GenAI4ELab/papercli-papers huggingface dataset:
CRAM: Large Scale Video Continual Learning with Bootstrapped Compression
Authors: Shivani Mall; Joao F. Henriques
Venue: ICCV 2025 (main)

Abstract: Continual learning (CL) promises to allow neural networks to learn from continuous streams of inputs, instead of IID (independent and identically distributed) sampling, which requires random access to a full dataset. This would allow for much smaller storage requirements and self-sufficiency of deployed systems that cope with natural distribution shifts, similarly to biological learning.We focus on video CL employing a rehearsal-based approach, which reinforces past samples from a memory buffer. We posit that part of the reason why practical video CL is challenging is the high memory requirements of video, further exacerbated by long-videos and continual streams, which are at odds with the common rehearsal-buffer size constraints. To address this, we propose to use compressed vision, i.e. store video codes (embeddings) instead of raw inputs, and train a video classifier by IID sampling from this rolling buffer. Training a video compressor online (so not depending on any pre-trained networks) means that it is also subject to catastrophic forgetting. We propose a scheme to deal with this forgetting by refreshing video codes, which requires careful decompression with a previous version of the network and recompression with a new one. We name our method Continually Refreshed Amodal Memory (CRAM). We expand current video CL benchmarks to large-scale settings, namely EpicKitchens-100 and Kinetics-700, with thousands of relatively long videos, and demonstrate empirically that our video CL method outperforms prior art with a significantly reduced memory footprint.
DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understanding
Authors: Thomas Kreutz; Max Mühlhäuser; Alejandro Sanchez Guinea
Venue: ICCV 2025 (main)

Abstract: Despite LiDAR (Light Detection and Ranging) being an effective privacy-preserving alternative to RGB cameras to perceive human activities, it remains largely underexplored in the context of multi-modal contrastive pre-training for human activity understanding tasks, such as human activity recognition (HAR), retrieval, or person re-identification (RE-ID). To close this gap, our work explores learning the correspondence between LiDAR point clouds, human skeleton poses, IMU data, and text in a joint embedding space. More specifically, we present DeSPITE, a \underline De ep \underline S keleton-\underline P ointcloud-\underline I MU-\underline T ext \underline E mbedding model, which effectively learns a joint embedding space across these four modalities. At the heart of our empirical exploration, we have combined the existing LIPD and Babel datasets, which enabled us to synchronize data of all four modalities, allowing us to explore the learning of a new joint embedding space. Our experiments demonstrate novel human activity understanding tasks for point cloud sequences enabled through DeSPITE, including Skeleton\leftrightarrowPointcloud\leftrightarrowIMU matching, retrieval, and temporal moment retrieval. Furthermore, we show that DeSPITE is an effective pre-training strategy for point cloud HAR through experiments in MSR-Action3D and HMPEAR. Code and models are publicly available at https://github.com/thkreutz/despite.
Leveraging Panoptic Scene Graph for Evaluating Fine-Grained Text-to-Image Generation
Authors: Xueqing Deng; Linjie Yang; Qihang Yu; Chenglin Yang; Liang-Chieh Chen
Venue: ICCV 2025 (main)

Abstract: Text-to-image (T2I) models have advanced rapidly with diffusion-based breakthroughs, yet their evaluation remains challenging. Human assessments are costly, and existing automated metrics lack accurate compositional understanding. To address these limitations, we introduce PSG-Bench, a novel benchmark featuring 5K text prompts designed to evaluate the capabilities of advanced T2I models. Additionally, we propose PSGEval, a scene graph-based evaluation metric that converts generated images into structured representations and applies graph matching techniques for accurate and scalable assessment. PSGEval is a detection based evaluation metric without relying on QA generations. Our experimental results demonstrate that PSGEval aligns well with human evaluations, mitigating biases present in existing automated metrics. We further provide a detailed ranking and analysis of recent T2I models, offering a robust framework for future research in T2I evaluation.
Noise2Score3D: Tweedie's Approach for Unsupervised Point Cloud Denoising
Authors: Xiangbin Wei; Yuanfeng Wang; Ao Xu; Lingyu Zhu; Dongyong Sun; Keren Li; Yang Li; Qi Qin
Venue: ICCV 2025 (main)

