Foundation segmentation models while powerful pose a significant risk: they enable users to effortlessly extract any objects from any digital content with a single click potentially leading to copyright infringement or malicious misuse. To mitigate this risk we introduce a new task "Anything Unsegmentable" to grant any image "the right to be unsegmented". The ambitious pursuit of the task is to achieve highly transferable adversarial attack against all prompt-based segmentation models regardless of model parameterizations and prompts. We highlight the non-transferable and heterogeneous nature of prompt-specific adversarial noises. Our approach focuses on disrupting image encoder features to achieve prompt-agnostic attacks. Intriguingly targeted feature attacks exhibit better transferability compared to untargeted ones suggesting the optimal update direction aligns with the image manifold. Based on the observations we design a novel attack named Unsegment Anything by Simulating Deformation (UAD). Our attack optimizes a differentiable deformation function to create a target deformed image which alters structural information while preserving achievable feature distance by adversarial example. Extensive experiments verify the effectiveness of our approach compromising a variety of promptable segmentation models with different architectures and prompt interfaces.
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Recent advancements in Vision-Language Models (VLMs) have marked a significant leap in bridging the gap between computer vision and natural language processing. However traditional VLMs trained through contrastive learning on limited and noisy image-text pairs often lack the spatial and linguistic understanding to generalize well to dense vision tasks or less common languages. Our approach Solid Foundation CLIP (SF-CLIP) circumvents this issue by implicitly building on the solid visual and language understanding of foundational models trained on vast amounts of unimodal data. SF-CLIP integrates contrastive image-text pretraining with a masked knowledge distillation from large foundational text and vision models. This methodology guides our VLM in developing robust text and image representations. As a result SF-CLIP shows exceptional zero-shot classification accuracy and enhanced image and text retrieval capabilities setting a new state of the art for ViT-B/16 trained on YFCC15M and CC12M. Moreover the dense per-patch supervision enhances our zero-shot and linear probe performance in semantic segmentation tasks. A remarkable aspect of our model is its multilingual proficiency evidenced by strong retrieval results in multiple languages despite being trained predominantly on English data. We achieve all of these improvements without sacrificing the training efficiency through our selective application of masked distillation and the inheritance of teacher word embeddings.
Semantic segmentation models while effective for in-distribution categories face challenges in real-world deployment due to encountering out-of-distribution (OoD) objects. Detecting these OoD objects is crucial for safety-critical applications. Existing methods rely on anomaly scores but choosing a suitable threshold for generating masks presents difficulties and can lead to fragmentation and inaccuracy. This paper introduces a method to convert anomaly Score To segmentation Mask called S2M a simple and effective framework for OoD detection in semantic segmentation. Unlike assigning anomaly scores to pixels S2M directly segments the entire OoD object. By transforming anomaly scores into prompts for a promptable segmentation model S2M eliminates the need for threshold selection. Extensive experiments demonstrate that S2M outperforms the state-of-the-art by approximately 20% in IoU and 40% in mean F1 score on average across various benchmarks including Fishyscapes Segment-Me-If-You-Can and RoadAnomaly datasets.
SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers
PDF ↗Unsupervised object-centric learning aims to decompose scenes into interpretable object entities termed slots. Slot-based auto-encoders stand out as a prominent method for this task. Within them crucial aspects include guiding the encoder to generate object-specific slots and ensuring the decoder utilizes them during reconstruction. This work introduces two novel techniques (i) an attention-based self-training approach which distills superior slot-based attention masks from the decoder to the encoder enhancing object segmentation and (ii) an innovative patch-order permutation strategy for autoregressive transformers that strengthens the role of slot vectors in reconstruction. The effectiveness of these strategies is showcased experimentally. The combined approach significantly surpasses prior slot-based autoencoder methods in unsupervised object segmentation especially with complex real-world images. We provide the implementation code at https://github.com/gkakogeorgiou/spot .
Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D poses in cluttered scenes presenting significant challenges for model generalizability. Fortunately the recent Segment Anything Model (SAM) has showcased remarkable zero-shot transfer performance which provides a promising solution to tackle this task. Motivated by this we introduce SAM-6D a novel framework designed to realize the task through two steps including instance segmentation and pose estimation. Given the target objects SAM-6D employs two dedicated sub-networks namely Instance Segmentation Model (ISM) and Pose Estimation Model (PEM) to perform these steps on cluttered RGB-D images. ISM takes SAM as an advanced starting point to generate all possible object proposals and selectively preserves valid ones through meticulously crafted object matching scores in terms of semantics appearance and geometry. By treating pose estimation as a partial-to-partial point matching problem PEM performs a two-stage point matching process featuring a novel design of background tokens to construct dense 3D-3D correspondence ultimately yielding the pose estimates. Without bells and whistles SAM-6D outperforms the existing methods on the seven core datasets of the BOP Benchmark for both instance segmentation and pose estimation of novel objects.
Colorizing line art is a pivotal task in the production of hand-drawn cel animation. This typically involves digital painters using a paint bucket tool to manually color each segment enclosed by lines based on RGB values predetermined by a color designer. This frame-by-frame process is both arduous and time-intensive. Current automated methods mainly focus on segment matching. This technique migrates colors from a reference to the target frame by aligning features within line-enclosed segments across frames. However issues like occlusion and wrinkles in animations often disrupt these direct correspondences leading to mismatches. In this work we introduce a new learning-based inclusion matching pipeline which directs the network to comprehend the inclusion relationships between segments rather than relying solely on direct visual correspondences. Our method features a two-stage pipeline that integrates a coarse color warping module with an inclusion matching module enabling more nuanced and accurate colorization. To facilitate the training of our network we also develope a unique dataset referred to as PaintBucket-Character. This dataset includes rendered line arts alongside their colorized counterparts featuring various 3D characters. Extensive experiments demonstrate the effectiveness and superiority of our method over existing techniques.
CLIP has demonstrated marked progress in visual recognition due to its powerful pre-training on large-scale image-text pairs. However it still remains a critical challenge: how to transfer image-level knowledge into pixel-level understanding tasks such as semantic segmentation. In this paper to solve the mentioned challenge we analyze the gap between the capability of the CLIP model and the requirement of the zero-shot semantic segmentation task. Based on our analysis and observations we propose a novel method for zero-shot semantic segmentation dubbed CLIP-RC (CLIP with Regional Clues) bringing two main insights. On the one hand a region-level bridge is necessary to provide fine-grained semantics. On the other hand overfitting should be mitigated during the training stage. Benefiting from the above discoveries CLIP-RC achieves state-of-the-art performance on various zero-shot semantic segmentation benchmarks including PASCAL VOC PASCAL Context and COCO-Stuff 164K. Code will be available at https://github.com/Jittor/JSeg.
We introduce SynCLR a novel approach for learning visual representations exclusively from synthetic images without any real data. We synthesize a large dataset of image captions using LLMs then use an off-the-shelf text-to-image model to generate multiple images corresponding to each synthetic caption. We perform visual representation learning on these synthetic images via contrastive learning treating images sharing the same caption as positive pairs. The resulting representations demonstrate remarkable transferability competing favorably with other general-purpose visual representation learners such as CLIP and DINO v2 in image classification tasks. Furthermore in dense prediction tasks such as semantic segmentation SynCLR outperforms previous self-supervised methods by a significant margin e.g. improving over MAE and iBOT by 5.0 and 3.1 mIoU on ADE20k for ViT-B/16.
Prominent solutions for medical image segmentation are typically tailored for automatic or interactive setups posing challenges in facilitating progress achieved in one task to another. This also necessitates separate models for each task duplicating both training time and parameters. To address above issues we introduce S2VNet a universal framework that leverages Slice-to-Volume propagation to unify automatic/interactive segmentation within a single model and one training session. Inspired by clustering-based segmentation techniques S2VNet makes full use of the slice-wise structure of volumetric data by initializing cluster centers from the cluster results of previous slice. This enables knowledge acquired from prior slices to assist in the segmentation of the current slice further efficiently bridging the communication between remote slices using mere 2D networks. Moreover such a framework readily accommodates inter- active segmentation with no architectural change simply by initializing centroids from user inputs. S2VNet distinguishes itself by swift inference speeds and reduced memory consumption compared to prevailing 3D solutions. It can also handle multi-class interactions with each of them serving to initialize different centroids. Experiments on three benchmarks demonstrate S2VNet surpasses task-specified solutions on both automatic/interactive setups.
