Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics treatment evaluation and clinical research. The complex kidney system comprises various components across multiple levels including regions (cortex medulla) functional units (glomeruli tubules) and cells (podocytes mesangial cells in glomerulus). Prior studies have predominantly overlooked the intricate spatial interrelations among objects from clinical knowledge. In this research we introduce a novel universal proposition learning approach called panoramic renal pathology segmentation (PrPSeg) designed to segment comprehensively panoramic structures within kidney by integrating extensive knowledge of kidney anatomy. In this paper we propose (1) the design of a comprehensive universal proposition matrix for renal pathology facilitating the incorporation of classification and spatial relationships into the segmentation process; (2) a token-based dynamic head single network architecture with the improvement of the partial label image segmentation and capability for future data enlargement; and (3) an anatomy loss function quantifying the inter-object relationships across the kidney.
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Unveiling the Power of Audio-Visual Early Fusion Transformers with Dense Interactions through Masked Modeling
PDF ↗Humans possess a remarkable ability to integrate auditory and visual information enabling a deeper understanding of the surrounding environment. This early fusion of audio and visual cues demonstrated through cognitive psychology and neuroscience research offers promising potential for developing multimodal perception models. However training early fusion architectures poses significant challenges as the increased model expressivity requires robust learning frameworks to harness their enhanced capabilities. In this paper we address this challenge by leveraging the masked reconstruction framework previously successful in unimodal settings to train audio-visual encoders with early fusion. Additionally we propose an attention-based fusion module that captures interactions between local audio and visual representations enhancing the model's ability to capture fine-grained interactions. While effective this procedure can become computationally intractable as the number of local representations increases. Thus to address the computational complexity we propose an alternative procedure that factorizes the local representations before representing audio-visual interactions. Extensive evaluations on a variety of datasets demonstrate the superiority of our approach in audio-event classification visual sound localization sound separation and audio-visual segmentation. These contributions enable the efficient training of deeply integrated audio-visual models and significantly advance the usefulness of early fusion architectures.
Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation
PDF ↗Recent weakly supervised semantic segmentation (WSSS) methods strive to incorporate contextual knowledge to improve the completeness of class activation maps (CAM). In this work we argue that the knowledge bias between instances and contexts affects the capability of the prototype to sufficiently understand instance semantics. Inspired by prototype learning theory we propose leveraging prototype awareness to capture diverse and fine-grained feature attributes of instances. The hypothesis is that contextual prototypes might erroneously activate similar and frequently co-occurring object categories due to this knowledge bias. Therefore we propose to enhance the prototype representation ability by mitigating the bias to better capture spatial coverage in semantic object regions. With this goal we present a Context Prototype-Aware Learning (CPAL) strategy which leverages semantic context to enrich instance comprehension. The core of this method is to accurately capture intra-class variations in object features through context-aware prototypes facilitating the adaptation to the semantic attributes of various instances. We design feature distribution alignment to optimize prototype awareness aligning instance feature distributions with dense features. In addition a unified training framework is proposed to combine label-guided classification supervision and prototypes-guided self-supervision. Experimental results on PASCAL VOC 2012 and MS COCO 2014 show that CPAL significantly improves off-the-shelf methods and achieves state-of-the-art performance. The project is available at \href https://github.com/Barrett-python/CPAL https://github.com/Barrett-python/CPAL.
Towards holistic understanding of 3D scenes a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories while also reflecting the inherent hierarchical structure. To achieve this we propose OmniSeg3D an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework which is accomplished by two steps. Firstly we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly image features rendered from the 3D feature field are clustered at different levels which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations this framework yields a global consistent 3D feature field which further enables hierarchical segmentation multi-object selection and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation.
Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning
PDF ↗In the realm of computer vision and graphics accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking registration texture transfer and statistical shape analysis. Moving beyond traditional hand-crafted and data-driven feature learning methods we incorporate spectral methods with deep learning focusing on functional maps (FMs) and optimal transport (OT). Traditional OT-based approaches often reliant on entropy regularization OT in learning-based framework face computational challenges due to their quadratic cost. Our key contribution is to employ the sliced Wasserstein distance (SWD) for OT which is a valid fast optimal transport metric in an unsupervised shape matching framework. This unsupervised framework integrates functional map regularizers with a novel OT-based loss derived from SWD enhancing feature alignment between shapes treated as discrete probability measures. We also introduce an adaptive refinement process utilizing entropy regularized OT further refining feature alignments for accurate point-to-point correspondences. Our method demonstrates superior performance in non-rigid shape matching including near-isometric and non-isometric scenarios and excels in downstream tasks like segmentation transfer. The empirical results on diverse datasets highlight our framework's effectiveness and generalization capabilities setting new standards in non-rigid shape matching with efficient OT metrics and an adaptive refinement module.
Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences of location tokens. This paradigm lacks pixel-level representations that are important for fine-grained visual understanding and diagnosis. In this work we introduce GROUNDHOG an MLLM developed by grounding Large Language Models to holistic segmentation. GROUNDHOG incorporates a masked feature extractor and converts extracted features into visual entity tokens for the MLLM backbone which then connects groundable phrases to unified grounding masks by retrieving and merging the entity masks. To train GROUNDHOG we carefully curated M3G2 a grounded visual instruction tuning dataset with Multi-Modal Multi-Grained Grounding by harvesting a collection of segmentation-grounded datasets with rich annotations. Our experimental results show that GROUNDHOG achieves superior performance on various language grounding tasks without task-specific fine-tuning and significantly reduces object hallucination. GROUNDHOG also demonstrates better grounding towards complex forms of visual input and provides easy-to-understand diagnosis in failure cases.
Large foundation models known for their strong zero-shot generalization have excelled in visual and language applications. However applying them to medical image segmentation a domain with diverse imaging types and target labels remains an open challenge. Current approaches such as adapting interactive segmentation models like Segment Anything Model (SAM) require user prompts for each sample during inference. Alternatively transfer learning methods like few/one-shot models demand labeled samples leading to high costs. This paper introduces a new paradigm toward the universal medical image segmentation termed 'One-Prompt Segmentation.' One-Prompt Segmentation combines the strengths of one-shot and interactive methods. In the inference stage with just one prompted sample it can adeptly handle the unseen task in a single forward pass. We train One-Prompt Model on 64 open-source medical datasets accompanied by the collection of over 3000 clinician-labeled prompts. Tested on 14 previously unseen datasets the One-Prompt Model showcases superior zero-shot segmentation capabilities outperforming a wide range of related methods. The code and data is released as https://github.com/KidsWithTokens/one-prompt.
MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation
PDF ↗Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains but due to the absence of style diversity during training enhancing performance on unseen target domains using only single source domain data remains a challenging task. Generation of simulated data is a feasible alternative to retrieving large style-diverse real-world datasets as it is a cumbersome and budget-intensive process. However the large domain-specific inconsistencies between simulated and real-world data pose a significant generalization challenge in semantic segmentation. In this work to alleviate this problem we propose a novel Multi-Resolution Feature Perturbation (MRFP) technique to randomize domain-specific fine-grained features and perturb style of coarse features. Our experimental results on various urban-scene segmentation datasets clearly indicate that along with the perturbation of style-information perturbation of fine-feature components is paramount to learn domain invariant robust feature maps for semantic segmentation models. MRFP is a simple and computationally efficient transferable module with no additional learnable parameters or objective functions that helps state-of-the-art deep neural networks to learn robust domain invariant features for simulation-to-real semantic segmentation. Code is available at https://github.com/airl-iisc/MRFP.
We propose a novel approach to the action segmentation task for long untrimmed videos based on solving an optimal transport problem. By encoding a temporal consistency prior into a Gromov-Wasserstein problem we are able to decode a temporally consistent segmentation from a noisy affinity/matching cost matrix between video frames and action classes. Unlike previous approaches our method does not require knowing the action order for a video to attain temporal consistency. Furthermore our resulting (fused) Gromov-Wasserstein problem can be efficiently solved on GPUs using a few iterations of projected mirror descent. We demonstrate the effectiveness of our method in an unsupervised learning setting where our method is used to generate pseudo-labels for self-training. We evaluate our segmentation approach and unsupervised learning pipeline on the Breakfast 50-Salads YouTube Instructions and Desktop Assembly datasets yielding state-of-the-art results for the unsupervised video action segmentation task.
The robust association of the same objects across video frames in complex scenes is crucial for many applications especially object tracking. Current methods predominantly rely on labeled domain-specific video datasets which limits cross-domain generalization of learned similarity embeddings. We propose MASA a novel method for robust instance association learning capable of matching any objects within videos across diverse domains without tracking labels. Leveraging the rich object segmentation from the Segment Anything Model (SAM) MASA learns instance-level correspondence through exhausive data transformations. We treat the SAM outputs as dense object region proposals and learn to match those regions from a vast image collection. We further design a universal MASA adapter which can work in tandem with foundational segmentation or detection models and enable them to track any detected objects. Those combinations present strong zero-shot tracking ability in complex domains. Extensive tests on multiple challenging MOT and MOTS benchmarks indicate that the proposed method using only unlabelled static images achieves even better performance than state-of-the-art methods trained with fully annotated in-domain video sequences in zero-shot association. Our code is available at https://github.com/siyuanliii/masa.
