present an optimal transport-based method to reduce the depth of over-parametrized deep neural networks, alleviating their computational burden. More specifically, we propose a new regularization strategy based on the Max-Sliced Wasserstein distance to minimize the distance between the intermediate feature distributions in the neural network. We show that minimizing this distance enables the complete removal of intermediate layers in the network, achieving better performance/depth trade-off compared to existing techniques. We assess the effectiveness of our method on traditional image classification setups and extend it to generative image models. Our code is available at https://github.com/VGCQ/LaCoOT.
Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction
Authors: Weirong Chen; Ganlin Zhang; Felix Wimbauer; Rui Wang; Nikita Araslanov; Andrea Vedaldi; Daniel Cremers
Venue: ICCV 2025 (main)

Abstract: Traditional SLAM systems, which rely on bundle adjustment, struggle with the highly dynamic scenes commonly found in casual videos. Such videos entangle the motion of dynamic elements, undermining the assumption of static environments required by traditional systems. Existing techniques either filter out dynamic elements or model their motion independently. However, the former often results in incomplete reconstructions, while the latter can lead to inconsistent motion estimates. Taking a novel approach, this work leverages a 3D point tracker to separate camera-induced motion from the observed motion of dynamic objects. By considering only the camera-induced component, bundle adjustment can operate reliably on all scene elements. We further ensure depth consistency across video frames with lightweight post-processing based on scale maps. Our framework combines the core of traditional SLAM---bundle adjustment---with a robust learning-based 3D tracker. Integrating motion decomposition, bundle adjustment, and depth refinement, our unified framework, BA-Track, accurately tracks camera motion and produces temporally coherent and scale-consistent dense reconstructions, accommodating both static and dynamic elements. Our experiments on challenging datasets reveal significant improvements in camera pose estimation and 3D reconstruction accuracy.
ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations
Authors: Tianming Liang; Kun-Yu Lin; Chaolei Tan; Jianguo Zhang; Wei-Shi Zheng; Jian-Fang Hu
Venue: ICCV 2025 (main)

Abstract: Referring video object segmentation (RVOS) aims to segment target objects throughout a video based on a text description. This is challenging as it involves deep vision-language understanding, pixel-level dense prediction and spatiotemporal reasoning. Despite notable progress in recent years, existing methods still exhibit a noticeable gap when considering all these aspects. In this work, we propose ReferDINO, a strong RVOS model that inherits region-level vision-language alignment from foundational visual grounding models, and is further endowed with pixel-level dense perception and cross-modal spatiotemporal reasoning. In detail, ReferDINO integrates two key components: 1) a grounding-guided deformable mask decoder that utilizes location prediction to progressively guide mask prediction through differentiable deformation mechanisms; 2) an object-consistent temporal enhancer that injects pretrained time-varying text features into inter-frame interaction to capture object-aware dynamic changes. Moreover, a confidence-aware query pruning strategy is designed to accelerate object decoding without compromising model performance. Extensive experimental results on five benchmarks demonstrate that our ReferDINO significantly outperforms previous methods (e.g., +3.9% (\mathcal J &\mathcal F ) on Ref-YouTube-VOS) with real-time inference speed (51 FPS).
LA-MOTR: End-to-End Multi-Object Tracking by Learnable Association
Authors: Peng Wang; Yongcai Wang; Hualong Cao; Wang Chen; Deying Li
Venue: ICCV 2025 (main)