In this paper we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data with the overarching objective of attaining robust and universal object segmentation within the event-centric domain. One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery. Capitalizing on the vast repositories of datasets with paired events and RGB images our proposition is to harness and extrapolate the profound knowledge encapsulated within the pre-trained SAM framework. As a cornerstone to achieving this we introduce a multi-scale feature distillation methodology. This methodology rigorously optimizes the alignment of token embeddings originating from event data with their RGB image counterparts thereby preserving and enhancing the robustness of the overall architecture. Considering the distinct significance that token embeddings from intermediate layers hold for higher-level embeddings our strategy is centered on accurately calibrating the pivotal token embeddings. This targeted calibration is aimed at effectively managing the discrepancies in high-level embeddings originating from both the event and image domains. Extensive experiments on different datasets demonstrate the effectiveness of the proposed distillation method. Code in https://github.com/happychenpipi/EventSAM.
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
PDF ↗Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks without dense annotations. However attributed to the frequent coupling of co-occurring objects and the limited supervision from image-level labels the challenging co-occurrence problem is widely present and leads to false activation of objects in WSSS. In this work we devise a 'Separate and Conquer' scheme SeCo to tackle this issue from dimensions of image space and feature space. In the image space we propose to 'separate' the co-occurring objects with image decomposition by subdividing images into patches. Importantly we assign each patch a category tag from Class Activation Maps (CAMs) which spatially helps remove the co-context bias and guide the subsequent representation. In the feature space we propose to 'conquer' the false activation by enhancing semantic representation with multi-granularity knowledge contrast. To this end a dual-teacher-single-student architecture is designed and tag-guided contrast is conducted which guarantee the correctness of knowledge and further facilitate the discrepancy among co-contexts. We streamline the multi-staged WSSS pipeline end-to-end and tackle this issue without external supervision. Extensive experiments are conducted validating the efficiency of our method and the superiority over previous single-staged and even multi-staged competitors on PASCAL VOC and MS COCO. Code is available at https://github.com/zwyang6/SeCo.git.
Sparse LiDAR point clouds cause severe loss of detail of static structures and reduce the density of static points available for navigation. Reduced density can be detrimental to navigation under several scenarios. We observe that despite high sparsity in most cases the global topology of LiDAR outlining the static structures can be inferred. We utilize this property to obtain a backbone skeleton of a LiDAR scan in the form of a single connected component that is a proxy to its global topology. We utilize the backbone to augment new points along static structures to overcome sparsity. Newly introduced points could correspond to existing static structures or to static points that were earlier obstructed by dynamic objects. To the best of our knowledge we are the first to use such a strategy for sparse LiDAR point clouds. Existing solutions close to our approach fail to identify and preserve the global static LiDAR topology and generate sub-optimal points. We propose GLiDR a Graph Generative network that is topologically regularized using 0-dimensional Persistent Homology (PH) constraints. This enables GLiDR to introduce newer static points along a topologically consistent global static LiDAR backbone. GLiDR generates precise static points using 32x sparser dynamic scans and performs better than the baselines across three datasets. GLiDR generates a valuable byproduct - an accurate binary segmentation mask of static and dynamic objects that are helpful for navigation planning and safety in constrained environments. The newly introduced static points allow GLiDR to outperform LiDAR-based navigation using SLAM in several settings.
