GenAI4ELab/papercli-papers huggingface dataset:
CAR: Class-Aware Regularizations for Semantic Segmentation
Authors: Ye Huang; Di Kang; Liang Chen; Xuefei Zhe; Wenjing Jia; Linchao Bao; Xiangjian He
Venue: ECCV 2022 (main)

Abstract: "Recent segmentation methods, such as OCR and CPNet, utilizing “class level” information in addition to pixel features, have achieved notable success for boosting the accuracy of existing network modules. However, the extracted class-level information was simply concatenated to pixel features, without explicitly being exploited for better pixel representation learning. Moreover, these approaches learn soft class centers based on coarse mask prediction, which is prone to error accumulation. In this paper, aiming to use class level information more effectively, we propose a universal Class-Aware Regularization (CAR) approach to optimize the intra-class variance and inter-class distance during feature learning, motivated by the fact that humans can recognize an object by itself no matter which other objects it appears with. Three novel loss functions are proposed. The first loss function encourages more compact class representations within each class, the second directly maximizes the distance between different class centers, and the third further pushes the distance between inter-class centers and pixels. Furthermore, the class center in our approach is directly generated from ground truth instead of from the error-prone coarse prediction. Our method can be easily applied to most existing segmentation models during training, including OCR and CPNet, and can largely improve their accuracy at no additional inference overhead. Extensive experiments and ablation studies conducted on multiple benchmark datasets demonstrate that the proposed CAR can boost the accuracy of all baseline models by up to 2.23% mIOU with superior generalization ability. The complete code is available at https://github.com/edwardyehuang/CAR."
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation
Authors: Yuyang Zhao; Zhun Zhong; Na Zhao; Nicu Sebe; Gim Hee Lee
Venue: ECCV 2022 (main)

Abstract: "In this paper, we study the task of synthetic-to-real domain generalized semantic segmentation, which aims to learn a model that is robust to unseen real-world scenes using only synthetic data. The large domain shift between synthetic and real-world data, including the limited source environmental variations and the large distribution gap between synthetic and real-world data, significantly hinders the model performance on unseen real-world scenes. In this work, we propose the Style-HAllucinated Dual consistEncy learning (SHADE) framework to handle such domain shift. Specifically, SHADE is constructed based on two consistency constraints, Style Consistency (SC) and Retrospection Consistency (RC). SC enriches the source situations and encourages the model to learn consistent representation across style-diversified samples. RC leverages real-world knowledge to prevent the model from overfitting to synthetic data and thus largely keeps the representation consistent between the synthetic and real-world models. Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning. SHM selects basis styles from the source distribution, enabling the model to dynamically generate diverse and realistic samples during training. Experiments show that our SHADE yields significant improvement and outperforms state-of-the-art methods by 5.05% and 8.35% on the average mIoU of three real-world datasets on single- and multi-source settings, respectively."
SeqFormer: Sequential Transformer for Video Instance Segmentation
Authors: Junfeng Wu; Yi Jiang; Song Bai; Wenqing Zhang; Xiang Bai
Venue: ECCV 2022 (main)

Abstract: "In this work, we present SeqFormer for video instance segmentation. SeqFormer follows the principle of vision transformer that models instance relationships among video frames. Nevertheless, we observe that a stand-alone instance query suffices for capturing a time sequence of instances in a video, but attention mechanisms shall be done with each frame independently. To achieve this, SeqFormer locates an instance in each frame and aggregates temporal information to learn a powerful representation of a video-level instance, which is used to predict the mask sequences on each frame dynamically. Instance tracking is achieved naturally without tracking branches or post-processing. On YouTube-VIS, SeqFormer achieves 47.4 AP with a ResNet-50 backbone and 49.0 AP with a ResNet-101 backbone without bells and whistles. Such achievement significantly exceeds the previous state-of-the-art performance by 4.6 and 4.4, respectively. In addition, integrated with the recently-proposed Swin transformer, SeqFormer achieves a much higher AP of 59.3. We hope SeqFormer could be a strong baseline that fosters future research in video instance segmentation, and in the meantime, advances this field with a more robust, accurate, neat model. The code is available at https://github.com/wjf5203/SeqFormer."
Saliency Hierarchy Modeling via Generative Kernels for Salient Object Detection
Authors: Wenhu Zhang; Liangli Zheng; Huanyu Wang; Xintian Wu; Xi Li
Venue: ECCV 2022 (main)