Abstract: This paper proposes LA-MOTR, a novel Tracking-by-Learnable-Association framework that resolves the competing optimization objectives between detection and association in end-to-end Tracking-by-Attention (TbA) Multi-Object Tracking. Current TbA methods employ shared decoders for simultaneous object detection and tracklet association, often resulting in task interference and suboptimal accuracy. By contrast, our end-to-end framework decouples these tasks into two specialized modules: Separated Object-Tracklet Detection (SOTD) and Spatial-Guided Learnable Association (SGLA). This decoupled design offers flexibility and explainability. In particular, SOTD independently detects new objects and existing tracklets in each frame, while SGLA associates them via Spatial-Weighted Learnable Attention module guided by relative spatial cues. Temporal coherence is further maintained through Tracklet Updates Module. The learnable association mechanism resolves the inherent suboptimal association issues in decoupled frameworks, avoiding the task interference commonly observed in joint approaches. Evaluations on DanceTrack, MOT17, and SportMOT datasets demonstrate state-of-the-art performance. Extensive ablation studies validate the effectiveness of the designed modules. Code is available at https://github.com/PenK1nG/LA-MOTR.
NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenes
Authors: Han-Hung Lee; Qinghong Han; Angel X. Chang
Venue: ICCV 2025 (main)

Abstract: In this paper, we explore the task of generating expansive outdoor scenes, ranging from castles to high-rises. Unlike indoor scene generation, which has been a primary focus of prior work, outdoor scene generation presents unique challenges, including wide variations in scene heights and the need for a method capable of rapidly producing large landscapes. To address this, we propose an efficient approach that encodes scene chunks as uniform vector sets, offering better compression and performance than the spatially structured latents used in prior methods. Furthermore, we train an explicit outpainting model for unbounded generation, which improves coherence compared to prior resampling-based inpainting schemes while also speeding up generation by eliminating extra diffusion steps. To facilitate this task, we curate NuiScene43, a small but high-quality set of scenes, preprocessed for joint training. Notably, when trained on scenes of varying styles, our model can blend different environments, such as rural houses and city skyscrapers, within the same scene, highlighting the potential of our curation process to leverage heterogeneous scenes for joint training.
NegRefine: Refining Negative Label-Based Zero-Shot OOD Detection
Authors: Amirhossein Ansari; Ke Wang; Pulei Xiong
Venue: ICCV 2025 (main)

Abstract: Recent advancements in Vision-Language Models like CLIP have enabled zero-shot OOD detection by leveraging both image and textual label information. Among these, negative label-based methods such as NegLabel and CSP have shown promising results by utilizing a lexicon of words to define negative labels for distinguishing OOD samples. However, these methods suffer from detecting in-distribution samples as OOD due to negative labels that are subcategories of in-distribution labels or proper nouns. They also face limitations in handling images that match multiple in-distribution and negative labels. We propose NegRefine, a novel negative label refinement framework for zero-shot OOD detection. By introducing a filtering mechanism to exclude subcategory labels and proper nouns from the negative label set and incorporating a multi-matching-aware scoring function that dynamically adjusts the contributions of multiple labels matching an image, NegRefine ensures a more robust separation between in-distribution and OOD samples. We evaluate NegRefine on large-scale benchmarks, including ImageNet-1K. The code is available at https://github.com/ah-ansari/NegRefine.
DM-EFS: Dynamically Multiplexed Expanded Features Set Form for Robust and Efficient Small Object Detection
Authors: Aashish Sharma
Venue: ICCV 2025 (main)

Abstract: In this paper, we address the problem of small object detection (SOD) by introducing our novel approach - Dynamically Multiplexed Expanded Features Set (DM-EFS) form. Detecting small objects is challenging as they usually suffer from inadequate feature representation. Hence, to address this, we propose the Expanded Features Set (EFS) form - a simple yet effective idea to improve the feature representation of small objects by utilizing the untapped higher resolution features from the shallower layers of the backbone module. We observe that the EFS form improves the SOD performance. However, due to processing of additional features, it has a higher computational cost which reduces inference efficiency. Hence, to address this, we propose Dynamic Feature Multiplexing (DFM) - a novel design that optimizes the usage of the EFS form during inference by dynamically multiplexing it to create our aforementioned DM-EFS form. Since our DM-EFS form is a multiplexed (or subsampled) optimal version of the EFS form, it improves the SOD performance like the EFS form but with a lower computational cost. Extensive experiments confirm the efficacy of our DM-EFS approach. Integrated with YOLOv7 base model, our DM-EFS achieves state-of-the art results on diverse SOD datasets outperforming the base model and SOD baselines, with on-par or even better inference efficiency.
OmniVTON: Training-Free Universal Virtual Try-On
Authors: Zhaotong Yang; Yuhui Li; Shengfeng He; Xinzhe Li; Yangyang Xu; Junyu Dong; Yong Du
Venue: ICCV 2025 (main)