This paper studies the problem of structured 3D recon- struction using wireframes that consist of line segments and junctions focusing on the computation of structured boundary geometries of scenes. Instead of leveraging matching-based solutions from 2D wireframes (or line segments) for 3D wireframe reconstruction as done in prior arts we present NEAT a rendering-distilling formulation using neural fields to represent 3D line segments with 2D observations and bipartite matching for perceiving and dis- tilling of a sparse set of 3D global junctions. The proposed NEAT enjoys the joint optimization of the neural fields and the global junctions from scratch using view-dependent 2D observations without precomputed cross-view feature matching. Comprehensive experiments on the DTU and BlendedMVS datasets demonstrate our NEAT's superiority over state-of-the-art alternatives for 3D wireframe recon- struction. Moreover the distilled 3D global junctions by NEAT are a better initialization than SfM points for the recently-emerged 3D Gaussian Splatting for high-fidelity novel view synthesis using about 20 times fewer initial 3D points. Project page: https://xuenan.net/neat
Route planning for navigation under partial observability plays a crucial role in modern robotics and autonomous driving. Existing route planning approaches can be categorized into two main classes: traditional autoregressive and diffusion-based methods. The former often fails due to its myopic nature while the latter either assumes full observability or struggles to adapt to unfamiliar scenarios due to strong couplings with behavior cloning from experts. To address these deficiencies we propose a versatile diffusion-based approach for both 2D and 3D route planning under partial observability. Specifically our value-guided diffusion policy first generates plans to predict actions across various timesteps providing ample foresight to the planning. It then employs a differentiable planner with state estimations to derive a value function directing the agent's exploration and goal-seeking behaviors without seeking experts while explicitly addressing partial observability. During inference our policy is further enhanced by a best-plan-selection strategy substantially boosting the planning success rate. Moreover we propose projecting point clouds derived from RGB-D inputs onto 2D grid-based bird-eye-view maps via semantic segmentation generalizing to 3D environments. This simple yet effective adaption enables zero-shot transfer from 2D-trained policy to 3D cutting across the laborious training for 3D policy and thus certifying our versatility. Experimental results demonstrate our superior performance particularly in navigating situations beyond expert demonstrations surpassing state-of-the-art autoregressive and diffusion-based baselines for both 2D and 3D scenarios.
Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation
PDF ↗Distribution shift widely exists in medical images acquired from different medical centres and poses a significant obstacle to deploying the pre-trained semantic segmentation model in real-world applications. Test-time adaptation has proven its effectiveness in tackling the cross-domain distribution shift during inference. However most existing methods achieve adaptation by updating the pre-trained models rendering them susceptible to error accumulation and catastrophic forgetting when encountering a series of distribution shifts (i.e. under the continual test-time adaptation setup). To overcome these challenges caused by updating the models in this paper we freeze the pre-trained model and propose the Visual Prompt-based Test-Time Adaptation (VPTTA) method to train a specific prompt for each test image to align the statistics in the batch normalization layers. Specifically we present the low-frequency prompt which is lightweight with only a few parameters and can be effectively trained in a single iteration. To enhance prompt initialization we equip VPTTA with a memory bank to benefit the current prompt from previous ones. Additionally we design a warm-up mechanism which mixes source and target statistics to construct warm-up statistics thereby facilitating the training process. Extensive experiments demonstrate the superiority of our VPTTA over other state-of-the-art methods on two medical image segmentation benchmark tasks. The code and weights of pre-trained source models are available at https://github.com/Chen-Ziyang/VPTTA.
Classifier-Free Guidance (CFG) has been widely used in text-to-image diffusion models where the CFG scale is introduced to control the strength of text guidance on the whole image space. However we argue that a global CFG scale results in spatial inconsistency on varying semantic strengths and suboptimal image quality. To address this problem we present a novel approach Semantic-aware Classifier-Free Guidance (S-CFG) to customize the guidance degrees for different semantic units in text-to-image diffusion models. Specifically we first design a training-free semantic segmentation method to partition the latent image into relatively independent semantic regions at each denoising step. In particular the cross-attention map in the denoising U-net backbone is renormalized for assigning each patch to the corresponding token while the self-attention map is used to complete the semantic regions. Then to balance the amplification of diverse semantic units we adaptively adjust the CFG scales across different semantic regions to rescale the text guidance degrees into a uniform level. Finally extensive experiments demonstrate the superiority of S-CFG over the original CFG strategy on various text-to-image diffusion models without requiring any extra training cost. our codes are available at https://github.com/SmilesDZgk/S-CFG.