Abstract: "Salient Object Detection (SOD) is a challenging problem that aims to precisely recognize and segment the salient objects. In ground-truth maps, all pixels belonging to the salient objects are positively annotated with the same value. However, the saliency level should be a relative quantity, which varies among different regions in a given sample and different samples. The conflict between various saliency levels and single saliency value in ground-truth, results in learning difficulty. To alleviate the problem, we propose a Saliency Hierarchy Network (SHNet), modeling saliency patterns via generative kernels from two perspectives: region-level and sample-level. Specifically, we construct a Saliency Hierarchy Module to explicitly model saliency levels of different regions in a given sample with the guide of prior knowledge. Moreover, considering the sample-level divergence, we introduce a Hyper Kernel Generator to capture the global contexts and adaptively generate convolution kernels for various inputs. As a result, extensive experiments on five standard benchmarks demonstrate our SHNet outperforms other state-of-the-art methods in both terms of performance and efficiency."
In Defense of Online Models for Video Instance Segmentation
Authors: Junfeng Wu; Qihao Liu; Yi Jiang; Song Bai; Alan Yuille; Xiang Bai
Venue: ECCV 2022 (main)

Abstract: "In recent years, video instance segmentation (VIS) has been largely advanced by offline models, while online models gradually attracted less attention possibly due to their inferior performance. However, online methods have their inherent advantage in handling long video sequences and ongoing videos while offline models fail due to the limit of computational resources. Therefore, it would be highly desirable if online models can achieve comparable or even better performance than offline models. By dissecting current online models and offline models, we demonstrate that the main cause of the performance gap is the error-prone association between frames caused by the similar appearance among different instances in the feature space. Observing this, we propose an online framework based on contrastive learning that is able to learn more discriminative instance embeddings for association and fully exploit history information for stability. Despite its simplicity, our method outperforms all online and offline methods on three benchmarks. Specifically, we achieve 49.5 AP on YouTube-VIS 2019, a significant improvement of 13.2 AP and 2.1 AP over the prior online and offline art, respectively. Moreover, we achieve 30.2 AP on OVIS, a more challenging dataset with significant crowding and occlusions, surpassing the prior art by 14.8 AP. The proposed method won first place in the video instance segmentation track of the 4th Large-scale Video Object Segmentation Challenge (CVPR2022). We hope the simplicity and effectiveness of our method, as well as our insight on current methods, could shed light on the exploration of VIS models. The code is available at https://github.com/wjf5203/VNext."
Active Pointly-Supervised Instance Segmentation
Authors: Chufeng Tang; Lingxi Xie; Gang Zhang; Xiaopeng Zhang; Qi Tian; Xiaolin Hu
Venue: ECCV 2022 (main)

Abstract: "The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting, named active pointly-supervised instance segmentation (APIS), which starts with box-level annotations and iteratively samples a point within the box and asks if it falls on the object. The key of APIS is to find the most desirable points to maximize the segmentation accuracy with limited annotation budgets. We formulate this setting and propose several uncertainty-based sampling strategies. The model developed with these strategies yields consistent performance gain on the challenging MS-COCO dataset, compared against other learning strategies. The results suggest that APIS, integrating the advantages of active learning and point-based supervision, is an effective learning paradigm for label-efficient instance segmentation."
A Transformer-Based Decoder for Semantic Segmentation with Multi-level Context Mining
Authors: Bowen Shi; Dongsheng Jiang; Xiaopeng Zhang; Han Li; Wenrui Dai; Junni Zou; Hongkai Xiong; Qi Tian
Venue: ECCV 2022 (main)

Abstract: "Transformers have recently shown superior performance than CNN on semantic segmentation. However, previous works mostly focus on the deliberate design of the encoder, while seldom considering the decoder part. In this paper, we find that a light weighted decoder counts for segmentation, and propose a pure transformer-based segmentation decoder, named SegDeformer, to seamlessly incorporate into current varied transformer-based encoders. The highlight is that SegDeformer is able to conveniently utilize the tokenized input and the attention mechanism of the transformer for effective context mining. This is achieved by two key component designs, i.e., the internal and external context mining modules. The former is equipped with internal attention within an image to better capture global-local context, while the latter introduces external tokens from other images to enhance current representation. To enable SegDeformer in a scalable way, we further provide performance/efficiency optimization modules for flexible deployment. Experiments on widely used benchmarks ADE20K, COCO-Stuff and Cityscapes and different transformer encoders (e.g., ViT, MiT and Swin) demonstrate that SegDeformer can bring consistent performance gains."
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
Authors: Ho Kei Cheng; Alexander G. Schwing
Venue: ECCV 2022 (main)

Abstract: "We present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model. Prior work on video object segmentation typically only uses one type of feature memory. For videos longer than a minute, a single feature memory model tightly links memory consumption and accuracy. In contrast, following the Atkinson-Shiffrin model, we develop an architecture that incorporates multiple independent yet deeply-connected feature memory stores: a rapidly updated sensory memory, a high-resolution working memory, and a compact thus sustained long-term memory. Crucially, we develop a memory potentiation algorithm that routinely consolidates actively used working memory elements into the long-term memory, which avoids memory explosion and minimizes performance decay for long-term prediction. Combined with a new memory reading mechanism, XMem greatly exceeds state-of-the-art performance on long-video datasets while being on par with state-of-the-art methods (that do not work on long videos) on short-video datasets. Code is available at https://hkchengrex.github.io/XMem"
Self-Distillation for Robust LiDAR Semantic Segmentation in Autonomous Driving
Authors: Jiale Li; Hang Dai; Yong Ding
Venue: ECCV 2022 (main)

