Weakly supervised semantic segmentation (WSSS) aims to generate dense labels using sparse annotations, such as image-level labels. Existing class activation map (CAM) generation methods have been able to locate rough objects. However, due to the limited information provided by image level labels, the bias activation problem, including over-activation, becomes another key obstacle in WSSS. To rectify such bias activation, we attempt to mine pixel level class feature distribution information from the entire dataset. Specifically, we propose to use normalizing flow to model the class feature distribution of all pixels across the entire dataset and design a Bias-Resilient WSSS framework based on Normalizing Flow (BRNF). Normalizing flow has the ability to map complex distributions to normal distributions. Building upon it, we designed an additional Gaussian mixture classifier which classifies pixels from the perspective of feature distributions, providing supplementary information to the conventional MLP based classifier. In addition, we use this distribution to sample low bias features as positive anchors for contrastive learning, thereby encouraging feature optimization toward the correct low-bias direction. Experimental results demonstrate that our method significantly outperforms existing baselines, achieving state-of-the-art performance on WSSS benchmarks. Code will be available at https://github.com/DpDark/BRNF.
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Camouflaged scenes, where objects blend seamlessly into their environments, pose significant challenges to both human observers and computer vision systems. These objects match the background in color, texture, and shape, making them difficult to detect. To this end, we propose leveraging the Segment Anything Model (SAM) to tackle this challenging task effectively. Specifically, we propose how to exploit SAM without requiring any manual prompts by proposing several ideas. At the core of our method lies the rich information extracted through multi-modal prompts. At first, we generate an image caption using the BLIP model and obtain its text embedding through the use of a text encoder. We then generate a visual embedding through the vision encoder of the BLIP model and use both as inputs to SAM to provide additional semantic information about the image. Finally, we propose a couple of architectural novelties, a) we effectively integrate the multi-modal information in SAM through a multi-level adapter and b) we replace the dense embedding of SAM with the image embedding of its image encoder. Our method achieves new state-of-the-art performance in 11 out of 12 metrics in three benchmark datasets for camouflaged detection. Additionally, our method can be successfully adapted to other tasks such as medical image segmentation performing on par or even outperforming the state-of-the-art methods. Our code is available in https://github.com/ic-qialanqian/Vision-Language-SAM.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction
PDF ↗Pixel-level vision tasks, such as semantic segmentation, require extensive and high-quality annotated data, which is costly to obtain. Semi-supervised semantic segmentation (SSSS) has emerged as a solution to alleviate the labeling burden by leveraging both labeled and unlabeled data through self-training techniques. Meanwhile, the advent of foundational segmentation models pre-trained on massive data, has shown the potential to generalize across domains effectively. This work explores whether a foundational segmentation model can address label scarcity in the pixel-level vision task as an annotator for unlabeled images. Specifically, we investigate the efficacy of using SEEM, a Segment Anything Model (SAM) variant fine-tuned for textual input, to generate predictive masks for unlabeled data. To address the shortcomings of using SEEM-generated masks as supervision, we propose ConformalSAM, a novel SSSS framework which first calibrates the foundation model using the target domain's labeled data and then filters out unreliable pixel labels of unlabeled data so that only high-confidence labels are used as supervision. By leveraging conformal prediction (CP) to adapt foundation models to target data through uncertainty calibration, ConformalSAM exploits the strong capability of the foundational segmentation model reliably which benefits the early-stage learning, while a subsequent self-reliance training strategy mitigates overfitting to SEEM-generated masks in the later training stage. Our experiment demonstrates that, on three standard benchmarks of SSSS, ConformalSAM achieves superior performance compared to recent SSSS methods and helps boost the performance of those methods as a plug-in.
Video grounding (VG) task focuses on locating specific moments in a video based on a query, usually in text form. However, traditional VG struggles with some scenarios like streaming video or queries using visual cues. To fill this gap, we present a new task named Online Video Grounding with Hybrid-modal Queries (OVG-HQ), which enables online segment localization using text, images, video segments, and their combinations. This task poses two new challenges: limited context in online settings and modality imbalance during training, where dominant modalities overshadow weaker ones. To address these, we propose OVG-HQ-Unify, a unified framework featuring a Parametric Memory Block (PMB) that uses neural network parameters to dynamically retain past context and a cross-modal distillation strategy that guides the learning of non-dominant modalities. This design enables a single model to effectively handle hybrid-modal queries. Due to the lack of suitable datasets, we construct QVHighlights-Unify, an expanded dataset with multi-modal queries. Besides, since offline metrics overlook prediction timeliness, we adapt them to the online setting, introducing oR@n, IoU=m, and online mean Average Precision (omAP) to evaluate both accuracy and efficiency. Experiments show that our OVG-HQ-Unify outperforms existing models, offering a robust solution for online, hybrid-modal video grounding. We will release our source code and dataset.
