GenAI4ELab/papercli-papers huggingface dataset:
DLME: Deep Local-Flatness Manifold Embedding
Authors: Zelin Zang; Siyuan Li; Di Wu; Ge Wang; Kai Wang; Lei Shang; Baigui Sun; Hao Li; Stan Z. Li
Venue: ECCV 2022 (main)

Abstract: "Manifold learning (ML) aims to seek low-dimensional embedding from high-dimensional data. The problem is challenging on real-world datasets, especially with under-sampling data, and we find that previous methods perform poorly in this case. Generally, ML methods first transform input data into a low-dimensional embedding space to maintain the data’s geometric structure and subsequently perform downstream tasks therein. The poor local connectivity of under-sampling data in the former step and inappropriate optimization objectives in the latter step leads to two problems: structural distortion and underconstrained embedding. This paper proposes a novel ML framework named Deep Local-flatness Manifold Embedding (DLME) to solve these problems. The proposed DLME constructs semantic manifolds by data augmentation and overcomes the structural distortion problem using a smoothness constrained based on a local flatness assumption about the manifold. To overcome the underconstrained embedding problem, we design a loss and theoretically demonstrate that it leads to a more suitable embedding based on the local flatness. Experiments on three types of datasets (toy, biological, and image) for various downstream tasks (classification, clustering, and visualization) show that our proposed DLME outperforms state-of-the-art ML and contrastive learning methods."
Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching
Authors: Jiazhen Liu; Xirong Li; Qijie Wei; Jie Xu; Dayong Ding
Venue: ECCV 2022 (main)

Abstract: "For retinal image matching (RIM), we propose SuperRetina, the first end-to-end method with jointly trainable keypoint detector and descriptor. SuperRetina is trained in a novel semi-supervised manner. A small set of (nearly 100) images are incompletely labeled and used to supervise the network to detect keypoints on the vascular tree. To attack the incompleteness of manual labeling, we propose Progressive Keypoint Expansion to enrich the keypoint labels at each training epoch. By utilizing a keypoint-based improved triplet loss as its description loss, SuperRetina produces highly discriminative descriptors at full input image size. Extensive experiments on multiple real-world datasets justify the viability of SuperRetina. Even with manual labeling replaced by auto labeling and thus making the training process fully manual-annotation free, SuperRetina compares favorably against a number of strong baselines for two RIM tasks, i.e. image registration and identity verification."
Graph Neural Network for Cell Tracking in Microscopy Videos
Authors: Tal Ben-Haim; Tammy Riklin Raviv
Venue: ECCV 2022 (main)

Abstract: "We present a novel graph neural network (GNN) approach for cell tracking in high-throughput microscopy videos. By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its nodes and their associations by its edges, we extract the entire set of cell trajectories by looking for the maximal paths in the graph. This is accomplished by several key contributions incorporated into an end-to-end deep learning framework. We exploit a deep metric learning algorithm to extract cell feature vectors that distinguish between instances of different biological cells and assemble same cell instances. We introduce a new GNN block type which enables a mutual update of node and edge feature vectors, thus facilitating the underlying message passing process. The message passing concept, whose extent is determined by the number of GNN blocks, is of fundamental importance as it enables the ‘flow’ of information between nodes and edges much behind their neighbors in consecutive frames. Finally, we solve an edge classification problem and use the identified active edges to construct the cells’ tracks and lineage trees. We demonstrate the strengths of the proposed cell tracking approach by applying it to 2D and 3D datasets of different cell types, imaging setups, and experimental conditions. We show that our framework outperforms current state-of-the-art methods on most of the evaluated datasets. The code is available at our repository: https://github.com/talbenha/cell-tracker-gnn."
CXR Segmentation by AdaIN-Based Domain Adaptation and Knowledge Distillation
Authors: Yujin Oh; Jong Chul Ye
Venue: ECCV 2022 (main)

