CVPR 2024 Papers

This paper presents a simple but performant semi-supervised semantic segmentation approach called CorrMatch. Previous approaches mostly employ complicated training strategies to leverage unlabeled data but overlook the role of correlation maps in modeling the relationships between pairs of locations. We observe that the correlation maps not only enable clustering pixels of the same category easily but also contain good shape information which previous works have omitted. Motivated by these we aim to improve the use efficiency of unlabeled data by designing two novel label propagation strategies. First we propose to conduct pixel propagation by modeling the pairwise similarities of pixels to spread the high-confidence pixels and dig out more. Then we perform region propagation to enhance the pseudo labels with accurate class-agnostic masks extracted from the correlation maps. CorrMatch achieves great performance on popular segmentation benchmarks. Taking the DeepLabV3+ with ResNet-101 backbone as our segmentation model we receive a 76%+ mIoU score on the Pascal VOC 2012 dataset with only 92 annotated images. Code is available at https://github.com/BBBBchan/CorrMatch .

Recently an audio-visual segmentation (AVS) task has been introduced aiming to group pixels with sounding objects within a given video. This task necessitates a first-ever audio-driven pixel-level understanding of the scene posing significant challenges. In this paper we propose an innovative audio-visual transformer framework termed COMBO an acronym for COoperation of Multi-order Bilateral relatiOns. For the first time our framework explores three types of bilateral entanglements within AVS: pixel entanglement modality entanglement and temporal entanglement. Regarding pixel entanglement we employ a Siam-Encoder Module (SEM) that leverages prior knowledge to generate more precise visual features from the foundational model. For modality entanglement we design a Bilateral-Fusion Module (BFM) enabling COMBO to align corresponding visual and auditory signals bi-directionally. As for temporal entanglement we introduce an innovative adaptive inter-frame consistency loss according to the inherent rules of temporal. Comprehensive experiments and ablation studies on AVSBench-object (84.7 mIoU on S4 59.2 mIou on MS3) and AVSBench-semantic (42.1 mIoU on AVSS) datasets demonstrate that COMBO surpasses previous state-of-the-art methods. Project page is available at https://yannqi.github.io/AVS-COMBO.

Most continual segmentation methods tackle the problem as a per-pixel classification task. However such a paradigm is very challenging and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones as objectness has strong transfer ability and forgetting resistance. Based on these findings we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification. CoMasTRe uses a two-stage segmenter learning class-agnostic mask proposals at the first stage and leaving recognition to the second stage. During continual learning a simple but effective distillation is adopted to strengthen objectness. To further mitigate the forgetting of old classes we design a multi-label class distillation strategy suited for segmentation. We assess the effectiveness of CoMasTRe on PASCAL VOC and ADE20K. Extensive experiments show that our method outperforms per-pixel and query-based methods on both datasets. Code will be available at https://github.com/jordangong/CoMasTRe.

Despite great improvements in semantic segmentation challenges persist because of the lack of local/global contexts and the relationship between them. In this paper we propose Contextrast a contrastive learning-based semantic segmentation method that allows to capture local/global contexts and comprehend their relationships. Our proposed method comprises two parts: a) contextual contrastive learning (CCL) and b) boundary-aware negative (BANE) sampling. Contextual contrastive learning obtains local/global context from multi-scale feature aggregation and inter/intra-relationship of features for better discrimination capabilities. Meanwhile BANE sampling selects embedding features along the boundaries of incorrectly predicted regions to employ them as harder negative samples on our contrastive learning resolving segmentation issues along the boundary region by exploiting fine-grained details. We demonstrate that our Contextrast substantially enhances the performance of semantic segmentation networks outperforming state-of-the-art contrastive learning approaches on diverse public datasets e.g. Cityscapes CamVid PASCAL-C COCO-Stuff and ADE20K without an increase in computational cost during inference.

Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods address one of these issues separately. However the coexistence of limited annotation and domain shift is quite common which motivates us to introduce a novel and challenging scenario: Mixed Domain Semi-supervised medical image Segmentation (MiDSS). In this scenario we handle data from multiple medical centers with limited annotations available for a single domain and a large amount of unlabeled data from multiple domains. We found that the key to solving the problem lies in how to generate reliable pseudo labels for the unlabeled data in the presence of domain shift with labeled data. To tackle this issue we employ Unified Copy-Paste (UCP) between images to construct intermediate domains facilitating the knowledge transfer from the domain of labeled data to the domains of unlabeled data. To fully utilize the information within the intermediate domain we propose a symmetric Guidance training strategy (SymGD) which additionally offers direct guidance to unlabeled data by merging pseudo labels from intermediate samples. Subsequently we introduce a Training Process aware Random Amplitude MixUp (TP-RAM) to progressively incorporate style-transition components into intermediate samples. Compared with existing state-of-the-art approaches our method achieves a notable 13.57% improvement in Dice score on Prostate dataset as demonstrated on three public datasets. Our code is available at https://github.com/MQinghe/MiDSS

Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail CLOUDS is a framework that integrates Foundation Models of various kinds: (i) CLIP backbone for its robust feature representation (ii) Diffusion Model to diversify the content thereby covering various modes of the possible target distribution and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions notably outperforming prior methods by 5.6% and 6.7% on averaged mIoU respectively. Our code is available at https://github.com/yasserben/CLOUDS

Data replay is a successful incremental learning technique for images. It prevents catastrophic forgetting by keeping a reservoir of previous data original or synthesized to ensure the model retains past knowledge while adapting to novel concepts. However its application in the video domain is rudimentary as it simply stores frame exemplars for action recognition. This paper presents the first exploration of video data replay techniques for incremental action segmentation focusing on action temporal modeling. We propose a Temporally Coherent Action (TCA) model which represents actions using a generative model instead of storing individual frames. The integration of a conditioning variable that captures temporal coherence allows our model to understand the evolution of action features over time. Therefore action segments generated by TCA for replay are diverse and temporally coherent. In a 10-task incremental setup on the Breakfast dataset our approach achieves significant increases in accuracy for up to 22% compared to the baselines.

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.

Weakly Supervised Semantic Segmentation (WSSS) relies on Class Activation Maps (CAMs) to extract spatial information from image-level labels. With the success of Vision Transformer (ViT) the migration of ViT is actively conducted in WSSS. This work proposes a novel WSSS framework with Class Token Infusion (CTI). By infusing the class tokens from images we guide class tokens to possess class-specific distinct characteristics and global-local consistency. For this we devise two kinds of token infusion: 1) Intra-image Class Token Infusion (I-CTI) and 2) Cross-Image Class Token Infusion (C-CTI). In I-CTI we infuse the class tokens from the same but differently augmented images and thus make CAMs consistent among various deformations (view color). In C-CTI by infusing the class tokens from the other images and imposing the resulting CAMs to be similar it learns class-specific distinct characteristics. Besides the CTI we bring the background (BG) concept into ViT with the BG token to reduce the false positive activation of CAMs. We demonstrate the effectiveness of our method on PASCAL VOC 2012 and MS COCO 2014 datasets achieving state-of-the-art results in weakly supervised semantic segmentation. The code is available at https://github.com/yoon307/CTI

In recent decades the vision community has witnessed remarkable progress in visual recognition partially owing to advancements in dataset benchmarks. Notably the established COCO benchmark has propelled the development of modern detection and segmentation systems. However the COCO segmentation benchmark has seen comparatively slow improvement over the last decade. Originally equipped with coarse polygon annotations for thing instances it gradually incorporated coarse superpixel annotations for stuff regions which were subsequently heuristically amalgamated to yield panoptic segmentation annotations. These annotations executed by different groups of raters have resulted not only in coarse segmentation masks but also in inconsistencies between segmentation types. In this study we undertake a comprehensive reevaluation of the COCO segmentation annotations. By enhancing the annotation quality and expanding the dataset to encompass 383K images with more than 5.18M panoptic masks we introduce COCONut the COCO Next Universal segmenTation dataset. COCONut harmonizes segmentation annotations across semantic instance and panoptic segmentation with meticulously crafted high-quality masks and establishes a robust benchmark for all segmentation tasks. To our knowledge COCONut stands as the inaugural large-scale universal segmentation dataset verified by human raters. We anticipate that the release of COCONut will significantly contribute to the community's ability to assess the progress of novel neural networks.

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions. In this work we introduce a novel cost-based approach to adapt vision-language foundation models notably CLIP for the intricate task of semantic segmentation. Through aggregating the cosine similarity score i.e. the cost volume between image and text embeddings our method potently adapts CLIP for segmenting seen and unseen classes by fine-tuning its encoders addressing the challenges faced by existing methods in handling unseen classes. Building upon this we explore methods to effectively aggregate the cost volume considering its multi-modal nature of being established between image and text embeddings. Furthermore we examine various methods for efficiently fine-tuning CLIP.

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/.

