CVPR 2024 Papers

In recent years semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet a prevalent limitation of supervised learning techniques remains the need for extensive manual annotations by experts. In this work we explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. The main idea is to learn the joint data manifold of images and labels leveraging recent advancements in denoising diffusion probabilistic models. To the best of our knowledge we are the first to generate both images and corresponding masks for satellite segmentation. We find that the obtained pairs not only display high quality in fine-scale features but also ensure a wide sampling diversity. Both aspects are crucial for earth observation data where semantic classes can vary severely in scale and occurrence frequency. We employ the novel data instances for downstream segmentation as a form of data augmentation. In our experiments we provide comparisons to prior works based on discriminative diffusion models or GANs. We demonstrate that integrating generated samples yields significant quantitative improvements for satellite semantic segmentation -- both compared to baselines and when training only on the original data.

Once only a few-shot annotated samples are available the performance of learning-based object detection would be heavily dropped. Many few-shot object detection (FSOD) methods have been proposed to tackle this issue by adopting image-level augmentations in linear manners. Nevertheless those handcrafted enhancements often suffer from limited diversity and lack of semantic awareness resulting in unsatisfactory performance. To this end we propose a Semantic-guided Non-linear Instance-level Data Augmentation method (SNIDA) for FSOD by decoupling the foreground and background to increase their diversities respectively. We design a semantic awareness enhancement strategy to separate objects from backgrounds. Concretely masks of instances are extracted by an unsupervised semantic segmentation module. Then the diversity of samples would be improved by fusing instances into different backgrounds. Considering the shortcomings of augmenting images in a limited transformation space of existing traditional data augmentation methods we introduce an object reconstruction enhancement module. The aim of this module is to generate sufficient diversity and non-linear training data at the instance level through a semantic-guided masked autoencoder. In this way the potential of data can be fully exploited in various object detection scenarios. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that the proposed method outperforms baselines by a large margin and achieves new state-of-the-art results under different shot settings.

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.

This paper aims at achieving fine-grained building attribute segmentation in a cross-view scenario i.e. using satellite and street-view image pairs. The main challenge lies in overcoming the significant perspective differences between street views and satellite views. In this work we introduce SG-BEV a novel approach for satellite-guided BEV fusion for cross-view semantic segmentation. To overcome the limitations of existing cross-view projection methods in capturing the complete building facade features we innovatively incorporate Bird's Eye View (BEV) method to establish a spatially explicit mapping of street-view features. Moreover we fully leverage the advantages of multiple perspectives by introducing a novel satellite-guided reprojection module optimizing the uneven feature distribution issues associated with traditional BEV methods. Our method demonstrates significant improvements on four cross-view datasets collected from multiple cities including New York San Francisco and Boston. On average across these datasets our method achieves an increase in mIOU by 10.13% and 5.21% compared with the state-of-the-art satellite-based and cross-view methods. The code and datasets of this work will be released at https://github.com/sysu-liweijia-lab/SG-BEV.

Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-trained vision-language models in which the key is to adopt the image-level model for pixel-level segmentation task. In this paper we propose a simple encoder-decoder named SED for open-vocabulary semantic segmentation which comprises a hierarchical encoder-based cost map generation and a gradual fusion decoder with category early rejection. The hierarchical encoder-based cost map generation employs hierarchical backbone instead of plain transformer to predict pixel-level image-text cost map. Compared to plain transformer hierarchical backbone better captures local spatial information and has linear computational complexity with respect to input size. Our gradual fusion decoder employs a top-down structure to combine cost map and the feature maps of different backbone levels for segmentation. To accelerate inference speed we introduce a category early rejection scheme in the decoder that rejects many no-existing categories at the early layer of decoder resulting in at most 4.7 times acceleration without accuracy degradation. Experiments are performed on multiple open-vocabulary semantic segmentation datasets which demonstrates the efficacy of our SED method. When using ConvNeXt-B our SED method achieves mIoU score of 31.6% on ADE20K with 150 categories at 82 millisecond (ms) per image on a single A6000. Our source code is available at https://github.com/xb534/SED.

Advancements in 3D instance segmentation have traditionally been tethered to the availability of annotated datasets limiting their application to a narrow spectrum of object categories. Recent efforts have sought to harness vision-language models like CLIP for open-set semantic reasoning yet these methods struggle to distinguish between objects of the same categories and rely on specific prompts that are not universally applicable. In this paper we introduce SAI3D a novel zero-shot 3D instance segmentation approach that synergistically leverages geometric priors and semantic cues derived from Segment Anything Model (SAM). Our method partitions a 3D scene into geometric primitives which are then progressively merged into 3D instance segmentations that are consistent with the multi-view SAM masks. Moreover we design a hierarchical region-growing algorithm with a dynamic thresholding mechanism which largely improves the robustness of fine-grained 3D scene parsing. Empirical evaluations on ScanNet Matterport3D and the more challenging ScanNet++ datasets demonstrate the superiority of our approach. Notably SAI3D outperforms existing open-vocabulary baselines and even surpasses fully-supervised methods in class-agnostic segmentation on ScanNet++. Our project page is at https://yd-yin.github.io/SAI3D/.