Abstract: "As segmentation labels are scarce, extensive researches have been conducted to train segmentation networks with domain adaptation, semi-supervised or self-supervised learning techniques to utilize abun- dant unlabeled dataset. However, these approaches appear different from each other, so it is not clear how these approaches can be combined for better performance. Inspired by recent multi-domain image translation approaches, here we propose a novel segmentation framework using adap- tive instance normalization (AdaIN), so that a single generator is trained to perform both domain adaptation and semi-supervised segmentation tasks via knowledge distillation by simply changing task-specific AdaIN codes. Specifically, our framework is designed to deal with difficult situ- ations in chest X-ray radiograph (CXR) segmentation, where labels are only available for normal data, but trained model should be applied to both normal and abnormal data. The proposed network demonstrates great generalizability under domain shift and achieves the state-of-the- art performance for abnormal CXR segmentation."
Accurate Detection of Proteins in Cryo-Electron Tomograms from Sparse Labels
Authors: Qinwen Huang; Ye Zhou; Hsuan-Fu Liu; Alberto Bartesaghi
Venue: ECCV 2022 (main)

Abstract: "Cryo-electron tomography (CET) combined with sub-volume averaging (SVA), is currently the only imaging technique capable of determining the structure of proteins imaged inside cells at molecular resolution. To obtain high-resolution reconstructions, sub-volumes containing randomly distributed copies of the protein of interest need be identified, extracted and subjected to SVA, making accurate particle detection a critical step in the CET processing pipeline. Classical template-based methods have high false-positive rates due to the very low signal-to-noise ratios (SNR) typical of CET volumes, while more recent neural-network based detection algorithms require extensive labeling, are very slow to train and can take days to run. To address these issues, we propose a novel particle detection framework that uses positive-unlabeled learning and exploits the unique properties of 3D tomograms to improve detection performance. Our end-to-end framework is able to identify particles within minutes when trained using a single partially labeled tomogram. We conducted extensive validation experiments on two challenging CET datasets representing different experimental conditions, and observed more than 10% improvement in mAP and F1 scores compared to existing particle picking methods used in CET. Ultimately, the proposed framework will facilitate the structural analysis of challenging biomedical targets imaged within the native environment of cells."
K-SALSA: K-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment
Authors: Minkyu Jeon; Hyeonjin Park; Hyunwoo J. Kim; Michael Morley; Hyunghoon Cho
Venue: ECCV 2022 (main)

Abstract: "The application of modern machine learning to retinal image analyses offers valuable insights into a broad range of human health conditions beyond ophthalmic diseases. Additionally, data sharing is key to fully realizing the potential of machine learning models by providing a rich and diverse collection of training data. However, the personally-identifying nature of retinal images, encompassing the unique vascular structure of each individual, often prevents this data from being shared openly. While prior works have explored image de-identification strategies based on synthetic averaging of images in other domains (e.g. facial images), existing techniques face difficulty in preserving both privacy and clinical utility in retinal images, as we demonstrate in our work. We therefore introduce k-SALSA, a generative adversarial network (GAN)-based framework for synthesizing retinal fundus images that summarize a given private dataset while satisfying the privacy notion of k-anonymity. k-SALSA brings together state-of-the-art techniques for training and inverting GANs to achieve practical performance on retinal images. Furthermore, k-SALSA leverages a new technique, called local style alignment, to generate a synthetic average that maximizes the retention of fine-grain visual patterns in the source images, thus improving the clinical utility of the generated images. On two benchmark datasets of diabetic retinopathy (EyePACS and APTOS), we demonstrate our improvement upon existing methods with respect to image fidelity, classification performance, and mitigation of membership inference attacks. Our work represents a step toward broader sharing of retinal images for scientific collaboration. Keywords : Medical image privacy, k-anonymity, generative adversarial networks, fundus imaging, synthetic data generation, style transfer"
RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-Guided Disease Classification
Authors: Moinak Bhattacharya; Shubham Jain; Prateek Prasanna
Venue: ECCV 2022 (main)

