Text-to-image diffusion models produce high quality images but do not offer control over individual instances in the image. We introduce InstanceDiffusion that adds precise instance-level control to text-to-image diffusion models. InstanceDiffusion supports free-form language conditions per instance and allows flexible ways to specify instance locations such as simple single points scribbles bounding boxes or intricate instance segmentation masks and combinations thereof. We propose three major changes to text-to-image models that enable precise instance-level control. Our UniFusion block enables instance-level conditions for text-to-image models the ScaleU block improves image fidelity and our Multi-instance Sampler improves generations for multiple instances. InstanceDiffusion significantly surpasses specialized state-of-the-art models for each location condition. Notably on the COCO dataset we outperform previous state-of-the-art by 20.4% AP50box for box inputs and 25.4% IoU for mask inputs.
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We propose SNI-SLAM a semantic SLAM system utilizing neural implicit representation that simultaneously performs accurate semantic mapping high-quality surface reconstruction and robust camera tracking. In this system we introduce hierarchical semantic representation to allow multi-level semantic comprehension for top-down structured semantic mapping of the scene. In addition to fully utilize the correlation between multiple attributes of the environment we integrate appearance geometry and semantic features through cross-attention for feature collaboration. This strategy enables a more multifaceted understanding of the environment thereby allowing SNI-SLAM to remain robust even when single attribute is defective. Then we design an internal fusion-based decoder to obtain semantic RGB Truncated Signed Distance Field (TSDF) values from multi-level features for accurate decoding. Furthermore we propose a feature loss to update the scene representation at the feature level. Compared with low-level losses such as RGB loss and depth loss our feature loss is capable of guiding the network optimization on a higher-level. Our SNI-SLAM method demonstrates superior performance over all recent NeRF-based SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets while also showing excellent capabilities in accurate semantic segmentation and real-time semantic mapping. Codes will be available at https://github.com/IRMVLab/SNI-SLAM.
WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects Under Occlusion
PDF ↗Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion. In this work we introduce a novel framework for automatically generating a large realistic dataset of dynamic objects under occlusions using freely available time-lapse imagery. By leveraging off-the-shelf 2D (bounding box segmentation keypoint) and 3D (pose shape) predictions as pseudo-groundtruth unoccluded 3D objects are identified automatically and composited into the background in a clip-art style ensuring realistic appearances and physically accurate occlusion configurations. The resulting clip-art image with pseudo-groundtruth enables efficient training of object reconstruction methods that are robust to occlusions. Our method demonstrates significant improvements in both 2D and 3D reconstruction particularly in scenarios with heavily occluded objects like vehicles and people in urban scenes.
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation
PDF ↗Semi-supervised semantic segmentation allows model to mine effective supervision from unlabeled data to complement label-guided training. Recent research has primarily focused on consistency regularization techniques exploring perturbation-invariant training at both the image and feature levels. In this work we proposed a novel feature-level consistency learning framework named Density-Descending Feature Perturbation (DDFP). Inspired by the low-density separation assumption in semi-supervised learning our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore which is the regions with lower density. We propose to shift features with confident predictions towards lower-density regions by perturbation injection. The perturbed features are then supervised by the predictions on the original features thereby compelling the classifier to explore less dense regions to effectively regularize the decision boundary. Central to our method is the estimation of feature density. To this end we introduce a lightweight density estimator based on normalizing flow allowing for efficient capture of the feature density distribution in an online manner. By extracting gradients from the density estimator we can determine the direction towards less dense regions for each feature. The proposed DDFP outperforms other designs on feature-level perturbations and shows state of the art performances on both Pascal VOC and Cityscapes dataset under various partition protocols. The project is available at https://github.com/Gavinwxy/DDFP.
Panoptic segmentation combining semantic and instance segmentation stands as a cutting-edge computer vision task. Despite recent progress with deep learning models the dynamic nature of real-world applications necessitates continual learning where models adapt to new classes (plasticity) over time without forgetting old ones (catastrophic forgetting). Current continual segmentation methods often rely on distillation strategies like knowledge distillation and pseudo-labeling which are effective but result in increased training complexity and computational overhead. In this paper we introduce a novel and efficient method for continual panoptic segmentation based on Visual Prompt Tuning dubbed ECLIPSE. Our approach involves freezing the base model parameters and fine-tuning only a small set of prompt embeddings addressing both catastrophic forgetting and plasticity and significantly reducing the trainable parameters. To mitigate inherent challenges such as error propagation and semantic drift in continual segmentation we propose logit manipulation to effectively leverage common knowledge across the classes. Experiments on ADE20K continual panoptic segmentation benchmark demonstrate the superiority of ECLIPSE notably its robustness against catastrophic forgetting and its reasonable plasticity achieving a new state-of-the-art. The code is available at https://github.com/clovaai/ECLIPSE.