Exploiting Domain Properties in Language-Driven Domain Generalization for Semantic Segmentation
PDF ↗Recent domain generalized semantic segmentation (DGSS) studies have achieved notable improvements by distilling semantic knowledge from Vision-Language Models (VLMs). However, they overlook the semantic misalignment between visual and textual contexts, which arises due to the rigidity of a fixed context prompt learned on a single source domain. To this end, we present a novel domain generalization framework for semantic segmentation, namely Domain-aware Prompt-driven Masked Transformer (DPMFormer). Firstly, we introduce domain-aware prompt learning to facilitate semantic alignment between visual and textual cues. To capture various domain-specific properties with a single source dataset, we propose domain-aware contrastive learning along with the texture perturbation that diversifies the observable domains. Lastly, to establish a framework resilient against diverse environmental changes, we have proposed the domain-robust consistency learning which guides the model to minimize discrepancies of prediction from original and the augmented images. Through experiments and analyses, we demonstrate the superiority of the proposed framework, which establishes a new state-of-the-art on various DGSS benchmarks.
Diffusion Transformer (DiT)-based generation models have achieved remarkable success in video generation. However, their inherent computational demands pose significant efficiency challenges. In this paper, we exploit the inherent temporal non-uniformity of real-world videos, and observe that videos exhibit dynamic information density, with high-motion segments demanding greater detail preservation than static scenes. Inspired by this temporal non-uniformity, we propose DLFR-Gen, a training-free approach for Diffusion-based Video Generation with Dynamic Latent Frame Rate. DLFR-Gen adaptively adjusts the number of elements in latent space based on the motion frequency of the latent space content, using fewer tokens for low-frequency segments while preserving detail in high-frequency segments. Specifically, our key contributions are: (1) A dynamic frame rate scheduler for DiT video generation that adaptively assigns frame rates for video segments. (2) A novel latent-space frame merging method to align latent representations with their denoised counterparts before merging those redundant in low-resolution space. (3) A preference analysis of Rotary Positional Embeddings (RoPE) across DiT layers, informing a tailored RoPE strategy optimized for semantic and local information capture. Experiments show that DLFR-Gen can achieve a speedup up to 3x for video generation with minimal quality degradation.
We present REM, a framework for segmenting a wide range of concepts in video that can be described through natural language. Our method leverages the universal visual-language mapping learned by video diffusion models on Internet-scale data by fine-tuning them on small-scale Referring Object Segmentation datasets. Our key insight is to preserve the entirety of the generative model's architecture by shifting its objective from predicting noise to predicting mask latents. The resulting model can accurately segment rare and unseen objects, despite only being trained on a limited set of categories. Additionally, it can effortlessly generalize to non-object dynamic concepts, such as smoke or raindrops, as demonstrated in our new benchmark for Referring Video Process Segmentation (Ref-VPS). REM performs on par with the state-of-the-art on in-domain datasets, like Ref-DAVIS, while outperforming them by up to 12 IoU points out-of-domain, leveraging the power of generative pre-training. We also show that advancements in video generation directly improve segmentation.
ObjectRelator: Enabling Cross-View Object Relation Understanding Across Ego-Centric and Exo-Centric Perspectives
PDF ↗Bridging the gap between ego-centric and exo-centric views has been a long-standing question in computer vision. In this paper, we focus on the emerging Ego-Exo object correspondence task, which aims to understand object relations across ego-exo perspectives through segmentation. While numerous segmentation models have been proposed, most operate on a single image (view), making them impractical for cross-view scenarios. PSALM, a recently proposed segmentation method, stands out as a notable exception with its demonstrated zero-shot ability on this task. However, due to the drastic viewpoint change between ego and exo, PSALM fails to accurately locate and segment objects, especially in complex backgrounds or when object appearances change significantly. To address these issues, we propose ObjectRelator, a novel approach featuring two key modules: Multimodal Condition Fusion (MCFuse) and SSL-based Cross-View Object Alignment (XObjAlign). MCFuse introduces language as an additional cue, integrating both visual masks and textual descriptions to improve object localization and prevent incorrect associations. XObjAlign enforces cross-view consistency through self-supervised alignment, enhancing robustness to object appearance variations. Extensive experiments demonstrate ObjectRelator's effectiveness on the large-scale Ego-Exo4D benchmark and HANDAL-X (an adapted dataset for cross-view segmentation) with state-of-the-art performance. Code is available at: http://yuqianfu.com/ObjectRelator.