Abstract: Building on recent advances in Bayesian statistics and image denoising, we propose Noise2Score3D, a fully unsupervised framework for point cloud denoising. Noise2Score3D learns the score function of the underlying point cloud distribution directly from noisy data, eliminating the need for clean data during training. Using Tweedie's formula, our method performs denoising in a single step, avoiding the iterative processes used in existing unsupervised methods, thus improving both accuracy and efficiency. Additionally, we introduce Total Variation for Point Clouds as a denoising quality metric, which allows for the estimation of unknown noise parameters. Experimental results demonstrate that Noise2Score3D achieves state-of-the-art performance on standard benchmarks among unsupervised learning methods in Chamfer distance and point-to-mesh metrics. Noise2Score3D also demonstrates strong generalization ability beyond training datasets. Our method, by addressing the generalization issue and challenge of the absence of clean data in learning-based methods, paves the way for learning-based point cloud denoising methods in real-world applications.
I2-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
Authors: Zhimin Liao; Ping Wei; Ruijie Zhang; Shuaijia Chen; Haoxuan Wang; Ziyang Ren
Venue: ICCV 2025 (main)

Abstract: Forecasting the evolution of 3D scenes and generating unseen scenarios through occupancy-based world models offers substantial potential to enhance the safety of autonomous driving systems. While tokenization has revolutionized image and video generation, efficiently tokenizing complex 3D scenes remains a critical challenge for 3D world models. To address this, we propose I^ 2 -World, an efficient framework for 4D occupancy forecasting. Our method decouples scene tokenization into intra-scene and inter-scene tokenizers. The intra-scene tokenizer employs a multi-scale residual quantization strategy to hierarchically compress 3D scenes while preserving spatial details. The inter-scene tokenizer residually aggregates temporal dependencies across timesteps. This dual design retains the compactness of 3D tokenizers while capturing the dynamic expressiveness of 4D approaches. Unlike decoder-only GPT-style autoregressive models, I^ 2 -World adopts an encoder-decoder architecture. The encoder aggregates spatial context from the current scene and predicts a transformation matrix to guide future scene generation. The decoder, conditioned on this matrix and historical tokens, ensures temporal consistency during generation. Experiments demonstrate that I^ 2 -World achieves state-of-the-art performance, surpassing existing approaches by 41.8% in 4D occupancy forecasting with exceptional efficiency--requiring only 2.9 GB of training memory and achieving real-time inference at 94.8 FPS.
Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts
Authors: Hongcheng Gao; Tianyu Pang; Chao Du; Taihang Hu; Zhijie Deng; Min Lin
Venue: ICCV 2025 (main)

Abstract: With the rapid progress of diffusion models (DMs), significant efforts are being made to unlearn harmful or copyrighted concepts from pretrained DMs to prevent potential model misuse. However, it is observed that even when DMs are properly unlearned before release, malicious finetuning can compromise this process, causing DMs to relearn the unlearned concepts. This occurs partly because certain benign concepts (e.g., "skin") retained in DMs are related to the unlearned ones (e.g., "nudity"), facilitating their relearning via finetuning. To address this, we propose meta-unlearning on DMs. Intuitively, a meta-unlearned DM should behave like an unlearned DM when used as is; moreover, if the meta-unlearned DM undergoes malicious finetuning on unlearned concepts, the related benign concepts retained within it will be triggered to self-destruct, hindering the relearning of unlearned concepts. Our meta-unlearning framework is compatible with most existing unlearning methods, requiring only the addition of an easy-to-implement meta objective. We validate our approach through empirical experiments on meta-unlearning concepts from Stable Diffusion models (SD-v1-4 and SDXL), supported by extensive ablation studies.
Auto-Vocabulary Semantic Segmentation
Authors: Osman Ülger; Maksymilian Kulicki; Yuki Asano; Martin R. Oswald
Venue: ICCV 2025 (main)