Abstract: "We propose a new and effective self-distillation framework with our new Test-Time Augmentation (TTA) and Transformer based Voxel Feature Encoder (TransVFE) for robust LiDAR semantic segmentation in autonomous driving, where the robustness is mission-critical but usually neglected. The proposed framework enables the knowledge to be distilled from a teacher model instance to a student model instance, while the two model instances are with the same network architecture for jointly learning and evolving. This requires a strong teacher model to evolve in training. Our TTA strategy effectively reduces the uncertainty in the inference stage of the teacher model. Thus, we propose to equip the teacher model with TTA for providing privileged guidance while the student continuously updates the teacher with better network parameters learned by itself. To further enhance the teacher model, we propose a TransVFE to improve the point cloud encoding by modeling and preserving the local relationship among the points inside each voxel via multi-head attention. The proposed modules are generally designed to be instantiated with different backbones. Evaluations on SemanticKITTI and nuScenes datasets show that our method achieves state-of-the-art performance. Our code will be made publicly available."
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
Authors: Xu Yan; Jiantao Gao; Chaoda Zheng; Chao Zheng; Ruimao Zhang; Shuguang Cui; Zhen Li
Venue: ECCV 2022 (main)

Abstract: "As camera and LiDAR sensors capture complementary information used in autonomous driving, great efforts have been made to develop semantic segmentation algorithms through multi-modality data fusion. However, fusion-based approaches require paired data, i.e., LiDAR point clouds and camera images with strict point-to-pixel mappings, as the inputs in both training and inference, which seriously hinders their application in practical scenarios. Thus, in this work, we propose the 2D Priors Assisted Semantic Segmentation (2DPASS), a general training scheme, to boost the representation learning on point clouds, by fully taking advantage of 2D images with rich appearance. In practice, by leveraging an auxiliary modal fusion and multi-scale fusion-to-single knowledge distillation (MSFSKD), 2DPASS acquires richer semantic and structural information from the multi-modal data, which are then online distilled to the pure 3D network. As a result, equipped with 2DPASS, our baseline shows significant improvement with only point cloud inputs. Specifically, it achieves the state-of-the-arts on two large-scale benchmarks (i.e. SemanticKITTI and NuScenes), including top-1 results in both single and multiple scan(s) competitions of SemanticKITTI."
Extract Free Dense Labels from CLIP
Authors: Chong Zhou; Chen Change Loy; Bo Dai
Venue: ECCV 2022 (main)

Abstract: "Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition. Many recent studies leverage the pre-trained CLIP models for image-level classification and manipulation. In this paper, we wish examine the intrinsic potential of CLIP for pixel-level dense prediction, specifically in semantic segmentation. To this end, with minimal modification, we show that MaskCLIP yields compelling segmentation results on open concepts across various datasets in the absence of annotations and fine-tuning. By adding pseudo labeling and self-training, MaskCLIP+ surpasses SOTA transductive zero-shot semantic segmentation methods by large margins, e.g., mIoUs of unseen classes on PASCAL VOC/PASCAL Context/COCO Stuff are improved from 35.6/20.7/30.3 to 86.1/66.7/54.7. We also test the robustness of MaskCLIP under input corruption and evaluate its capability in discriminating fine-grained objects and novel concepts. Our finding suggests that MaskCLIP can serve as a new reliable source of supervision for dense prediction tasks to achieve annotation-free segmentation. Source code is available at https://github.com/chongzhou96/MaskCLIP."
3D Compositional Zero-Shot Learning with DeCompositional Consensus
Authors: Muhammad Ferjad Naeem; Evin Pınar Örnek; Yongqin Xian; Luc Van Gool; Federico Tombari
Venue: ECCV 2022 (main)

Abstract: "Parts represent a basic unit of geometric and semantic similarity across different objects. We argue that part knowledge should be composable beyond the observed object classes. Towards this, we present 3D Compositional Zero-shot Learning as a problem of part generalization from seen to unseen object classes for semantic segmentation. We provide a structured study through benchmarking the task with the proposed Compositional-PartNet dataset. This dataset is created by processing the original PartNet to maximize part overlap across different objects. The existing point cloud part segmentation methods fail to generalize to unseen object classes in this setting. As a solution, we propose DeCompositional Consensus, which combines a part segmentation network with a part scoring network. The key intuition to our approach is that a segmentation mask over some parts should have a consensus with its part scores when each part is taken apart. The two networks reason over different part combinations defined in a per-object part prior to generate the most suitable segmentation mask. We demonstrate that our method allows compositional zero-shot segmentation and generalized zero-shot classification and establishes the state of the art on both tasks."
Video Mask Transfiner for High-Quality Video Instance Segmentation
Authors: Lei Ke; Henghui Ding; Martin Danelljan; Yu-Wing Tai; Chi-Keung Tang; Fisher Yu
Venue: ECCV 2022 (main)