3D Gaussian Splatting (3DGS) has demonstrated impressive performance in novel view synthesis and real-time rendering. However, it heavily relies on high-quality initial sparse points from Structure-from-Motion (SfM) which often struggles in textureless regions, degrading the geometry and visual quality of 3DGS. To address this limitation, we propose a novel initialization pipeline, achieving high-fidelity reconstruction from dense image sequences without relying on SfM-derived point clouds. Specifically, we first propose an effective depth alignment method to align the estimated monocular depth with depth rendered from an under-optimized coarse Gaussian model using an unbiased depth rasterization approach and ensemble them afterward. After that, to efficiently process dense image sequences, we incorporate a progressive segmented initialization process that to generate the initial points. Extensive experiments demonstrate the superiority of our method over previous approaches. Notably, our method outperforms the SfM-based method by a 14.4% improvement in LPIPS on the Mip-NeRF360 datasets and a 30.7% improvement on the Tanks and Temples datasets.
In the medical imaging domain, it is a fundamental challenge to collect large-scale labeled data due to privacy, involved logistics, and the high cost of labeling medical images. In this work, we present the UK Biobank Organs and Bones (UKBOB), the largest labeled dataset of body organs of 51,761 MRI 3D samples (17.9 M 2D images) and a total of more than 1.37 billion 2D segmentation masks of 72 organs based on the UK Biobank MRI dataset. We utilize automatic labeling, filter the labels with organ-specific filters, and manually annotate a subset of 300 MRIs with 11 abdominal classes to validate the quality (UKBOB-manual). This approach allows for scaling up the dataset collection while maintaining confidence in the labels. We further confirm the validity of the labels by the zero-shot generalization of trained models on the filtered UKBOB to other small labeled datasets from a similar domain ( _E.g._ abdominal MRI). To further elevate the effect of the noisy labels, we propose a novel Entropy Test-time Adaptation (ETTA) to refine the segmentation output. We use UKBOB to train a foundation model (_Swin-BOB_) for 3D medical image segmentation based on Swin-UNetr, achieving state-of-the-art results in several benchmarks in 3D medical imaging, including BRATS brain MRI tumour challenge (+0.4% improvement), and BTCV abdominal CT scan benchmark (+1.3% improvement). Pre-trained model and our filtered labels will be made available with the UK Biobank.
In this paper, we propose SimMLM, a simple yet powerful framework for multimodal learning with missing modalities. Unlike existing approaches that rely on sophisticated network architectures or complex data imputation techniques, SimMLM provides a generic and effective solution that can adapt to various missing modality scenarios with improved accuracy and robustness. Specifically, SimMLM consists of a generic Dynamic Mixture of Modality Experts (DMoME) architecture, featuring a dynamic, learnable gating mechanism that automatically adjusts each modality's contribution in both full and partial modality settings. A key innovation of SimMLM is the proposed More vs. Fewer (MoFe) ranking loss, which ensures that task accuracy improves or remains stable as more modalities are made available. This aligns the model with an intuitive principle: removing one or more modalities should not increase accuracy. We validate SimMLM on multimodal medical image segmentation (BraTS 2018) and multimodal classification (UPMC Food-101, avMNIST) tasks, where it consistently surpasses competitive methods, demonstrating superior accuracy, interpretability, robustness, and reliability across both complete and missing modality scenarios at test time.
We address the challenge of lifting 2D visual segmentation to 3D in Gaussian Splatting. Existing methods often suffer from inconsistent 2D masks across viewpoints and produce noisy segmentation boundaries as they neglect these semantic cues to refine the learned Gaussians. To overcome this, we introduce Gaussian Instance Tracing (GIT), which augments the standard Gaussian representation with an instance weight matrix across input views. Leveraging the inherent consistency of Gaussians in 3D, we use this matrix to identify and correct 2D segmentation inconsistencies. Furthermore, since each Gaussian ideally corresponds to a single object, we propose a GIT-guided adaptive density control mechanism to split and prune ambiguous Gaussians during training, resulting in sharper and more coherent 2D and 3D segmentation boundaries. Experimental results show that our method extracts clean 3D assets and consistently improves 3D segmentation in both online (e.g., self-prompting) and offline (e.g., contrastive lifting) settings, enabling applications such as hierarchical segmentation, object extraction, and scene editing.