Abstract: Open-Vocabulary Segmentation (OVS) methods are capable of performing semantic segmentation without relying on a fixed vocabulary, and in some cases, without training or fine-tuning. However, OVS methods typically require a human in the loop to specify the vocabulary based on the task or dataset at hand. In this paper, we introduce Auto-Vocabulary Semantic Segmentation (AVS), advancing open-ended image understanding by eliminating the necessity to predefine object categories for segmentation. Our approach, AutoSeg, presents a framework that autonomously identifies relevant class names using semantically enhanced BLIP embeddings and segments them afterwards. Given that open-ended object category predictions cannot be directly compared with a fixed ground truth, we develop a Large Language Model-based Auto-Vocabulary Evaluator (LAVE) to efficiently evaluate the automatically generated classes and their corresponding segments. With AVS, our method sets new benchmarks on datasets PASCAL VOC, Context, ADE20K, and Cityscapes, while showing competitive performance to OVS methods that require specified class names. All code is released at https://github.com/ozzyou/AutoSeg.
SpinMeRound: Consistent Multi-View Identity Generation Using Diffusion Models
Authors: Stathis Galanakis; Alexandros Lattas; Stylianos Moschoglou; Bernhard Kainz; Stefanos Zafeiriou
Venue: ICCV 2025 (main)

Abstract: Despite recent progress in diffusion models, generating realistic head portraits from novel viewpoints remains a significant challenge in computer vision. Most current approaches are constrained to limited angular ranges, predominantly focusing on frontal or near-frontal views. Moreover, although the recent emerging large-scale diffusion models have been proven robust in handling 3D scenes, they underperform on facial data, given their complex structure and the uncanny valley pitfalls. In this paper, we propose SpinMeRound, a diffusion-based approach designed to generate consistent and accurate head portraits from novel viewpoints. By leveraging a number of input views alongside an identity embedding, our method effectively synthesizes diverse viewpoints of a subject whilst robustly maintaining its unique identity features. Through experimentation, we showcase our model's generation capabilities in full head synthesis, while beating current state-of-the-art multi-view diffusion models.
MSA2: Multi-task Framework with Structure-aware and Style-adaptive Character Representation for Open-set Chinese Text Recognition
Authors: Yangfu Li; Hongjian Zhan; Qi Liu; Li Sun; Yu-Jie Xiong; Yue Lu
Venue: ICCV 2025 (main)

Abstract: Most existing methods regard open-set Chinese text recognition (CTR) as a single-task problem, primarily focusing on prototype learning of linguistic components or glyphs to identify unseen characters. In contrast, humans identify characters by integrating multiple perspectives, including linguistic and visual cues. Inspired by this, we propose a multi-task framework termed MSA^2, which considers multi-view character representations for open-set CTR. Within MSA^2, we introduce two novel strategies for character representation: structure-aware component encoding (SACE) and style-adaptive glyph embedding (SAGE). SACE utilizes a binary tree with dynamic representation space to emphasize the primary linguistic components, thereby generating structure-aware and discriminative linguistic representations for each character. Meanwhile, SAGE employs a glyph-centric contrastive learning to aggregate features from diverse forms, yielding robust glyph representations for the CTR model to adapt to the style variations among various fonts. Extensive experiments demonstrate that our proposed MSA^2 outperforms state-of-the-art CTR methods, achieving an average improvement of 1.3% and 6.0% in accuracy under closed-set and open-set settings on the BCTR dataset, respectively. The code will be available soon.
IGL-Nav: Incremental 3D Gaussian Localization for Image-goal Navigation
Authors: Wenxuan Guo; Xiuwei Xu; Hang Yin; Ziwei Wang; Jianjiang Feng; Jie Zhou; Jiwen Lu
Venue: ICCV 2025 (main)

