CVPR 2024 论文列表

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging thin filamentous and widely branching morphologies multiple neurons are tightly inter-weaved and partial volume effects uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing to date methods are typically benchmarked on synthetic datasets. To address this gap we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies and facilitate scientific discovery in basic neuroscience.

We study supervised action segmentation whose goal is to predict framewise action labels of a video. To capture temporal dependencies over long horizons prior works either improve framewise features with transformer or refine framewise predictions with learned action features. However they are computationally costly and ignore that frame and action features contain complimentary information which can be leveraged to enhance both features and improve temporal modeling. Therefore we propose an efficient Frame-Action Cross-attention Temporal modeling (FACT) framework that performs temporal modeling with frame and action features in parallel and leverage this parallelism to achieve iterative bidirectional information transfer between the features and refine them. FACT network contains (i) a frame branch to learn frame-level information with convolutions and frame features (ii) an action branch to learn action-level dependencies with transformers and action tokens and (iii) cross-attentions to allow communication between the two branches. We also propose a new matching loss to ensure each action token uniquely encodes an action segment thus better captures its semantics. Thanks to our architecture we can also leverage textual transcripts of videos to help action segmentation. We evaluate FACT on four video datasets (two egocentric and two third-person) for action segmentation with and without transcripts showing that it significantly improves the state-of-the-art accuracy while enjoys lower computational cost (3 times faster) than existing transformer-based methods

This paper introduces a novel approach to learning instance segmentation using extreme points i.e. the topmost leftmost bottommost and rightmost points of each object. These points are readily available in the modern bounding box annotation process while offering strong clues for precise segmentation and thus allows to improve performance at the same annotation cost with box-supervised methods. Our work considers extreme points as a part of the true instance mask and propagates them to identify potential foreground and background points which are all together used for training a pseudo label generator. Then pseudo labels given by the generator are in turn used for supervised learning of our final model. On three public benchmarks our method significantly outperforms existing box-supervised methods further narrowing the gap with its fully supervised counterpart. In particular our model generates high-quality masks when a target object is separated into multiple parts where previous box-supervised methods often fail.

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.

In the realm of video object segmentation (VOS) the challenge of operating under low-light conditions persists resulting in notably degraded image quality and compromised accuracy when comparing query and memory frames for similarity computation. Event cameras characterized by their high dynamic range and ability to capture motion information of objects offer promise in enhancing object visibility and aiding VOS methods under such low-light conditions. This paper introduces a pioneering framework tailored for low-light VOS leveraging event camera data to elevate segmentation accuracy. Our approach hinges on two pivotal components: the Adaptive Cross-Modal Fusion (ACMF) module aimed at extracting pertinent features while fusing image and event modalities to mitigate noise interference and the Event-Guided Memory Matching (EGMM) module designed to rectify the issue of inaccurate matching prevalent in low-light settings. Additionally we present the creation of a synthetic LLE-DAVIS dataset and the curation of a real-world LLE-VOS dataset encompassing frames and events. Experimental evaluations corroborate the efficacy of our method across both datasets affirming its effectiveness in low-light scenarios. The datasets are available at https://github.com/HebeiFast/EventLowLightVOS.

From image-text pairs large-scale vision-language models (VLMs) learn to implicitly associate image regions with words which prove effective for tasks like visual question answering. However leveraging the learned association for open-vocabulary semantic segmentation remains a challenge. In this paper we propose a simple yet extremely effective training-free technique Plug-and-Play Open-Vocabulary Semantic Segmentation (PnP-OVSS) for this task. PnP-OVSS leverages a VLM with direct text-to-image cross-attention and an image-text matching loss. To balance between over-segmentation and under-segmentation we introduce Salience Dropout; by iteratively dropping patches that the model is most attentive to we are able to better resolve the entire extent of the segmentation mask. PnP-OVSS does not require any neural network training and performs hyperparameter tuning without the need for any segmentation annotations even for a validation set. PnP-OVSS demonstrates substantial improvements over comparable baselines (+29.4% mIoU on Pascal VOC +13.2% mIoU on Pascal Context +14.0% mIoU on MS COCO +2.4% mIoU on COCO Stuff) and even outperforms most baselines that conduct additional network training on top of pretrained VLMs. Our codebase is at https://github.com/letitiabanana/PnP-OVSS.

Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript which is time-consuming and hard to be parallelized while training. In this work we aim to escape from this inefficient alignment with massive but redundant frames and instead to directly localize a few action transitions for pseudo segmentation generation where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed.

While recent 3D instance segmentation approaches show promising results based on transformer architectures they often fail to correctly identify instances with similar appearances. They also ambiguously determine edges leading to multiple misclassifications of adjacent edge points. In this work we introduce a novel framework called EASE to overcome these challenges and improve the perception of complex 3D instances. We first propose a semantic guidance network to leverage rich semantic knowledge from a language model as intelligent priors enhancing the functional understanding of real-world instances beyond relying solely on geometrical information. We explicitly instruct the basic instance queries using text embeddings of each instance to learn deep semantic details. Further we utilize the edge prediction module encouraging the segmentation network to be edge-aware. We extract voxel-wise edge maps from point features and use them as auxiliary information for learning edge cues. In our extensive experiments on large-scale benchmarks ScanNetV2 ScanNet200 S3DIS and STPLS3D our EASE outperforms existing state-of-the-art models demonstrating its superior performance.

An efficient and effective decoding mechanism is crucial in medical image segmentation especially in scenarios with limited computational resources. However these decoding mechanisms usually come with high computational costs. To address this concern we introduce EMCAD a new efficient multi-scale convolutional attention decoder designed to optimize both performance and computational efficiency. EMCAD leverages a unique multi-scale depth-wise convolution block significantly enhancing feature maps through multi-scale convolutions. EMCAD also employs channel spatial and grouped (large-kernel) gated attention mechanisms which are highly effective at capturing intricate spatial relationships while focusing on salient regions. By employing group and depth-wise convolution EMCAD is very efficient and scales well (e.g. only 1.91M parameters and 0.381G FLOPs are needed when using a standard encoder). Our rigorous evaluations across 12 datasets that belong to six medical image segmentation tasks reveal that EMCAD achieves state-of-the-art (SOTA) performance with 79.4% and 80.3% reduction in #Params and #FLOPs respectively. Moreover EMCAD's adaptability to different encoders and versatility across segmentation tasks further establish EMCAD as a promising tool advancing the field towards more efficient and accurate medical image analysis. Our implementation is available at https://github.com/SLDGroup/EMCAD.

The portrait matting task aims to extract an alpha matte with complete semantics and finely detailed contours. In comparison to CNN-based approaches transformers with self-attention module have a better capacity to capture long-range dependencies and low-frequency semantic information of a portrait. However recent research shows that the self-attention mechanism struggles with modeling high-frequency contour information and capturing fine contour details which can lead to bias while predicting the portrait's contours. To deal with this issue we propose EFormer to enhance the model's attention towards both the low-frequency semantic and high-frequency contour features. For the high-frequency contours our research demonstrates that cross-attention module between different resolutions can guide our model to allocate attention appropriately to these contour regions. Supported by this we can successfully extract the high-frequency detail information around the portrait's contours which were previously ignored by self-attention. Based on the cross-attention module we further build a semantic and contour detector (SCD) to accurately capture both the low-frequency semantic and high-frequency contour features. And we design a contour-edge extraction branch and semantic extraction branch to extract refined high-frequency contour features and complete low-frequency semantic information respectively. Finally we fuse the two kinds of features and leverage the segmentation head to generate a predicted portrait matte. Experiments on VideoMatte240K (JPEG SD Format) and Adobe Image Matting (AIM) datasets demonstrate that EFormer outperforms previous portrait matte methods.

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.

Semantic segmentation has innately relied on extensive pixel-level annotated data leading to the emergence of unsupervised methodologies. Among them leveraging self-supervised Vision Transformers for unsupervised semantic segmentation (USS) has been making steady progress with expressive deep features. Yet for semantically segmenting images with complex objects a predominant challenge remains: the lack of explicit object-level semantic encoding in patch-level features. This technical limitation often leads to inadequate segmentation of complex objects with diverse structures. To address this gap we present a novel approach EAGLE which emphasizes object-centric representation learning for unsupervised semantic segmentation. Specifically we introduce EiCue a spectral technique providing semantic and structural cues through an eigenbasis derived from the semantic similarity matrix of deep image features and color affinity from an image. Further by incorporating our object-centric contrastive loss with EiCue we guide our model to learn object-level representations with intra- and inter-image object-feature consistency thereby enhancing semantic accuracy. Extensive experiments on COCO-Stuff Cityscapes and Potsdam-3 datasets demonstrate the state-of-the-art USS results of EAGLE with accurate and consistent semantic segmentation across complex scenes.