This work addresses motion-guided few-shot video object segmentation (FSVOS), which aims to segment dynamic objects in videos based on a few annotated examples with the same motion patterns. Existing FSVOS datasets and methods typically focus on object categories, which are static attributes that ignore the rich temporal dynamics in videos, limiting their application in scenarios requiring motion understanding. To fill this gap, we introduce MOVE, a large-scale dataset specifically designed for motion-guided FSVOS. Based on MOVE, we comprehensively evaluate 6 state-of-the-art methods from 3 different related tasks across 2 experimental settings. Our results reveal that current methods struggle to address motion-guided FSVOS, prompting us to analyze the associated challenges and propose a baseline method, Decoupled Motion Appearance Network (DMA). Experiments demonstrate that our approach achieves superior performance in few shot motion understanding, establishing a solid foundation for future research in this direction.
The remarkable performance of large multimodal models (LMMs) has attracted significant interest from the image segmentation community.To align with the next-token-prediction paradigm, current LMM-driven segmentation methods either use object boundary points to represent masks or introduce special segmentation tokens, whose hidden states are decoded by a segmentation model requiring the original image as input.However, these approaches often suffer from inadequate mask representation and complex architectures, limiting the potential of LMMs.In this work, we propose the Hierarchical Mask Tokenizer (HiMTok), which represents segmentation masks with up to 32 tokens and eliminates the need for the original image during mask de-tokenization.HiMTok allows for compact and coarse-to-fine mask representations, aligning well with the LLM next-token-prediction paradigm and facilitating the direct acquisition of segmentation capabilities.We develop a 3-stage training recipe for progressive learning of segmentation and visual capabilities, featuring a hierarchical mask loss for effective coarse-to-fine learning.Additionally, we enable bidirectional information flow, allowing conversion between bounding boxes and mask tokens to fully leverage multi-task training potential.Extensive experiments demonstrate that our method achieves state-of-the-art performance across various segmentation tasks,while also enhancing visual grounding and maintaining overall visual understanding.
Deep Adaptive Unfolded Network via Spatial Morphology Stripping and Spectral Filtration for Pan-sharpening
PDF ↗In the field of pan-sharpening, existing deep methods are hindered in deepening cross-modal complementarity in the intermediate feature, and lack effective strategies to harness the network entirety for optimal solutions, exhibiting limited feasibility and interpretability due to their black-box designs. Besides, validating pan-sharpening performance in high-level semantic tasks is intractable for the absence of datasets. To tackle these issues, we propose a deep adaptive unfolded network via spatial morphology stripping and spectral filtration for pan-sharpening, which is conceptualized as a linear inverse problem regularized by spatial and spectral priors. Specifically, we incorporate phase-oriented constraints into the spatial prior to facilitate the extraction of modal-invariant spatial morphology by intrinsic decomposition and leverage a physics-driven spectral filtration attention mechanism aligned with the spectral prior to mine the inherent spectral correlation. After transparently unfolding the model into a multi-stage network, an adaptive stage-exiting mechanism is designed to capitalize on fusion diversity by aggregating optimal image patches across candidate stages. To systematically complete the assessment, we construct the first panoptic segmentation dataset as a semantic-level benchmark for pan-sharpening performance validation. Extensive experiments are conducted to verify the merits of our method with state-of-the-arts. Code is available at https://github.com/Baixuzx7/DAPNet.
Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling
PDF ↗Recent advances in 3D neural representations and instance-level editing models have enabled the efficient creation of high-quality 3D content. However, achieving precise local 3D edits remains challenging, especially for Gaussian Splatting, due to inconsistent multi-view 2D part segmentations and inherently ambiguous nature of Score Distillation Sampling (SDS) loss. To address these limitations, we propose RoMaP, a novel local 3D Gaussian editing framework that enables precise and drastic part-level modifications. First, we introduce a robust 3D mask generation module with our 3D-Geometry Aware Label Prediction (3D-GALP), which uses spherical harmonics (SH) coefficients to model view-dependent label variations and soft-label property, yielding accurate and consistent part segmentations across viewpoints. Second, we propose a regularized SDS loss that combines the standard SDS loss with additional regularizers. In particular, an L1 anchor loss is introduced via our Scheduled Latent Mixing and Part (SLaMP) editing method, which generates high-quality part-edited 2D images and confines modifications only to the target region while preserving contextual coherence. Additional regularizers, such as Gaussian prior removal, further improve flexibility by allowing changes beyond the existing context, and robust 3D masking prevents unintended edits. Experimental results demonstrate that our RoMaP achieves state-of-the-art local 3D editing on both reconstructed and generated Gaussian scenes and objects qualitatively and quantitatively, making it possible for more robust and flexible part-level 3D Gaussian editing. Code is available at https://janeyeon.github.io/romap.