Abstract: Visual navigation with an image as goal is a fundamental and challenging problem. Conventional methods either rely on end-to-end RL learning or modular-based policy with topological graph or BEV map as memory, which cannot fully model the geometric relationship between the explored 3D environment and the goal image. In order to efficiently and accurately localize the goal image in 3D space, we build our navigation system upon the renderable 3D gaussian (3DGS) representation. However, due to the computational intensity of 3DGS optimization and the large search space of 6-DoF camera pose, directly leveraging 3DGS for image localization during agent exploration process is prohibitively inefficient. To this end, we propose IGL-Nav, an Incremental 3D Gaussian Localization framework for efficient and 3D-aware image-goal navigation. Specifically, we incrementally update the scene representation as new images arrive with feed-forward monocular prediction. Then we coarsely localize the goal by leveraging the geometric information for discrete space matching, which can be equivalent to efficient 3D convolution. When the agent is close to the goal, we finally solve the fine target pose with optimization via differentiable rendering. The proposed IGL-Nav outperforms existing state-of-the-art methods by a large margin across diverse experimental configurations. It can also handle the more challenging free-view image-goal setting and be deployed on real-world robotic platform using a cellphone to capture goal image at arbitrary pose. Project page: https://gwxuan.github.io/IGL-Nav/.
PCR-GS: COLMAP-Free 3D Gaussian Splatting via Pose Co-Regularizations
Authors: Yu Wei; Jiahui Zhang; Xiaoqin Zhang; Ling Shao; Shijian Lu
Venue: ICCV 2025 (main)

Abstract: COLMAP-free 3D Gaussian Splatting (3D-GS) has recently attracted increasing attention due to its remarkable performance in reconstructing high-quality 3D scenes from unposed images or videos. However, it often struggles to handle scenes with complex camera trajectories as featured by drastic rotation and translation across adjacent camera views, leading to degraded estimation of camera poses and further local minima in joint optimization of camera poses and 3D-GS. We propose PCR-GS, an innovative COLMAP-free 3DGS technique that achieves superior 3D scene modeling and camera pose estimation via camera pose co-regularization. PCR-GS achieves regularization from two perspectives. The first is feature reprojection regularization which extracts view-robust DINO features from adjacent camera views and aligns their semantic information for camera pose regularization. The second is wavelet-based frequency regularization which exploits discrepancy in high-frequency details to further optimize the rotation matrix in camera poses. Extensive experiments over multiple real-world scenes show that the proposed PCR-GS achieves superior pose-free 3D-GS scene modeling under dramatic changes of camera trajectories.
EA-KD: Entropy-based Adaptive Knowledge Distillation
Authors: Chi-Ping Su; Ching-Hsun Tseng; Bin Pu; Lei Zhao; Jiewen Yang; Zhuangzhuang Chen; Shin-Jye Lee
Venue: ICCV 2025 (main)

Abstract: Knowledge distillation (KD) enables a smaller "student" model to mimic a larger "teacher" model by transferring knowledge from the teacher's output or features. However, most KD methods treat all samples uniformly, overlooking the varying learning value of each sample and thereby limiting effectiveness. In this paper, we propose Entropy-based Adaptive Knowledge Distillation (EA-KD), a simple yet effective plug-and-play KD method that prioritizes learning from valuable samples. EA-KD quantifies each sample's learning value by strategically combining the entropy of the teacher and student output, then dynamically reweights the distillation loss to place greater emphasis on high-entropy samples. Extensive experiments across diverse KD frameworks and tasks--including image classification, object detection, and large language model (LLM) distillation--demonstrate that EA-KD consistently enhances performance, achieving state-of-the-art results with negligible computational cost. Our code is available at: https://github.com/cpsu00/EA-KD
D3: Training-Free AI-Generated Video Detection Using Second-Order Features
Authors: Chende Zheng; Ruiqi Suo; Chenhao Lin; Zhengyu Zhao; Le Yang; Shuai Liu; Minghui Yang; Cong Wang; Chao Shen
Venue: ICCV 2025 (main)

Abstract: The evolution of video generation techniques, such as Sora, has made it increasingly easy to produce high-fidelity AI-generated videos, raising public concern over the dissemination of synthetic content. However, existing detection methodologies remain limited by their insufficient exploration of temporal artifacts in synthetic videos. To bridge this gap, we establish a theoretical framework through second-order dynamical analysis under Newtonian mechanics, subsequently extending the Second-order Central Difference features tailored for temporal artifact detection. Building on this theoretical foundation, we reveal a fundamental divergence in second-order feature distributions between real and AI-generated videos. Concretely, we propose Detection by Difference of Differences (D3), a novel training-free detection method that leverages the above second-order temporal discrepancies. We validate the superiority of our D3 on 4 open-source datasets (Gen-Video, VideoPhy, EvalCrafter, VidProM), 40 subsets in total. For example, on GenVideo, D3 outperforms the previous best method by 10.39% (absolute) mean Average Precision. Additional experiments on time cost and post-processing operations demonstrate D3's exceptional computational efficiency and strong robust performance. Our code is available at https://github.com/Zig-HS/D3.
Background Invariance Testing According to Semantic Proximity
Authors: Zukang Liao; Min Chen
Venue: ICCV 2025 (main)