Abstract: "While Video Instance Segmentation (VIS) has seen rapid progress, current approaches struggle to predict high-quality masks with accurate boundary details. Moreover, the predicted segmentations often fluctuate over time, suggesting that temporal consistency cues are neglected or not fully utilized. In this paper, we set out to tackle these issues, with the aim of achieving highly detailed and more temporally stable mask predictions for VIS. We first propose the Video Mask Transfiner (VMT) method, capable of leveraging fine-grained high-resolution features thanks to a highly efficient video transformer structure. Our VMT detects and groups sparse error-prone spatio-temporal regions of each tracklet in the video segment, which are then refined using both local and instance-level cues. Second, we identify that the coarse boundary annotations of the popular YouTube-VIS dataset constitute a major limiting factor. Based on our VMT architecture, we therefore design an automated annotation refinement approach by iterative training and self-correction. To benchmark high-quality mask predictions for VIS, we introduce the HQ-YTVIS dataset, consisting of a manually re-annotated test set and our automatically refined training data. We compare VMT with the most recent state-of-the-art methods on the HQ-YTVIS, as well as the Youtube-VIS, OVIS and BDD100K MOTS benchmarks. Experimental results clearly demonstrate the efficacy and effectiveness of our method on segmenting complex and dynamic objects, by capturing precise details."
Box-Supervised Instance Segmentation with Level Set Evolution
Authors: Wentong Li; Wenyu Liu; Jianke Zhu; Miaomiao Cui; Xian-Sheng Hua; Lei Zhang
Venue: ECCV 2022 (main)

Abstract: "In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which integrates the classical level set model with deep neural network delicately. Specifically, our proposed method iteratively learns a series of level sets through a continuous Chan-Vese energy-based function in an end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance. Both the input image and its deep features are employed as the input data to evolve the level set curves, where a box projection function is employed to obtain the initial boundary. By minimizing the fully differentiable energy function, the level set for each instance is iteratively optimized within its corresponding bounding box annotation. The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach to robust instance segmentation in various scenarios. The code is available at: https://github.com/LiWentomng/boxlevelset."
Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding
Authors: Hao Wen; Yunze Liu; Jingwei Huang; Bo Duan; Li Yi
Venue: ECCV 2022 (main)

Abstract: "This paper proposes a 4D backbone for long-term point cloud video understanding. A typical way to capture spatial-temporal context is using 4Dconv or transformer without hierarchy. However, those methods are neither effective nor efficient enough due to camera motion, scene changes, sampling patterns, and complexity of 4D data. To address those issues, we leverage the primitive plane as mid-level representation to capture the long-term spatial-temporal context in 4D point cloud videos, and propose a novel hierarchical backbone named Point Primitive Transformer(PPTr), which is mainly composed of intra-primitive point transformers and primitive transformers. Extensive experiments show that PPTr outperforms the previous state of the arts on different tasks."
Adaptive Agent Transformer for Few-Shot Segmentation
Authors: Yuan Wang; Rui Sun; Zhe Zhang; Tianzhu Zhang
Venue: ECCV 2022 (main)

Abstract: "Few-shot segmentation (FSS) aims to segment objects in a given query image with only a few labelled support images. The limited support information makes it an extremely challenging task. Most previous best-performing methods adopt prototypical learning or affinity learning. Nevertheless, they either neglect to further utilize support pixels for facilitating segmentation and lose spatial information, or are not robust to noisy pixels and computationally expensive. In this work, we propose a novel end-to-end adaptive agent transformer (AAFormer) to integrate prototypical and affinity learning to exploit the complementarity between them via a transformer encoder-decoder architecture, including a representation encoder, an agent learning decoder and an agent matching decoder. The proposed AAFormer enjoys several merits. First, to learn agent tokens well without any explicit supervision, and to make agent tokens capable of dividing different objects into diverse parts in an adaptive manner, we customize the agent learning decoder according to the three characteristics of context awareness, spatial awareness and diversity. Second, the proposed agent matching decoder is responsible for decomposing the direct pixel-level matching matrix into two more computationally-friendly matrices to suppress the noisy pixels. Extensive experimental results on two standard benchmarks demonstrate that our AAFormer performs favorably against state-of-the-art FSS methods."