Reverse engineering in the realm of Computer-Aided Design (CAD) has been a longstanding aspiration though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD-SIGNet an end-to-end trainable and auto-regressive architecture to recover the design history of a CAD model represented as a sequence of sketch- and-extrusion from an input point cloud. Our model learns CAD visual-language representations by layer-wise cross-attention between point cloud and CAD language embedding. In particular a new Sketch instance Guided Attention (SGA) module is proposed in order to reconstruct the fine- grained details of the sketches. Thanks to its auto-regressive nature CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an in- put point cloud but also provides multiple plausible design choices. This allows for an interactive reverse engineering scenario by providing designers with multiple next step choices along with the design process. Extensive experiments on publicly available CAD datasets showcase the effectiveness of our approach against existing baseline models in two settings namely full design history recovery and conditional auto-completion from point clouds.

When deploying segmentation models in practice it is critical to evaluate their behaviors in varied and complex scenes. Different from the previous evaluation paradigms only in consideration of global attribute variations (e.g. adverse weather) we investigate both local and global attribute variations for robustness evaluation. To achieve this we construct a mask-preserved attribute editing pipeline to edit visual attributes of real images with precise control of structural information. Therefore the original segmentation labels can be reused for the edited images. Using our pipeline we construct a benchmark covering both object and image attributes (e.g. color material pattern style). We evaluate a broad variety of semantic segmentation models spanning from conventional close-set models to recent open-vocabulary large models on their robustness to different types of variations. We find that both local and global attribute variations affect segmentation performances and the sensitivity of models diverges across different variation types. We argue that local attributes have the same importance as global attributes and should be considered in the robustness evaluation of segmentation models. Code: https://github.com/PRIS-CV/Pascal-EA.

3D instance segmentation (3DIS) is a crucial task but point-level annotations are tedious in fully supervised settings. Thus using bounding boxes (bboxes) as annotations has shown great potential. The current mainstream approach is a two-step process involving the generation of pseudo-labels from box annotations and the training of a 3DIS network with the pseudo-labels. However due to the presence of intersections among bboxes not every point has a determined instance label especially in overlapping areas. To generate higher quality pseudo-labels and achieve more precise weakly supervised 3DIS results we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (BSNet) which devises a novel pseudo-labeler called Simulation-assisted Transformer. The labeler consists of two main components. The first is Simulation-assisted Mean Teacher which introduces Mean Teacher for the first time in this task and constructs simulated samples to assist the labeler in acquiring prior knowledge about overlapping areas. To better model local-global structure we also propose Local-Global Aware Attention as the decoder for teacher and student labelers. Extensive experiments conducted on the ScanNetV2 and S3DIS datasets verify the superiority of our designs.

Audio-visual segmentation (AVS) aims to segment the sounding objects in video frames. Although great progress has been witnessed we experimentally reveal that current methods reach marginal performance gain within the use of the unlabeled frames leading to the underutilization issue. To fully explore the potential of the unlabeled frames for AVS we explicitly divide them into two categories based on their temporal characteristics i.e. neighboring frame (NF) and distant frame (DF). NFs temporally adjacent to the labeled frame often contain rich motion information that assists in the accurate localization of sounding objects. Contrary to NFs DFs have long temporal distances from the labeled frame which share semantic-similar objects with appearance variations. Considering their unique characteristics we propose a versatile framework that effectively leverages them to tackle AVS. Specifically for NFs we exploit the motion cues as the dynamic guidance to improve the objectness localization. Besides we exploit the semantic cues in DFs by treating them as valid augmentations to the labeled frames which are then used to enrich data diversity in a self-training manner. Extensive experimental results demonstrate the versatility and superiority of our method unleashing the power of the abundant unlabeled frames.

Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods train the labeled data with ground truths and unlabeled data with pseudo labels. However the two training flows are separate which allows labeled data to dominate the training process resulting in low-quality pseudo labels and consequently sub-optimal results. To alleviate this issue we present AllSpark which reborns the labeled features from unlabeled ones with the channel-wise cross-attention mechanism. We further introduce a Semantic Memory along with a Channel Semantic Grouping strategy to ensure that unlabeled features adequately represent labeled features. The AllSpark shed new light on the architecture level designs of SSSS rather than framework level which avoids increasingly complicated training pipeline designs. It can also be regarded as a flexible bottleneck module that can be seamlessly integrated into a general transformer-based segmentation model. The proposed AllSpark outperforms existing methods across all evaluation protocols on Pascal Cityscapes and COCO benchmarks without bells-and-whistles. Code and model weights are available at: https://github.com/xmed-lab/AllSpark.