Superpixels play a crucial role in image processing by partitioning an image into clusters of pixels with similar visual attributes. This facilitates subsequent image processing tasks offering computational advantages over the manipulation of individual pixels. While numerous oversegmentation techniques have emerged in recent years many rely on predefined initialization and termination criteria. In this paper a novel top-down superpixel segmentation algorithm called Hierarchical Histogram Threshold Segmentation (HHTS) is introduced. It eliminates the need for initialization and implements auto-termination outperforming state-of-the-art methods w.r.t boundary recall. This is achieved by iteratively partitioning individual pixel segments into foreground and background and applying intensity thresholding across multiple color channels. The underlying iterative process constructs a superpixel hierarchy that adapts to local detail distributions until color information exhaustion. Experimental results demonstrate the superiority of the proposed approach in terms of boundary adherence while maintaining competitive runtime performance on the BSDS500 and NYUV2 datasets. Furthermore an application of HHTS in refining machine learning-based semantic segmentation masks produced by the Segment Anything Foundation Model (SAM) is presented.
A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net a one-of-a-kind framework for fulfilling multi-task multi-dataset multi-modality LiDAR segmentation in a universal manner using just a single set of parameters. To better exploit data volume and diversity we first combine large-scale driving datasets acquired by different types of sensors from diverse scenes and then conduct alignments in three spaces namely data feature and label spaces during the training. As a result M3Net is capable of taming heterogeneous data for training state-of-the-art LiDAR segmentation models. Extensive experiments on twelve LiDAR segmentation datasets verify our effectiveness. Notably using a shared set of parameters M3Net achieves 75.1% 83.1% and 72.4% mIoU scores respectively on the official benchmarks of SemanticKITTI nuScenes and Waymo Open.
Sketch semantic segmentation is a well-explored and pivotal problem in computer vision involving the assignment of predefined part labels to individual strokes. This paper presents ContextSeg - a simple yet highly effective approach to tackling this problem with two stages. In the first stage to better encode the shape and positional information of strokes we propose to predict an extra dense distance field in an autoencoder network to reinforce structural information learning. In the second stage we treat an entire stroke as a single entity and label a group of strokes within the same semantic part using an autoregressive Transformer with the default attention mechanism. By group-based labeling our method can fully leverage the context information when making decisions for the remaining groups of strokes. Our method achieves the best segmentation accuracy compared with state-of-the-art approaches on two representative datasets and has been extensively evaluated demonstrating its superior performance. Additionally we offer insights into solving part imbalance in training data and the preliminary experiment on cross-category training which can inspire future research in this field.
The long-tailed distribution problem in medical image analysis reflects a high prevalence of common conditions and a low prevalence of rare ones which poses a significant challenge in developing a unified model capable of identifying rare or novel tumor categories not encountered during training. In this paper we propose a new Zero-shot Pan-Tumor segmentation framework (ZePT) based on query-disentangling and self-prompting to segment unseen tumor categories beyond the training set. ZePT disentangles the object queries into two subsets and trains them in two stages. Initially it learns a set of fundamental queries for organ segmentation through an object-aware feature grouping strategy which gathers organ-level visual features. Subsequently it refines the other set of advanced queries that focus on the auto-generated visual prompts for unseen tumor segmentation. Moreover we introduce query-knowledge alignment at the feature level to enhance each query's discriminative representation and generalizability. Extensive experiments on various tumor segmentation tasks demonstrate the performance superiority of ZePT which surpasses the previous counterparts and evidences the promising ability for zero-shot tumor segmentation in real-world settings.
CAD programs are a popular way to compactly encode shapes as a sequence of operations that are easy to parametrically modify. However without sufficient semantic comments and structure such programs can be challenging to understand let alone modify. We introduce the problem of semantic commenting CAD programs wherein the goal is to segment the input program into code blocks corresponding to semantically meaningful shape parts and assign a semantic label to each block. We solve the problem by combining program parsing with visual-semantic analysis afforded by recent advances in foundational language and vision models. Specifically by executing the input programs we create shapes which we use to generate conditional photorealistic images to make use of semantic annotators for such images. We then distill the information across the images and link back to the original programs to semantically comment on them. Additionally we collected and annotated a benchmark dataset CADTalk consisting of 5288 machine-made programs and 45 human-made programs with ground truth semantic comments. We extensively evaluated our approach compared it to a GPT-based baseline and an open-set shape segmentation baseline and reported an 83.24% accuracy on the new CADTalk dataset. Code and data: https://enigma-li.github.io/CADTalk/.