Abstract: "In this work, we present RadioTransformer, a novel visual attention-driven transformer framework, that leverages radiologists’ gaze patterns and models their visuo-cognitive behavior for disease diagnosis on chest radiographs. Domain experts, such as radiologists, rely on visual information for medical image interpretation. On the other hand, deep neural networks have demonstrated significant promise in similar tasks even where visual interpretation is challenging. Eye-gaze tracking has been used to capture the viewing behavior of domain experts, lending insights into the complexity of visual search. However, deep learning frameworks, even those that rely on attention mechanisms, do not leverage this rich domain information. RadioTransformer fills this critical gap by learning from radiologists’ visual search patterns, encoded as ‘human visual attention regions’ in a cascaded global-focal transformer framework. The overall ‘global’ image characteristics and the more detailed ‘local’ features are captured by the proposed global and focal modules, respectively. We experimentally validate the efficacy of our student-teacher approach for 8 datasets involving different disease classification tasks where eye-gaze data is not available during the inference phase. Code: https://github.com/bmi-imaginelab/radiotransformer."
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Authors: Kevin Thandiackal; Boqi Chen; Pushpak Pati; Guillaume Jaume; Drew F. K. Williamson; Maria Gabrani; Orcun Goksel
Venue: ECCV 2022 (main)

Abstract: "Multiple Instance Learning (MIL) methods have become increasingly popular for classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate at a single WSI magnification, by processing all the tissue patches. Such a formulation induces high computational requirements and constrains the contextualization of the WSI-level representation to a single scale. Certain MIL methods extend to multiple scales, but they are computationally more demanding. In this paper, inspired by the pathological diagnostic process, we propose ZoomMIL, a method that learns to perform multi-level zooming in an end-to-end manner. ZoomMIL builds WSI representations by aggregating tissue-context information from multiple magnifications. The proposed method outperforms the state-of-the-art MIL methods in WSI classification on two large datasets, while significantly reducing computational demands with regard to Floating-Point Operations (FLOPs) and processing time by 40-50x. Our code is available at: https://github.com/histocartography/zoommil."
Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis
Authors: Chongyang Zhong; Lei Hu; Zihao Zhang; Shihong Xia
Venue: ECCV 2022 (main)

Abstract: "Motion capture data is largely needed in the movie and game industry in recent years. Since the motion capture system is expensive and requires manual post-processing, motion synthesis is a plausible solution to acquire more motion data. However, generating the action-conditioned, realistic, and diverse 3D human motions given the semantic action labels is still challenging because the mapping from semantic labels to real motion sequences is hard to depict. Previous work made some positive attempts like appending label tokens to pose encoding and performing action bias on latent space, however, how to synthesize diverse motions that accurately match the given label is still not fully explored. In this paper, we propose the Uncoupled-Modulation Conditional Variational AutoEncoder(UM-CVAE) to generate action-conditioned motions from scratch in an uncoupled manner. The main idea is twofold: (i)training an action-agnostic encoder to eliminate the action-related information to learn the easy-modulated latent representation; (ii)strengthening the action-conditioned process with FiLM-based action-aware modulation. We conduct extensive experiments on the HumanAct12, UESTC, and BABEL datasets, demonstrating that our method achieves state-of-the-art performance both qualitatively and quantitatively with potential applications."
Towards Grand Unification of Object Tracking
Authors: Bin Yan; Yi Jiang; Peize Sun; Dong Wang; Zehuan Yuan; Ping Luo; Huchuan Lu
Venue: ECCV 2022 (main)