Due to its promising results density map regression has been widely employed for image-based crowd counting. The approach however often suffers from severe performance degradation when tested on data from unseen scenarios the so-called "domain shift" problem. To address the problem we investigate in this work single domain generalization (SDG) for crowd counting. The existing SDG approaches are mainly for image classification and segmentation and can hardly be extended to our case due to its regression nature and label ambiguity (i.e. ambiguous pixel-level ground truths). We propose MPCount a novel effective SDG approach even for narrow source distribution. MPCount stores diverse density values for density map regression and reconstructs domain-invariant features by means of only one memory bank a content error mask and attention consistency loss. By partitioning the image into grids it employs patch-wise classification as an auxiliary task to mitigate label ambiguity. Through extensive experiments on different datasets MPCount is shown to significantly improve counting accuracy compared to the state of the art under diverse scenarios unobserved in the training data characterized by narrow source distribution. Code is available at https://github.com/Shimmer93/MPCount.
Understanding long real-world videos requires modeling of long-range visual dependencies. To this end we explore video-first architectures building on the common paradigm of transferring large-scale image--text models to video via shallow temporal fusion. However we expose two limitations to the approach: (1) decreased spatial capabilities likely due to poor video--language alignment in standard video datasets and (2) higher memory consumption bottlenecking the number of frames that can be processed. To mitigate the memory bottleneck we systematically analyze the memory/accuracy trade-off of various efficient methods: factorized attention parameter-efficient image-to-video adaptation input masking and multi-resolution patchification. Surprisingly simply masking large portions of the video (up to 75%) during contrastive pre-training proves to be one of the most robust ways to scale encoders to videos up to 4.3 minutes at 1 FPS. Our simple approach for training long video-to-text models which scales to 1B parameters does not add new architectural complexity and is able to outperform the popular paradigm of using much larger LLMs as an information aggregator over segment-based information on benchmarks with long-range temporal dependencies (YouCook2 EgoSchema).
DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentation
PDF ↗Recently One-stage Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained increasing interest due to simplification over its cumbersome multi-stage counterpart. Limited by the inherent ambiguity of Class Activation Map (CAM) we observe that one-stage pipelines often encounter confirmation bias caused by incorrect CAM pseudo-labels impairing their final segmentation performance. Although recent works discard many unreliable pseudo-labels to implicitly alleviate this issue they fail to exploit sufficient supervision for their models. To this end we propose a dual student framework with trustworthy progressive learning (DuPL). Specifically we propose a dual student network with a discrepancy loss to yield diverse CAMs for each sub-net. The two sub-nets generate supervision for each other mitigating the confirmation bias caused by learning their own incorrect pseudo-labels. In this process we progressively introduce more trustworthy pseudo-labels to be involved in the supervision through dynamic threshold adjustment with an adaptive noise filtering strategy. Moreover we believe that every pixel even discarded from supervision due to its unreliability is important for WSSS. Thus we develop consistency regularization on these discarded regions providing supervision of every pixel. Experiment results demonstrate the superiority of the proposed DuPL over the recent state-of-the-art alternatives on PASCAL VOC 2012 and MS COCO datasets. Code is available at https://github.com/Wu0409/DuPL.
RankMatch: Exploring the Better Consistency Regularization for Semi-supervised Semantic Segmentation
PDF ↗The key lie in semi-supervised semantic segmentation is how to fully exploit substantial unlabeled data to improve the model's generalization performance by resorting to constructing effective supervision signals. Most methods tend to directly apply contrastive learning to seek additional supervision to complement independent regular pixel-wise consistency regularization. However these methods tend not to be preferred ascribed to their complicated designs heavy memory footprints and susceptibility to confirmation bias. In this paper we analyze the bottlenecks exist in contrastive learning-based methods and offer a fresh perspective on inter-pixel correlations to construct more safe and effective supervision signals which is in line with the nature of semantic segmentation. To this end we develop a coherent RankMatch network including the construction of representative agents to model inter-pixel correlation beyond regular individual pixel-wise consistency and further unlock the potential of agents by modeling inter-agent relationships in pursuit of rank-aware correlation consistency. Extensive experimental results on multiple benchmarks including mitochondria segmentation demonstrate that RankMatch performs favorably against state-of-the-art methods. Particularly in the low-data regimes RankMatch achieves significant improvements.