Event-based semantic segmentation has gained popularity due to its capability to deal with scenarios under high-speed motion and extreme lighting conditions which cannot be addressed by conventional RGB cameras. Since it is hard to annotate event data previous approaches rely on event-to-image reconstruction to obtain pseudo labels for training. However this will inevitably introduce noise and learning from noisy pseudo labels especially when generated from a single source may reinforce the errors. This drawback is also called confirmation bias in pseudo-labeling. In this paper we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation HPL-ESS to alleviate the influence of noisy pseudo labels. In particular we first employ a plain unsupervised domain adaptation framework as our baseline which can generate a set of pseudo labels through self-training. Then we incorporate offline event-to-image reconstruction into the framework and obtain another set of pseudo labels by predicting segmentation maps on the reconstructed images. A noisy label learning strategy is designed to mix the two sets of pseudo labels and enhance the quality. Moreover we propose a soft prototypical alignment module to further improve the consistency of target domain features. Extensive experiments show that our proposed method outperforms existing state-of-the-art methods by a large margin on the DSEC-Semantic dataset (+5.88% accuracy +10.32% mIoU) which even surpasses several supervised methods.
Polarization is a fundamental property of light that encodes abundant information regarding surface shape material illumination and viewing geometry. The computer vision community has witnessed a blossom of polarization-based vision applications such as reflection removal shape-from-polarization (SfP) transparent object segmentation and color constancy partially due to the emergence of single-chip mono/color polarization sensors that make polarization data acquisition easier than ever. However is polarization-based vision vulnerable to adversarial attacks? If so is that possible to realize these adversarial attacks in the physical world without being perceived by human eyes? In this paper we warn the community of the vulnerability of polarization-based vision which can be more serious than RGB-based vision. By adapting a commercial LCD projector we achieve locally controllable polarizing projection which is successfully utilized to fool state-of-the-art polarization-based vision algorithms for glass segmentation and SfP. Compared with existing physical attacks on RGB-based vision which always suffer from the trade-off between attack efficacy and eye conceivability the adversarial attackers based on polarizing projection are contact-free and visually imperceptible since naked human eyes can rarely perceive the difference of viciously manipulated polarizing light and ordinary illumination. This poses unprecedented risks on polarization-based vision for which due attentions should be paid and counter measures be considered.
In the field of computer vision Vision Transformers (ViTs) have emerged as a prominent deep learning architecture. Despite being inspired by Convolutional Neural Networks (CNNs) ViTs are susceptible to small spatial shifts in the input data - they lack shift-equivariance. To address this shortcoming we introduce novel data-adaptive designs for each of the ViT modules that break shift-equivariance such as tokenization self-attention patch merging and positional encoding. With our proposed modules we achieve perfect circular shift-equivariance across four prominent ViT architectures: Swin SwinV2 CvT and MViTv2. Additionally we leverage our design to further enhance consistency under standard shifts. We evaluate our adaptive ViT models on image classification and semantic segmentation tasks. Our models achieve competitive performance across three diverse datasets showcasing perfect (100%) circular shift consistency while improving standard shift consistency.
This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP) a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology enriches vision language models by leveraging extensive data without needing ground truth annotations. CPLIP involves constructing a pathology-specific dictionary generating textual descriptions for images using language models and retrieving relevant images for each text snippet via a pre-trained model. The model is then fine-tuned using a many-to-many contrastive learning method to align complex interrelated concepts across both modalities. Evaluated across multiple histopathology tasks CPLIP shows notable improvements in zero-shot learning scenarios outperforming existing methods in both interpretability and robustness and setting a higher benchmark for the application of vision-language models in the field. To encourage further research and replication the code for CPLIP is available on GitHubat https://cplip.github.io/
Neural 3D reconstruction from multi-view images has recently attracted increasing attention from the community. Existing methods normally learn a neural field for the whole scene while it is still under-explored how to reconstruct a target object indicated by users. Considering the Segment Anything Model (SAM) has shown effectiveness in segmenting any 2D images in this paper we propose NTO3D a novel high-quality Neural Target Object 3D (NTO3D) reconstruction method which leverages the benefits of both neural field and SAM. We first propose a novel strategy to lift the multi-view 2D segmentation masks of SAM into a unified 3D occupancy field. The 3D occupancy field is then projected into 2D space and generates the new prompts for SAM. This process is iterative until convergence to separate the target object from the scene. After this we then lift the 2D features of the SAM encoder into a 3D feature field in order to improve the reconstruction quality of the target object. NTO3D lifts the 2D masks and features of SAM into the 3D neural field for high-quality neural target object 3D reconstruction. We conduct detailed experiments on several benchmark datasets to demonstrate the advantages of our method. The code will be available at: https://github.com/ucwxb/NTO3D.