Recently One-stage Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained increasing interest due to simplification over its cumbersome multi-stage counterpart. Limited by the inherent ambiguity of Class Activation Map (CAM) we observe that one-stage pipelines often encounter confirmation bias caused by incorrect CAM pseudo-labels impairing their final segmentation performance. Although recent works discard many unreliable pseudo-labels to implicitly alleviate this issue they fail to exploit sufficient supervision for their models. To this end we propose a dual student framework with trustworthy progressive learning (DuPL). Specifically we propose a dual student network with a discrepancy loss to yield diverse CAMs for each sub-net. The two sub-nets generate supervision for each other mitigating the confirmation bias caused by learning their own incorrect pseudo-labels. In this process we progressively introduce more trustworthy pseudo-labels to be involved in the supervision through dynamic threshold adjustment with an adaptive noise filtering strategy. Moreover we believe that every pixel even discarded from supervision due to its unreliability is important for WSSS. Thus we develop consistency regularization on these discarded regions providing supervision of every pixel. Experiment results demonstrate the superiority of the proposed DuPL over the recent state-of-the-art alternatives on PASCAL VOC 2012 and MS COCO datasets. Code is available at https://github.com/Wu0409/DuPL.

Few-shot semantic segmentation (FSS) has achieved great success on segmenting objects of novel classes supported by only a few annotated samples. However existing FSS methods often underperform in the presence of domain shifts especially when encountering new domain styles that are unseen during training. It is suboptimal to directly adapt or generalize the entire model to new domains in the few-shot scenario. Instead our key idea is to adapt a small adapter for rectifying diverse target domain styles to the source domain. Consequently the rectified target domain features can fittingly benefit from the well-optimized source domain segmentation model which is intently trained on sufficient source domain data. Training domain-rectifying adapter requires sufficiently diverse target domains. We thus propose a novel local-global style perturbation method to simulate diverse potential target domains by perturbating the feature channel statistics of the individual images and collective statistics of the entire source domain respectively. Additionally we propose a cyclic domain alignment module to facilitate the adapter effectively rectifying domains using a reverse domain rectification supervision. The adapter is trained to rectify the image features from diverse synthesized target domains to align with the source domain. During testing on target domains we start by rectifying the image features and then conduct few-shot segmentation on the domain-rectified features. Extensive experiments demonstrate the effectiveness of our method achieving promising results on cross-domain few-shot semantic segmentation tasks. Our code is available at https://github.com/Matt-Su/DR-Adapter.

Annotation ambiguity due to inherent data uncertainties such as blurred boundaries in medical scans and different observer expertise and preferences has become a major obstacle for training deep-learning based medical image segmentation models. To address it the common practice is to gather multiple annotations from different experts leading to the setting of multi-rater medical image segmentation. Existing works aim to either merge different annotations into the "groundtruth" that is often unattainable in numerous medical contexts or generate diverse results or produce personalized results corresponding to individual expert raters. Here we bring up a more ambitious goal for multi-rater medical image segmentation i.e. obtaining both diversified and personalized results. Specifically we propose a two-stage framework named D-Persona (first Diversification and then Personalization). In Stage I we exploit multiple given annotations to train a Probabilistic U-Net model with a bound-constrained loss to improve the prediction diversity. In this way a common latent space is constructed in Stage I where different latent codes denote diversified expert opinions. Then in Stage II we design multiple attention-based projection heads to adaptively query the corresponding expert prompts from the shared latent space and then perform the personalized medical image segmentation. We evaluated the proposed model on our in-house Nasopharyngeal Carcinoma dataset and the public lung nodule dataset (i.e. LIDC-IDRI). Extensive experiments demonstrated our D-Persona can provide diversified and personalized results at the same time achieving new SOTA performance for multi-rater medical image segmentation. Our code will be released at https://github.com/ycwu1997/D-Persona.