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.

The goal of interactive image segmentation is to delineate specific regions within an image via visual or language prompts. Low-latency and high-quality interactive segmentation with diverse prompts remain challenging for existing specialist and generalist models. Specialist models with their limited prompts and task-specific designs experience high latency because the image must be recomputed every time the prompt is updated due to the joint encoding of image and visual prompts. Generalist models exemplified by the Segment Anything Model (SAM) have recently excelled in prompt diversity and efficiency lifting image segmentation to the foundation model era. However for high-quality segmentations SAM still lags behind state-of-the-art specialist models despite SAM being trained with x100 more segmentation masks. In this work we delve deep into the architectural differences between the two types of models. We observe that dense representation and fusion of visual prompts are the key design choices contributing to the high segmentation quality of specialist models. In light of this we reintroduce this dense design into the generalist models to facilitate the development of generalist models with high segmentation quality. To densely represent diverse visual prompts we propose to use a dense map to capture five types: clicks boxes polygons scribbles and masks. Thus we propose SegNext a next-generation interactive segmentation approach offering low latency high quality and diverse prompt support. Our method outperforms current state-of-the-art methods on HQSeg-44K and DAVIS quantitatively and qualitatively.

This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS) with a focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. The former arises from non-uniform point sampling allowing models to distinguish the density disparities between foreground and background for easier segmentation. The latter results from sampling only 2048 points limiting semantic information and deviating from the real-world practice. To address these issues we introduce a standardized FS-PCS setting upon which a new benchmark is built. Moreover we propose a novel FS-PCS model. While previous methods are based on feature optimization by mainly refining support features to enhance prototypes our method is based on correlation optimization referred to as Correlation Optimization Segmentation (COSeg). Specifically we compute Class-specific Multi-prototypical Correlation (CMC) for each query point representing its correlations to category prototypes. Then we propose the Hyper Correlation Augmentation (HCA) module to enhance CMC. Furthermore tackling the inherent property of few-shot training to incur base susceptibility for models we propose to learn non-parametric prototypes for the base classes during training. The learned base prototypes are used to calibrate correlations for the background class through a Base Prototypes Calibration (BPC) module. Experiments on popular datasets demonstrate the superiority of COSeg over existing methods. The code is available at github.com/ZhaochongAn/COSeg.

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.

With recent video object segmentation (VOS) benchmarks evolving to challenging scenarios we revisit a simple but overlooked strategy: restricting the size of memory banks. This diverges from the prevalent practice of expanding memory banks to accommodate extensive historical information. Our specially designed "memory deciphering" study offers a pivotal insight underpinning such a strategy: expanding memory banks while seemingly beneficial actually increases the difficulty for VOS modules to decode relevant features due to the confusion from redundant information. By restricting memory banks to a limited number of essential frames we achieve a notable improvement in VOS accuracy. This process balances the importance and freshness of frames to maintain an informative memory bank within a bounded capacity. Additionally restricted memory banks reduce the training-inference discrepancy in memory lengths compared with continuous expansion. This fosters new opportunities in temporal reasoning and enables us to introduce the previously overlooked "temporal positional embedding." Finally our insights are embodied in "RMem" ("R" for restricted) a simple yet effective VOS modification that excels at challenging VOS scenarios and establishes new state of the art for object state changes (VOST dataset) and long videos (the Long Videos dataset). Our codes are available at https://github.com/Restricted-Memory/RMemand our demo can be watched on https://youtu.be/V3tCFQsJrrM.

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.

We present Cutie a video object segmentation (VOS) network with object-level memory reading which puts the object representation from memory back into the video object segmentation result. Recent works on VOS employ bottom-up pixel-level memory reading which struggles due to matching noise especially in the presence of distractors resulting in lower performance in more challenging data. In contrast Cutie performs top-down object-level memory reading by adapting a small set of object queries. Via those it interacts with the bottom-up pixel features iteratively with a query-based object transformer (qt hence Cutie). The object queries act as a high-level summary of the target object while high-resolution feature maps are retained for accurate segmentation. Together with foreground-background masked attention Cutie cleanly separates the semantics of the foreground object from the background. On the challenging MOSE dataset Cutie improves by 8.7 J&F over XMem with a similar running time and improves by 4.2 J&F over DeAOT while being three times faster. Code is available at: hkchengrex.github.io/Cutie