Abstract: Image-based Virtual Try-On (VTON) techniques rely on either supervised in-shop approaches, which ensure high fidelity but struggle with cross-domain generalization, or unsupervised in-the-wild methods, which improve adaptability but remain constrained by data biases and limited universality. A unified, training-free solution that works across both scenarios remains an open challenge. We propose OmniVTON, the first training-free universal VTON framework that decouples garment and pose conditioning to achieve both texture fidelity and pose consistency across diverse settings. To preserve garment details, we introduce a garment prior generation mechanism that aligns clothing with the body, followed by continuous boundary stitching technique to achieve fine-grained texture retention. For precise pose alignment, we utilize DDIM inversion to capture structural cues while suppressing texture interference, ensuring accurate body alignment independent of the original image textures. By disentangling garment and pose constraints, OmniVTON eliminates the bias inherent in diffusion models when handling multiple conditions simultaneously. Experimental results demonstrate that OmniVTON achieves superior performance across diverse datasets, garment types, and application scenarios. Notably, it is the first framework capable of multi-human VTON, enabling realistic garment transfer across multiple individuals in a single scene. Code is available at https://github.com/Jerome-Young/OmniVTON.
ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation
Authors: Qizhen Lan; Qing Tian
Venue: ICCV 2025 (main)

Abstract: Dense visual prediction tasks, such as detection and segmentation, are crucial for time-critical applications (e.g., autonomous driving and video surveillance). While deep models achieve strong performance, their efficiency remains a challenge. Knowledge distillation (KD) is an effective model compression technique, but existing feature-based KD methods rely on static, teacher-driven feature selection, failing to adapt to the student's evolving learning state or leverage dynamic student-teacher interactions. To address these limitations, we propose Adaptive student-teacher Cooperative Attention Masking for Knowledge Distillation (ACAM-KD), which introduces two key components: (1) Student-Teacher Cross-Attention Feature Fusion (STCA-FF), which adaptively integrates features from both models for a more interactive distillation process, and (2) Adaptive Spatial-Channel Masking (ASCM), which dynamically generates importance masks to enhance both spatial and channel-wise feature selection. Unlike conventional KD methods, ACAM-KD adapts to the student's evolving needs throughout the entire distillation process. Extensive experiments on multiple benchmarks validate its effectiveness. For instance, on COCO2017, ACAM-KD improves object detection performance by up to 1.4 mAP over the state-of-the-art when distilling a ResNet-50 student from a ResNet-101 teacher. For semantic segmentation on Cityscapes, it boosts mIoU by 3.09 over the baseline with DeepLabV3-MobileNetV2 as the student model.
InfGen: A Resolution-Agnostic Paradigm for Scalable Image Synthesis
Authors: Tao Han; Wanghan Xu; Junchao Gong; Xiaoyu Yue; Song Guo; Luping Zhou; Lei Bai
Venue: ICCV 2025 (main)

Abstract: Arbitrary resolution image generation provides a consistent visual experience across devices, having extensive applications for producers and consumers. Current diffusion models increase computational demand quadratically with resolution, causing 4K image generation delays over 100 seconds. To solve this, we explore the second generation upon the latent diffusion models, where the fixed latent generated by diffusion models is regarded as the content representation and we propose to decode arbitrary resolution images with a compact generated latent using a one-step generator. Thus, we present the InfGen, replacing the VAE decoder with the new generator, for generating images at any resolution from a fixed-size latent without retraining the diffusion models, which simplifies the process, reducing computational complexity and can be applied to any model using the same latent space. Experiments show InfGen is capable of improving many models into the arbitrary high-resolution era while cutting 4K image generation time to under 10 seconds.
PlanGen: Towards Unified Layout Planning and Image Generation in Auto-Regressive Vision Language Models
Authors: Runze He; Bo Cheng; Yuhang Ma; Qingxiang Jia; Shanyuan Liu; Ao Ma; Xiaoyu Wu; Liebucha Wu; Dawei Leng; Yuhui Yin
Venue: ICCV 2025 (main)