Instance segmentation is data-hungry and as model capacity increases data scale becomes crucial for improving the accuracy. Most instance segmentation datasets today require costly manual annotation limiting their data scale. Models trained on such data are prone to overfitting on the training set especially for those rare categories. While recent works have delved into exploiting generative models to create synthetic datasets for data augmentation these approaches do not efficiently harness the full potential of generative models. To address these issues we introduce a more efficient strategy to construct generative datasets for data augmentation termed DiverGen. Firstly we provide an explanation of the role of generative data from the perspective of distribution discrepancy. We investigate the impact of different data on the distribution learned by the model. We argue that generative data can expand the data distribution that the model can learn thus mitigating overfitting. Additionally we find that the diversity of generative data is crucial for improving model performance and enhance it through various strategies including category diversity prompt diversity and generative model diversity. With these strategies we can scale the data to millions while maintaining the trend of model performance improvement. On the LVIS dataset DiverGen significantly outperforms the strong model X-Paste achieving +1.1 box AP and +1.1 mask AP across all categories and +1.9 box AP and +2.5 mask AP for rare categories. Our codes are available at https://github.com/aim-uofa/DiverGen.

We present Diff3F as a simple robust and class-agnostic feature descriptor that can be computed for untextured input shapes (meshes or point clouds). Our method distills diffusion features from image foundational models onto input shapes. Specifically we use the input shapes to produce depth and normal maps as guidance for conditional image synthesis. In the process we produce (diffusion) features in 2D that we subsequently lift and aggregate on the original surface. Our key observation is that even if the conditional image generations obtained from multi-view rendering of the input shapes are inconsistent the associated image features are robust and hence can be directly aggregated across views. This produces semantic features on the input shapes without requiring additional data or training. We perform extensive experiments on multiple benchmarks (SHREC'19 SHREC'20 FAUST and TOSCA) and demonstrate that our features being semantic instead of geometric produce reliable correspondence across both isometric and non-isometrically related shape families. Code is available via the project webpage at https://diff3f.github.io/

Densely annotating the large-scale point clouds is laborious. To alleviate the annotation burden contrastive learning has attracted increasing attention for tackling semi-supervised 3D semantic segmentation. However existing point-to-point contrastive learning techniques in literature are generally sensitive to outliers resulting in insufficient modeling of the point-wise representations. To address this problem we propose a method named DDSemi for semi-supervised 3D semantic segmentation where a density-guided contrastive learning technique is explored. This technique calculates the contrastive loss in a point-to-anchor manner by estimating an anchor for each class from the memory bank based on the finding that the cluster centers tend to be located in dense regions. In this technique an inter-contrast loss is derived from the perturbed unlabeled point cloud pairs while an intra-contrast loss is derived from a single unlabeled point cloud. The derived losses could enhance the discriminability of the features and implicitly constrain the semantic consistency between the perturbed unlabeled point cloud pairs. In addition we propose a dual-space hardness sampling strategy to pay more attention to the hard samples located in sparse regions of both the geometric space and feature space by reweighting the point-wise intra-contrast loss. Experimental results on both indoor-scene and outdoor-scene datasets demonstrate that the proposed method outperforms the comparative state-of-the-art semi-supervised methods.

Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars. In this paper we undertake a comprehensive study of CD-FSS and uncover two crucial insights: (i) the necessity of a fine-tuning stage to effectively transfer the learned meta-knowledge across domains and (ii) the overfitting risk during the naive fine-tuning due to the scarcity of novel category examples. With these insights we propose a novel cross-domain fine-tuning strategy that addresses the challenging CD-FSS tasks. We first design Bi-directional Few-shot Prediction (BFP) which establishes support-query correspondence in a bi-directional manner crafting augmented supervision to reduce the overfitting risk. Then we further extend BFP into Iterative Few-shot Adaptor (IFA) which is a recursive framework to capture the support-query correspondence iteratively targeting maximal exploitation of supervisory signals from the sparse novel category samples. Extensive empirical evaluations show that our method significantly outperforms the state-of-the-arts (+7.8%) which verifies that IFA tackles the cross-domain challenges and mitigates the overfitting simultaneously. The code is available at: https://github.com/niejiahao1998/IFA.