Abstract: In many applications, machine-learned (ML) models are required to hold some invariance qualities, such as rotation, size, and intensity invariance. Among these, testing for background invariance presents a significant challenge due to the vast and complex data space it encompasses. To evaluate invariance qualities, we first use a visualization-based testing framework which allows human analysts to assess and make informed decisions about the invariance properties of ML models. We show that such informative testing framework is preferred as ML models with the same global statistics (e.g., accuracy scores) can behave differently and have different visualized testing patterns. However, such human analysts might not lead to consistent decisions without a systematic sampling approach to select representative testing suites. In this work, we present a technical solution for selecting background scenes according to their semantic proximity to a target image that contains a foreground object being tested. We construct an ontology for storing knowledge about relationships among different objects using association analysis. This ontology enables an efficient and meaningful search for background scenes of different semantic distances to a target image, enabling the selection of a test suite that is both diverse and reasonable. Compared with other testing techniques, e.g., random sampling, nearest neighbors, or other sampled test suites by visual-language models (VLMs), our method achieved a superior balance between diversity and consistency of human annotations, thereby enhancing the reliability and comprehensiveness of background invariance testing.
Resolving Token-Space Gradient Conflicts: Token Space Manipulation for Transformer-Based Multi-Task Learning
Authors: Wooseong Jeong; Kuk-Jin Yoon
Venue: ICCV 2025 (main)

Abstract: Multi-Task Learning (MTL) enables multiple tasks to be learned within a shared network, but differences in objectives across tasks can cause negative transfer, where the learning of one task degrades another task's performance. While pre-trained transformers significantly improve MTL performance, their fixed network capacity and rigid structure limit adaptability. Previous dynamic network architectures attempt to address this but are inefficient as they directly convert shared parameters into task-specific ones. We propose Dynamic Token Modulation and Expansion (DTME-MTL), a framework applicable to any transformer-based MTL architecture. DTME-MTL enhances adaptability and reduces overfitting by identifying gradient conflicts in token space and applying adaptive solutions based on conflict type. Unlike prior methods that mitigate negative transfer by duplicating network parameters, DTME-MTL operates entirely in token space, enabling efficient adaptation without excessive parameter growth. Extensive experiments demonstrate that DTME-MTL consistently improves multi-task performance with minimal computational overhead, offering a scalable and effective solution for enhancing transformer-based MTL models.
ViT-Split: Unleashing the Power of Vision Foundation Models via Efficient Splitting Heads
Authors: Yifan Li; Xin Li; Tianqin Li; Wenbin He; Yu Kong; Liu Ren
Venue: ICCV 2025 (main)

Abstract: Vision foundation models (VFMs) have demonstrated remarkable performance across a wide range of downstream tasks. While several VFM adapters have shown promising results by leveraging the prior knowledge of VFMs, we identify two inefficiencies in these approaches. First, the interaction between convolutional neural network (CNN) and VFM backbone triggers early layer gradient backpropagation. Second, existing methods require tuning all components, adding complexity. Besides, these adapters alter VFM features, underutilizing the prior knowledge. To tackle these challenges, we propose a new approach called ViT-Split, based on a key observation: the layers of several VFMs, like DINOv2, can be divided into two distinct components: an extractor for learning low-level features and an adapter for learning task-specific features. Leveraging this insight, we eliminate the CNN branch and introduce two heads, task head and prior head, to the frozen VFM. The task head is designed to learn task-specific features, mitigating the early gradient propagation issue. The prior head is used to leverage the multi-scale prior features from the frozen VFM, reducing tuning parameters and overfitting. Extensive experiments on various tasks (e.g., segmentation, detection, depth estimation, and visual question answering) validate the effectiveness and efficiency of ViT-Split. Specifically, ViT-Split reduces training time up to 4xwhile achieving comparable or even better results on ADE20K, compared to other VFM adapters.