We present a neural radiance field method for urban-scale semantic and building-level instance segmentation from aerial images by lifting noisy 2D labels to 3D. This is a challenging problem due to two primary reasons. Firstly objects in urban aerial images exhibit substantial variations in size including buildings cars and roads which pose a significant challenge for accurate 2D segmentation. Secondly the 2D labels generated by existing segmentation methods suffer from the multi-view inconsistency problem especially in the case of aerial images where each image captures only a small portion of the entire scene. To overcome these limitations we first introduce a scale-adaptive semantic label fusion strategy that enhances the segmentation of objects of varying sizes by combining labels predicted from different altitudes harnessing the novel-view synthesis capabilities of NeRF. We then introduce a novel cross-view instance label grouping strategy based on the 3D scene representation to mitigate the multi-view inconsistency problem in the 2D instance labels. Furthermore we exploit multi-view reconstructed depth priors to improve the geometric quality of the reconstructed radiance field resulting in enhanced segmentation results. Experiments on multiple real-world urban-scale datasets demonstrate that our approach outperforms existing methods highlighting its effectiveness. The source code is available at https://github.com/zyqz97/Aerial_lifting.
Image signal processing (ISP) pipeline plays a fundamental role in digital cameras which converts raw Bayer sensor data to RGB images. However ISP-generated images usually suffer from imperfections due to the compounded degradations that stem from sensor noises demosaicing noises compression artifacts and possibly adverse effects of erroneous ISP hyperparameter settings such as ISO and gamma values. In a general sense these ISP imperfections can be considered as degradations. The highly complex mechanisms of ISP degradations some of which are even unknown pose great challenges to the generalization capability of deep neural networks (DNN) for image restoration and to their adaptability to downstream tasks. To tackle the issues we propose a novel DNN approach to learn degradation-independent representations (DiR) through the refinement of a self-supervised learned baseline representation. The proposed DiR learning technique has remarkable domain generalization capability and consequently it outperforms state-of-the-art methods across various downstream tasks including blind image restoration object detection and instance segmentation as verified in our experiments.
Feature matching is a crucial task in the field of computer vision which involves finding correspondences between images. Previous studies achieve remarkable performance using learning-based feature comparison. However the pervasive presence of matching redundancy between images gives rise to unnecessary and error-prone computations in these methods imposing limitations on their accuracy. To address this issue we propose MESA a novel approach to establish precise area (or region) matches for efficient matching redundancy reduction. MESA first leverages the advanced image understanding capability of SAM a state-of-the-art foundation model for image segmentation to obtain image areas with implicit semantic. Then a multi-relational graph is proposed to model the spatial structure of these areas and construct their scale hierarchy. Based on graphical models derived from the graph the area matching is reformulated as an energy minimization task and effectively resolved. Extensive experiments demonstrate that MESA yields substantial precision improvement for multiple point matchers in indoor and outdoor downstream tasks e.g. +13.61% for DKM in indoor pose estimation.
Self-supervised image backbones can be used to address complex 2D tasks (e.g. semantic segmentation object discovery) very efficiently and with little or no downstream supervision. Ideally 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results obtained thanks to distillation methods that keep improving. Yet we still notice a large performance gap when measuring by linear probing the quality of distilled vs fully supervised features. In this work instead of focusing only on the distillation method we study the effect of three pillars for distillation: the 3D backbone the pretrained 2D backbone and the pretraining 2D+3D dataset. In particular thanks to our scalable distillation method named ScaLR we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features and to improve the robustness of the pretrained backbones to domain gaps and perturbations.
We propose a lightweight and scalable Regional Point-Language Contrastive learning framework namely RegionPLC for open-world 3D scene understanding aiming to identify and recognize open-set objects and categories. Specifically based on our empirical studies we introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models yielding high-quality dense region-level language descriptions without human 3D annotations. Subsequently we devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning from dense regional language supervision. We carry out extensive experiments on ScanNet ScanNet200 and nuScenes datasets and our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2% and 9.1% for semantic and instance segmentation respectively while maintaining greater scalability and lower resource demands. Furthermore our method has the flexibility to be effortlessly integrated with language models to enable open-ended grounded 3D reasoning without extra task-specific training. Code will be released.
Interpreting camera data is key for autonomously acting systems such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand their surroundings and need the ability to deal with novel situations. This paper tackles open-world semantic segmentation i.e. the variant of interpreting image data in which objects occur that have not been seen during training. We propose a novel approach that performs accurate closed-world semantic segmentation and at the same time can identify new categories without requiring any additional training data. Our approach additionally provides a similarity measure for every newly discovered class in an image to a known category which can be useful information in downstream tasks such as planning or mapping. Through extensive experiments we show that our model achieves state-of-the-art results on classes known from training data as well as for anomaly segmentation and can distinguish between different unknown classes.