Abstract: In this paper, we propose a unified layout planning and image generation model, PlanGen, which can pre-plan spatial layout conditions before generating images as shown in Figure 1. Unlike previous diffusion-based models that treat layout planning and layout-to-image as two separate models, PlanGen jointly models the two tasks into one autoregressive transformer using only next-token prediction. PlanGen integrates layout conditions into the model as context without requiring specialized encoding of local captions and bounding box coordinates, which provides significant advantages over the previous embed-and-pool operations on layout conditions, particularly when dealing with complex layouts. Unified prompting allows PlanGen to perform multitasking training related to layout, including layout planning, layout-to-image generation, image layout understanding, etc. In addition, PlanGen can be seamlessly expanded to layout-guided image manipulation thanks to the well-designed modeling, with teacher-forcing content manipulation policy and negative layout guidance. Extensive experiments verify the effectiveness of our PlanGen in multiple layout-related tasks, showing its great potential.
AirCache: Activating Inter-modal Relevancy KV Cache Compression for Efficient Large Vision-Language Model Inference
Authors: Kai Huang; Hao Zou; Bochen Wang; Ye Xi; Zhen Xie; Hao Wang
Venue: ICCV 2025 (main)

Abstract: Recent advancements in Large Visual Language Models (LVLMs) have gained significant attention due to their remarkable reasoning capabilities and proficiency in generalization. However, processing a large number of visual tokens and generating long-context outputs impose substantial computational overhead, leading to excessive demands for key-value (KV) cache. To address this critical bottleneck, we propose AirCache, a novel KV cache compression method aimed at accelerating LVLMs inference. This work systematically investigates the correlations between visual and textual tokens within the attention mechanisms of LVLMs. Our empirical analysis reveals considerable redundancy in cached visual tokens, wherein strategically eliminating these tokens preserves model performance while significantly accelerating context generation. We introduce an elite observation window for assessing the importance of visual components in the KV cache, focusing on stable inter-modal relevancy modeling with enhanced multi-perspective consistency. Additionally, we develop an adaptive layer-wise budget allocation strategy that capitalizes on the strength and skewness of token importance distribution, showcasing superior efficiency compared to uniform allocation. Comprehensive evaluations across multiple LVLMs and benchmarks demonstrate that our method achieves comparable performance to the full cache while retaining only 10% of visual KV cache, thereby reducing decoding latency by 29% to 66% across various batch size and prompt length of inputs. Notably, as cache retention rates decrease, our method exhibits increasing performance advantages over existing approaches.
Completing 3D Partial Assemblies with View-Consistent 2D-3D Correspondence
Authors: Weihao Wang; Yu Lan; Mingyu You; Bin He
Venue: ICCV 2025 (main)

Abstract: 3D assembly completion represents a fundamental task in 3D computer vision and robotics. This task aims to retrieve the missing parts from a set of candidates and predict their 6-DoF poses to make the partial assembly complete. However, due to the inherent uncertainty in completion and the similarity among candidates, even humans struggle to achieve precise completion without external guidance. To address this challenge, we introduce an auxiliary image depicting the complete assembly from a specific view. The primary challenge lies in the lack of correspondence or grounding between the partial assembly and the image, leading to ambiguities in identifying missing parts and ineffective guidance for completion. Moreover, this correspondence heavily depends on the view of image, which, unfortunately, is often unknown in real-world scenarios. To this end, we propose a novel cross-modal 3D assembly completion framework. At its core is missing-oriented feature fusion augmented by self-supervised view alignment to establish view-consistent 2D-3D correspondence between the image and the partial assembly, which effectively captures clues of missing parts from the image and provides targeted guidance for completion. Extensive experiments demonstrate our state-of-the-art performance on the PartNet dataset and show its generalization capabilities in two downstream applications: component suggestion and furniture restoration.
Latte: Collaborative Test-Time Adaptation of Vision-Language Models in Federated Learning
Authors: Wenxuan Bao; Ruxi Deng; Ruizhong Qiu; Tianxin Wei; Hanghang Tong; Jingrui He
Venue: ICCV 2025 (main)