DUSt3R has recently demonstrated that many tasks in multi-view geometry, including estimating camera intrinsics and extrinsics, reconstructing 3D scenes, and establishing image correspondences, can be reduced to predicting a pair of viewpoint-invariant point maps, i.e., pixel-aligned point clouds defined in a common reference frame. While this formulation is elegant and powerful, it is limited to static scenes. To overcome this limitation, we introduce the concept of Dynamic Point Maps (DPM), which extends standard point maps to support 4D tasks such as motion segmentation, scene flow estimation, 3D object tracking, and 2D correspondence. Our key insight is that, when time is introduced, several possible spatial and temporal references can be used to define the point maps. We identify a minimal subset of these combinations that can be regressed by a network to solve the aforementioned tasks. We train a DPM predictor on a mixture of synthetic and real data and evaluate it across diverse benchmarks, including video depth prediction, dynamic point cloud reconstruction, 3D scene flow, and object pose tracking, achieving state-of-the-art performance.
Diffusion models have demonstrated powerful capability as a versatilist for dense vision tasks, yet the generalization ability to unseen domains remains rarely explored. This paper presents HarDiff, an efficient frequency learning scheme, so as to advance generalizable paradigms for diffusion based dense prediction. It draws inspiration from a fine-grained analysis of Discrete Hartley Transform, where some low-frequency features activate the broader content of an image, while some high-frequency features maintain sufficient details for dense pixels. Consequently, HarDiff consists of two key components. The low-frequency training process extracts structural priors from the source domain, to enhance understanding of task-related content. The high-frequency sampling process utilizes detail-oriented guidance from the unseen target domain, to infer precise dense predictions with target-related details. Extensive empirical evidence shows that HarDiff can be easily plugged into various dense vision tasks, e.g., semantic segmentation, depth estimation and haze removal, yielding improvements over the state-of-the-art methods in twelve public benchmarks.
Online mapping models show remarkable results in predicting vectorized maps from multi-view camera images only. However, all existing approaches still rely on ground-truth high-definition maps during training, which are expensive to obtain and often not geographically diverse enough for reliable generalization. In this work, we propose PseudoMapTrainer, a novel approach to online mapping that uses pseudo-labels generated from unlabeled sensor data. We derive those pseudo-labels by reconstructing the road surface from multi-camera imagery using Gaussian splatting and semantics of a pre-trained 2D segmentation network. In addition, we introduce a mask-aware assignment algorithm and loss function to handle partially masked pseudo-labels, allowing for the first time the training of online mapping models without any ground-truth maps. Furthermore, our pseudo-labels can be effectively used to pre-train an online model in a semi-supervised manner to leverage large-scale unlabeled crowdsourced data. The code is available at github.com/boschresearch/PseudoMapTrainer.
Prosthetic legs play a pivotal role in clinical rehabilitation, allowing individuals with lower-limb amputations the ability to regain mobility and improve their quality of life. Gait analysis is fundamental for optimizing prosthesis design and alignment, directly impacting the mobility and life quality of individuals with lower-limb amputations. Vision-based machine learning (ML) methods offer a scalable and non-invasive solution to gait analysis, but face challenges in correctly detecting and analyzing prosthesis, due to their unique appearances and new movement patterns. In this paper, we aim to bridge this gap by introducing a multi-purpose dataset, namely ProGait, to support multiple vision tasks including Video Object Segmentation, 2D Human Pose Estimation, and Gait Analysis (GA). ProGait provides 412 video clips from four above-knee amputees when testing multiple newly-fitted prosthetic legs through walking trials, and depicts the presence, contours, poses, and gait patterns of human subjects with transfemoral prosthetic legs. Alongside the dataset itself, we also present benchmark tasks and fine-tuned baseline models to illustrate the practical application and performance of the ProGait dataset. We compared our baseline models against pre-trained vision models, demonstrating improved generalizability when applying the ProGait dataset for prosthesis-specific tasks. The ProGait dataset is available at https://huggingface.co/datasets/ericyxy98/ProGait, and the source codes of our benchmark tasks are available at https://github.com/pittisl/ProGait.