Consistency learning is a central strategy to tackle unlabeled data in semi-supervised medical image segmentation (SSMIS) which enforces the model to produce consistent predictions under the perturbation. However most current approaches solely focus on utilizing a specific single perturbation which can only cope with limited cases while employing multiple perturbations simultaneously is hard to guarantee the quality of consistency learning. In this paper we propose an Adaptive Bidirectional Displacement (ABD) approach to solve the above challenge. Specifically we first design a bidirectional patch displacement based on reliable prediction confidence for unlabeled data to generate new samples which can effectively suppress uncontrollable regions and still retain the influence of input perturbations. Meanwhile to enforce the model to learn the potentially uncontrollable content a bidirectional displacement operation with inverse confidence is proposed for the labeled images which generates samples with more unreliable information to facilitate model learning. Extensive experiments show that ABD achieves new state-of-the-art performances for SSMIS significantly improving different baselines. Source code is available at https://github.com/chy-upc/ABD.
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
PDF ↗A major focus of clinical imaging workflow is disease diagnosis and management leading to medical imaging datasets strongly tied to specific clinical objectives. This scenario has led to the prevailing practice of developing task-specific segmentation models without gaining insights from widespread imaging cohorts. Inspired by the training program of medical radiology residents we propose a shift towards universal medical image segmentation a paradigm aiming to build medical image understanding foundation models by leveraging the diversity and commonality across clinical targets body regions and imaging modalities. Towards this goal we develop Hermes a novel context-prior learning approach to address the challenges of data heterogeneity and annotation differences in medical image segmentation. In a large collection of eleven diverse datasets (2438 3D images) across five modalities (CT PET T1 T2 and cine MRI) and multiple body regions we demonstrate the merit of the universal paradigm over the traditional paradigm on addressing multiple tasks within a single model. By exploiting the synergy across tasks Hermes achieves state-of-the-art performance on all testing datasets and shows superior model scalability. Results on two additional datasets reveals Hermes' strong performance for transfer learning incremental learning and generalization to downstream tasks. Hermes's learned priors demonstrate an appealing trait to reflect the intricate relations among tasks and modalities which aligns with the established anatomical and imaging principles in radiology. The code is available.
This paper studies the problem of concept-based interpretability of transformer representations for videos. Concretely we seek to explain the decision-making process of video transformers based on high-level spatiotemporal concepts that are automatically discovered. Prior research on concept-based interpretability has concentrated solely on image-level tasks. Comparatively video models deal with the added temporal dimension increasing complexity and posing challenges in identifying dynamic concepts over time. In this work we systematically address these challenges by introducing the first Video Transformer Concept Discovery (VTCD) algorithm. To this end we propose an efficient approach for unsupervised identification of units of video transformer representations - concepts and ranking their importance to the output of a model. The resulting concepts are highly interpretable revealing spatio-temporal reasoning mechanisms and object-centric representations in unstructured video models. Performing this analysis jointly over a diverse set of supervised and self-supervised representations we discover that some of these mechanism are universal in video transformers. Finally we show that VTCD can be used for fine-grained action recognition and video object segmentation.
We present WinSyn a unique dataset and testbed for creating high-quality synthetic data with procedural modeling techniques. The dataset contains high-resolution photographs of windows selected from locations around the world with 89318 individual window crops showcasing diverse geometric and material characteristics. We evaluate a procedural model by training semantic segmentation networks on both synthetic and real images and then comparing their performances on a shared test set of real images. Specifically we measure the difference in mean Intersection over Union (mIoU) and determine the effective number of real images to match synthetic data's training performance. We design a baseline procedural model as a benchmark and provide 21290 synthetically generated images. By tuning the procedural model key factors are identified which significantly influence the model's fidelity in replicating real-world scenarios. Importantly we highlight the challenge of procedural modeling using current techniques especially in their ability to replicate the spatial semantics of real-world scenarios. This insight is critical because of the potential of procedural models to bridge hidden scene aspects such as depth reflectivity material properties and lighting conditions.