Abstract: Test-time adaptation with pre-trained vision-language models has gained increasing attention for addressing distribution shifts during testing. Among these approaches, memory-based algorithms stand out due to their training-free nature and ability to leverage historical test data. However, existing test-time adaptation methods are typically designed for a single domain with abundant data. In decentralized settings such as federated learning, applying these methods individually to each client suffers from limited test data, while directly sharing a single global memory via the server prevents proper personalization to each client's unique distribution. To address this, we propose Latte, a novel framework where each client maintains a local memory to store embeddings from its own historical test data and an external memory to store class prototypes from other relevant clients. During communication, each client retrieves prototypes from similar clients under the server's coordination to expand its memory. For local adaptation, Latte utilizes both embedding similarity and uncertainty to enhance model performance. Our theoretical analysis shows that Latte effectively leverages in-distribution clients while remaining robust to out-of-distribution clients. Extensive experiments on domain adaptation and corruption benchmarks validate that Latte achieves superior performance in decentralized settings, while introducing only negligible communication and computation costs. Our code is available at https://github.com/baowenxuan/Latte.
SSVQ: Unleashing the Potential of Vector Quantization with Sign-Splitting
Authors: Shuaiting Li; Juncan Deng; Chengxuan Wang; Kedong Xu; Rongtao Deng; Hong Gu; Haibin Shen; Kejie Huang
Venue: ICCV 2025 (main)

Abstract: Vector Quantization (VQ) has emerged as a prominent weight compression technique, showcasing substantially lower quantization errors than uniform quantization across diverse models, particularly in extreme compression scenarios. However, its efficacy during fine-tuning is limited by the constraint of the compression format, where weight vectors assigned to the same codeword are restricted to updates in the same direction. Consequently, many quantized weights are compelled to move in directions contrary to their local gradient information. To mitigate this issue, we introduce a novel VQ paradigm, Sign-Splitting VQ (SSVQ), which decouples the sign bit of weights from the codebook. Our approach involves extracting the sign bits of uncompressed weights and performing clustering and compression on all-positive weights. We then introduce latent variables for the sign bit and jointly optimize both the signs and the codebook. Additionally, we implement a progressive freezing strategy for the learnable sign to ensure training stability. Extensive experiments on various modern models and tasks demonstrate that SSVQ achieves a significantly superior compression-accuracy trade-off compared to conventional VQ. Furthermore, we validate our algorithm on a hardware accelerator, showing that SSVQ achieves a 3x speedup over the 8-bit compressed model by reducing memory access. Our code is available at https://github.com/list0830/SSVQ.
From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations
Authors: Anthony Bisulco; Rahul Ramesh; Randall Balestriero; Pratik Chaudhari
Venue: ICCV 2025 (main)

Abstract: Masked Autoencoders (MAEs) have emerged as a powerful pretraining technique for vision foundation models. Despite their effectiveness, they require extensive hyperparameter tuning (masking ratio, patch size, encoder/decoder layers) when applied to novel datasets. While prior theoretical works have analyzed MAEs in terms of their attention patterns and hierarchical latent variable models, the connection between MAE hyperparameters and performance on downstream tasks is relatively unexplored. This work investigates how MAEs learn spatial correlations in the input image. We analytically derive the features learned by a linear MAE and show that masking ratio and patch size can be used to select for features that capture short- and long-range spatial correlations. We extend this analysis to non-linear MAEs to show that MAE representations adapt to spatial correlations in the dataset, beyond second-order statistics. Finally, we discuss some insights on how to select MAE hyper-parameters in practice.