Abstract: "We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters. Due to the fragmented definitions of the object tracking problem itself, most existing trackers are developed to address a single or part of tasks and overspecialize on the characteristics of specific tasks. By contrast, Unicorn provides a unified solution, adopting the same input, backbone, embedding, and head across all tracking tasks. For the first time, we accomplish the great unification of the tracking network architecture and learning paradigm. Unicorn performs on-par or better than its task-specific counterparts in 8 tracking datasets, including LaSOT, TrackingNet, MOT17, BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS. We believe that Unicorn will serve as a solid step towards the general vision model. Code is available at https://github.com/MasterBin-IIAU/Unicorn."
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Authors: Yifu Zhang; Peize Sun; Yi Jiang; Dongdong Yu; Fucheng Weng; Zehuan Yuan; Ping Luo; Wenyu Liu; Xinggang Wang
Venue: ECCV 2022 (main)

Abstract: "Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are higher than a threshold. The objects with low detection scores, e.g. occluded objects, are simply thrown away, which brings non-negligible true object missing and fragmented trajectories. To solve this problem, we present a simple, effective and generic association method, tracking by associating almost every detection box instead of only the high score ones. For the low score detection boxes, we utilize their similarities with tracklets to recover true objects and filter out the background detections. When applied to 9 different state-of-the-art trackers, our method achieves consistent improvement on IDF1 score ranging from 1 to 10 points. To put forwards the state-of-the-art performance of MOT, we design a simple and strong tracker, named ByteTrack. For the first time, we achieve 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU. ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks. The source code, pre-trained models with deploy versions and tutorials of applying to other trackers are released at https://github.com/ifzhang/ByteTrack."
Robust Multi-Object Tracking by Marginal Inference
Authors: Yifu Zhang; Chunyu Wang; Xinggang Wang; Wenjun Zeng; Wenyu Liu
Venue: ECCV 2022 (main)

Abstract: "Multi-object tracking in videos requires to solve a fundamental problem of one-to-one assignment between objects in adjacent frames. Most methods address the problem by first discarding impossible pairs whose feature distances are larger than a threshold, followed by linking objects using Hungarian algorithm to minimize the overall distance. However, we find that the distribution of the distances computed from Re-ID features may vary significantly for different videos. So there isn’t a single optimal threshold which allows us to safely discard impossible pairs. To address the problem, we present an efficient approach to compute a marginal probability for each pair of objects in real time. The marginal probability can be regarded as a normalized distance which is significantly more stable than the original feature distance. As a result, we can use a single threshold for all videos. The approach is general and can be applied to the existing trackers to obtain about one point improvement in terms of IDF1 metric. It achieves competitive results on MOT17 and MOT20 benchmarks. In addition, the computed probability is more interpretable which facilitates subsequent post-processing operations."
PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?
Authors: Aleksandr Kim; Guillem Brasó; Aljoša Ošep; Laura Leal-Taixé
Venue: ECCV 2022 (main)

Abstract: "Most (3D) multi-object tracking methods rely on appearance-based cues for data association. By contrast, we investigate how far we can get by only encoding geometric relationships between objects in 3D space as cues for data-driven data association. We encode 3D detections as nodes in a graph, where spatial and temporal pairwise relations among objects are encoded via localized polar coordinates on graph edges. This representation makes our geometric relations invariant to global transformations and smooth trajectory changes, especially under non-holonomic motion. This allows our graph neural network to learn to effectively encode temporal and spatial interactions and fully leverage contextual and motion cues to obtain final scene interpretation by posing data association as edge classification. We establish a new state-of-the-art on nuScenes dataset and, more importantly, show that our method, PolarMOT, generalizes remarkably well across different locations (Boston, Singapore, Karlsruhe) and datasets (nuScenes and KITTI)."
Particle Video Revisited: Tracking through Occlusions Using Point Trajectories
Authors: Adam W. Harley; Zhaoyuan Fang; Katerina Fragkiadaki
Venue: ECCV 2022 (main)