In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities but this idea is less explored in the vision domain. Existing visual prompting methods focus on referring segmentation to segment the most relevant object falling short of addressing many generic vision tasks like open-set segmentation and detection. In this paper we introduce a universal visual in-context prompting framework for both tasks as shown in Fig.1. In particular we build on top of an encoder-decoder architecture and develop a versatile prompt encoder to support a variety of prompts like strokes boxes and points. We further enhance it to take an arbitrary number of reference image segments as the context. Our extensive explorations show that the proposed visual in-context prompting elicits extraordinary referring and generic segmentation capabilities to refer and detect yielding competitive performance to close-set in-domain datasets and showing promising results on many open-set segmentation datasets. By joint training on COCO and SA-1B DINOv achieves 57.7 PQ on COCO and 23.2 PQ on ADE20K. Code will be available at https://github.com/UX-Decoder/DINOv
Semantic instance and panoptic segmentation of 3D point clouds have been addressed using task-specific models of distinct design. Thereby the similarity of all segmentation tasks and the implicit relationship between them have not been utilized effectively. This paper presents a unified simple and effective model addressing all these tasks jointly. The model named OneFormer3D performs instance and semantic segmentation consistently using a group of learnable kernels where each kernel is responsible for generating a mask for either an instance or a semantic category. These kernels are trained with a transformer-based decoder with unified instance and semantic queries passed as an input. Such a design enables training a model end-to-end in a single run so that it achieves top performance on all three segmentation tasks simultaneously. Specifically our OneFormer3D ranks 1st and sets a new state-of-the-art (+2.1 mAP50) in the ScanNet test leaderboard. We also demonstrate the state-of-the-art results in semantic instance and panoptic segmentation of ScanNet (+21 PQ) ScanNet200 (+3.8 mAP50) and S3DIS (+0.8 mIoU) datasets.
QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition
PDF ↗Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and background disturbances involved establishing robust correspondences between audio and visual contents poses unique challenges due to (1) complex entanglement across sound sources and (2) frequent changes in the occurrence of distinct sound events. Assuming sound events occur independently the multi-source semantic space can be represented as the Cartesian product of single-source sub-spaces. We are motivated to decompose the multi-source audio semantics into single-source semantics for more effective interactions with visual content. We propose a semantic decomposition method based on product quantization where the multi-source semantics can be decomposed and represented by several disentangled and noise-suppressed single-source semantics. Furthermore we introduce a global-to-local quantization mechanism which distills knowledge from stable global (clip-level) features into local (frame-level) ones to handle frequent changes in audio semantics. Extensive experiments demonstrate that our semantically decomposed audio representation significantly improves AVS performance eg +21.2% mIoU on the challenging AVS-Semantic benchmark with ResNet50 backbone.
Referring Image Segmentation (RIS) is a challenging task that requires an algorithm to segment objects referred by free-form language expressions. Despite significant progress in recent years most state-of-the-art (SOTA) methods still suffer from considerable language-image modality gap at the pixel and word level. These methods generally 1) rely on sentence-level language features for language-image alignment and 2) lack explicit training supervision for fine-grained visual grounding. Consequently they exhibit weak object-level correspondence between visual and language features. Without well-grounded features prior methods struggle to understand complex expressions that require strong reasoning over relationships among multiple objects especially when dealing with rarely used or ambiguous clauses. To tackle this challenge we introduce a novel Mask Grounding auxiliary task that significantly improves visual grounding within language features by explicitly teaching the model to learn fine-grained correspondence between masked textual tokens and their matching visual objects. Mask Grounding can be directly used on prior RIS methods and consistently bring improvements. Furthermore to holistically address the modality gap we also design a cross-modal alignment loss and an accompanying alignment module. These additions work synergistically with Mask Grounding. With all these techniques our comprehensive approach culminates in MagNet (Mask-grounded Network) an architecture that significantly outperforms prior arts on three key benchmarks (RefCOCO RefCOCO+ and G-Ref) demonstrating our method's effectiveness in addressing current limitations of RIS algorithms. Our code and pre-trained weights will be released.
Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature maps from the frozen visual encoder to compute the pixel-wise similarity as a key prior guidance for the decoder. However such a prior representation suffers from coarse granularity and poor generalization to new classes since these high-level feature maps have obvious category bias. In this work we propose to replace the visual prior representation with the visual-text alignment capacity to capture more reliable guidance and enhance the model generalization. Specifically we design two kinds of training-free prior information generation strategy that attempts to utilize the semantic alignment capability of the Contrastive Language-Image Pre-training model (CLIP) to locate the target class. Besides to acquire more accurate prior guidance we build a high-order relationship of attention maps and utilize it to refine the initial prior information. Experiments on both the PASCAL-5i and COCO-20i datasets show that our method obtains a clearly substantial improvement and reaches the new state-of-the-art performance. The code is available on the project website.