Referring image segmentation (RIS) aims to segment the target referent described by natural language. Recently large-scale pre-trained models e.g. CLIP and SAM have been successfully applied in many downstream tasks but they are not well adapted to RIS task due to inter-task differences. In this paper we propose a new prompt-driven framework named Prompt-RIS which bridges CLIP and SAM end-to-end and transfers their rich knowledge and powerful capabilities to RIS task through prompt learning. To adapt CLIP to pixel-level task we first propose a Cross-Modal Prompting method which acquires more comprehensive vision-language interaction and fine-grained text-to-pixel alignment by performing bidirectional prompting. Then the prompt-tuned CLIP generates masks points and text prompts for SAM to generate more accurate mask predictions. Moreover we further propose Instance Contrastive Learning to improve the model's discriminability to different instances and robustness to diverse languages describing the same instance. Extensive experiments demonstrate that the performance of our method outperforms the state-of-the-art methods consistently in both general and open-vocabulary settings.

We address the problem of online action segmentation for egocentric procedural task videos. While previous studies have mostly focused on offline action segmentation where entire videos are available for both training and inference the transition to online action segmentation is crucial for practical applications like AR/VR task assistants. Notably applying an offline-trained model directly to online inference results in a significant performance drop due to the inconsistency between training and inference. We propose an online action segmentation framework by first modifying existing architectures to make them causal. Second we develop a novel action progress prediction module to dynamically estimate the progress of ongoing actions and using them to refine the predictions of causal action segmentation. Third we propose to learn task graphs from training videos and leverage them to obtain smooth and procedure-consistent segmentations. With the combination of progress and task graph with casual action segmentation our framework effectively addresses prediction uncertainty and oversegmentation in online action segmentation and achieves significant improvement on three egocentric datasets.

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.

Computer-Aided Design (CAD) model reconstruction from point clouds is an important problem at the intersection of computer vision graphics and machine learning; it saves the designer significant time when iterating on in-the-wild objects. Recent advancements in this direction achieve relatively reliable semantic segmentation but still struggle to produce an adequate topology of the CAD model. In this work we analyze the current state of the art for that ill-posed task and identify shortcomings of existing methods. We propose a hybrid analytic-neural reconstruction scheme that bridges the gap between segmented point clouds and structured CAD models and can be readily combined with different segmentation backbones. Moreover to power the surface fitting stage we propose a novel implicit neural representation of freeform surfaces driving up the performance of our overall CAD reconstruction scheme. We extensively evaluate our method on the popular ABC benchmark of CAD models and set a new state-of-the-art for that dataset. Code is available at https://github.com/YujiaLiu76/point2cad.

Current state-of-the-art Video Object Segmentation (VOS) methods rely on dense per-object mask annotations both during training and testing. This requires time-consuming and costly video annotation mechanisms. We propose a novel Point-VOS task with a spatio-temporally sparse point-wise annotation scheme that substantially reduces the annotation effort. We apply our annotation scheme to two large-scale video datasets with text descriptions and annotate over 19M points across 133K objects in 32K videos. Based on our annotations we propose a new Point-VOS benchmark and a corresponding point-based training mechanism which we use to establish strong baseline results. We show that existing VOS methods can easily be adapted to leverage our point annotations during training and can achieve results close to the fully-supervised performance when trained on pseudo-masks generated from these points. In addition we show that our data can be used to improve models that connect vision and language by evaluating it on the Video Narrative Grounding (VNG) task. We will make our code and annotations available at https://pointvos.github.io.

This paper proposes a cross-modal distillation framework PartDistill which transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D shape part segmentation. PartDistill addresses three major challenges in this task: the lack of 3D segmentation in invisible or undetected regions in the 2D projections inconsistent 2D predictions by VLMs and the lack of knowledge accumulation across different 3D shapes. PartDistill consists of a teacher network that uses a VLM to make 2D predictions and a student network that learns from the 2D predictions while extracting geometrical features from multiple 3D shapes to carry out 3D part segmentation. A bi-directional distillation including forward and backward distillations is carried out within the framework where the former forward distills the 2D predictions to the student network and the latter improves the quality of the 2D predictions which subsequently enhances the final 3D segmentation. Moreover PartDistill can exploit generative models that facilitate effortless 3D shape creation for generating knowledge sources to be distilled. Through extensive experiments PartDistill boosts the existing methods with substantial margins on widely used ShapeNetPart and PartNetE datasets by more than 15% and 12% higher mIoU scores respectively. The code for this work is available at https://github.com/ardianumam/PartDistill.