Abstract: Tracking pixels in videos is typically studied as an optical flow estimation problem, where every pixel is described with a displacement vector that locates it in the next frame. Even though wider temporal context is freely available, prior efforts to take this into account have yielded only small gains over 2-frame methods. In this paper, we revisit Sand and Teller’s "particle video" approach, and study pixel tracking as a long-range motion estimation problem, where every pixel is described with a trajectory that locates it in multiple future frames. We re-build this classic approach using components that drive the current state-of-the-art in flow and object tracking, such as dense cost maps, iterative optimization, and learned appearance updates. We train our models using long-range amodal point trajectories mined from existing optical flow data that we synthetically augment with multi-frame occlusions. We test our approach in trajectory estimation benchmarks and in keypoint label propagation tasks, and compare favorably against state-of-the-art optical flow and feature tracking methods.
Tracking Objects As Pixel-Wise Distributions
Authors: Zelin Zhao; Ze Wu; Yueqing Zhuang; Boxun Li; Jiaya Jia
Venue: ECCV 2022 (main)

Abstract: "Multi-object tracking (MOT) requires detecting and associating objects through frames. Unlike tracking via detected bounding boxes or center points, we propose tracking objects as pixel-wise distributions. We instantiate this idea on a transformer-based architecture named P3AFormer, with pixel-wise propagation, prediction, and association. P3AFormer propagates pixel-wise features guided by flow information to pass messages between frames. Further, P3AFormer adopts a meta-architecture to produce multi-scale object feature maps. During inference, a pixel-wise association procedure is proposed to recover object connections through frames based on the pixel-wise prediction. P3AFormer yields 81.2\% in terms of MOTA on the MOT17 benchmark -- highest among all transformer networks to reach 80\% MOTA in literature. P3AFormer also outperforms state-of-the-arts on the MOT20 and KITTI benchmarks. The code is at https://github.com/dvlab-research/ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions."
CMT: Context-Matching-Guided Transformer for 3D Tracking in Point Clouds
Authors: Zhiyang Guo; Yunyao Mao; Wengang Zhou; Min Wang; Houqiang Li
Venue: ECCV 2022 (main)

Abstract: "How to effectively match the target template features with the search area is the core problem in point-cloud-based 3D single object tracking. However, in the literature, most of the methods focus on devising sophisticated matching modules at point-level, while overlooking the rich spatial context information of points. To this end, we propose Context-Matching-Guided Transformer (CMT), a Siamese tracking paradigm for 3D single object tracking. In this work, we first leverage the local distribution of points to construct a horizontally rotation-invariant contextual descriptor for both the template and the search area. Then, a novel matching strategy based on shifted windows is designed for such descriptors to effectively measure the template-search contextual similarity. Furthermore, we introduce a target-specific transformer and a spatial-aware orientation encoder to exploit the target-aware information in the most contextually relevant template points, thereby enhancing the search feature for a better target proposal. We conduct extensive experiments to verify the merits of our proposed CMT and report a series of new state-of-the-art records on three widely-adopted datasets."
Towards Generic 3D Tracking in RGBD Videos: Benchmark and Baseline
Authors: Jinyu Yang; Zhongqun Zhang; Zhe Li; Hyung Jin Chang; Aleš Leonardis; Feng Zheng
Venue: ECCV 2022 (main)

Abstract: "Tracking in 3D scenes is gaining momentum because of its numerous applications in robotics, autonomous driving, and scene understanding. Currently, 3D tracking is limited to specific model-based approaches involving point clouds, which impedes 3D trackers from applying in natural 3D scenes. RGBD sensors provide a more reasonable and acceptable solution for 3D object tracking due to their readily available synchronised color and depth information. Thus, in this paper, we investigate a novel problem: is it possible to track a generic (class-agnostic) 3D object in RGBD videos and predict 3D bounding boxes of the object of interest? To inspire further research on this topic, we newly construct a standard benchmark for generic 3D object tracking, ‘Track-it-in-3D’, which contains 300 RGBD video sequences with dense 3D annotations and corresponding evaluation protocols. Furthermore, we propose an effective tracking baseline to estimate 3D bounding boxes for arbitrary objects in RGBD videos, by fusing appearance and spatial information effectively. The dataset and codes will be publicly available."