pwc-archive/papers-with-abstracts huggingface dataset:
Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive Learning
Authors: Guangming Huang, Yunfei Long, Cunjin Luo
Date: 2024-10-17 00:00:00
Tasks: Contrastive Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Supervised contrastive learning has achieved remarkable success by leveraging label information; however, determining positive samples in multi-label scenarios remains a critical challenge. In multi-label supervised contrastive learning (MSCL), multi-label relations are not yet fully defined, leading to ambiguity in identifying positive samples and formulating contrastive loss functions to construct the representation space. To address these challenges, we: (i) first define five distinct multi-label relations in MSCL to systematically identify positive samples, (ii) introduce a novel Similarity-Dissimilarity Loss that dynamically re-weights samples through computing the similarity and dissimilarity factors between positive samples and given anchors based on multi-label relations, and (iii) further provide theoretical grounded proofs for our method through rigorous mathematical analysis that supports the formulation and effectiveness of the proposed loss function. We conduct the experiments across both image and text modalities, and extend the evaluation to medical domain. The results demonstrate that our method consistently outperforms baselines in a comprehensive evaluation, confirming its effectiveness and robustness. Code is available at: https://github.com/guangminghuang/similarity-dissimilarity-loss.
Temporal-Enhanced Multimodal Transformer for Referring Multi-Object Tracking and Segmentation
Authors: Changcheng Xiao, Qiong Cao, Yujie Zhong, Xiang Zhang, Tao Wang, Canqun Yang, Long Lan
Date: 2024-10-17 00:00:00
Tasks: Multi-Object Tracking, Multi-Object Tracking and Segmentation, Object Tracking, Referring Multi-Object Tracking, Segmentation, Video Segmentation, Video Semantic Segmentation

Referring multi-object tracking (RMOT) is an emerging cross-modal task that aims to locate an arbitrary number of target objects and maintain their identities referred by a language expression in a video. This intricate task involves the reasoning of linguistic and visual modalities, along with the temporal association of target objects. However, the seminal work employs only loose feature fusion and overlooks the utilization of long-term information on tracked objects. In this study, we introduce a compact Transformer-based method, termed TenRMOT. We conduct feature fusion at both encoding and decoding stages to fully exploit the advantages of Transformer architecture. Specifically, we incrementally perform cross-modal fusion layer-by-layer during the encoding phase. In the decoding phase, we utilize language-guided queries to probe memory features for accurate prediction of the desired objects. Moreover, we introduce a query update module that explicitly leverages temporal prior information of the tracked objects to enhance the consistency of their trajectories. In addition, we introduce a novel task called Referring Multi-Object Tracking and Segmentation (RMOTS) and construct a new dataset named Ref-KITTI Segmentation. Our dataset consists of 18 videos with 818 expressions, and each expression averages 10.7 masks, which poses a greater challenge compared to the typical single mask in most existing referring video segmentation datasets. TenRMOT demonstrates superior performance on both the referring multi-object tracking and the segmentation tasks.
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation
Authors: Kamil Kwarciak, Mateusz Daniol, Daria Hemmerling, Marek Wodzinski
Date: 2024-10-17 00:00:00
Tasks: Contrastive Learning, Segmentation, Skull Stripping, Super-Resolution

The skull segmentation from CT scans can be seen as an already solved problem. However, in MR this task has a significantly greater complexity due to the presence of soft tissues rather than bones. Capturing the bone structures from MR images of the head, where the main visualization objective is the brain, is very demanding. The attempts that make use of skull stripping seem to not be well suited for this task and fail to work in many cases. On the other hand, supervised approaches require costly and time-consuming skull annotations. To overcome the difficulties we propose a fully unsupervised approach, where we do not perform the segmentation directly on MR images, but we rather perform a synthetic CT data generation via MR-to-CT translation and perform the segmentation there. We address many issues associated with unsupervised skull segmentation including the unpaired nature of MR and CT datasets (contrastive learning), low resolution and poor quality (super-resolution), and generalization capabilities. The research has a significant value for downstream tasks requiring skull segmentation from MR volumes such as craniectomy or surgery planning and can be seen as an important step towards the utilization of synthetic data in medical imaging.
Performance of Gaussian Mixture Model Classifiers on Embedded Feature Spaces
Authors: Jeremy Chopin, Rozenn Dahyot
Date: 2024-10-17 00:00:00
Tasks: Image Classification

Data embeddings with CLIP and ImageBind provide powerful features for the analysis of multimedia and/or multimodal data. We assess their performance here for classification using a Gaussian Mixture models (GMMs) based layer as an alternative to the standard Softmax layer. GMMs based classifiers have recently been shown to have interesting performances as part of deep learning pipelines trained end-to-end. Our first contribution is to investigate GMM based classification performance taking advantage of the embedded spaces CLIP and ImageBind. Our second contribution is in proposing our own GMM based classifier with a lower parameters count than previously proposed. Our findings are, that in most cases, on these tested embedded spaces, one gaussian component in the GMMs is often enough for capturing each class, and we hypothesize that this may be due to the contrastive loss used for training these embedded spaces that naturally concentrates features together for each class. We also observed that ImageBind often provides better performance than CLIP for classification of image datasets even when these embedded spaces are compressed using PCA.
RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agents
Authors: Zhuoran Liu, Danpei Zhao, Bo Yuan
Date: 2024-10-17 00:00:00
Tasks: Question Answering, Task Planning, Visual Question Answering, Visual Question Answering (VQA)

Current methods for disaster scene interpretation in remote sensing images (RSIs) mostly focus on isolated tasks such as segmentation, detection, or visual question-answering (VQA). However, current interpretation methods often fail at tasks that require the combination of multiple perception methods and specialized tools. To fill this gap, this paper introduces Adaptive Disaster Interpretation (ADI), a novel task designed to solve requests by planning and executing multiple sequentially correlative interpretation tasks to provide a comprehensive analysis of disaster scenes. To facilitate research and application in this area, we present a new dataset named RescueADI, which contains high-resolution RSIs with annotations for three connected aspects: planning, perception, and recognition. The dataset includes 4,044 RSIs, 16,949 semantic masks, 14,483 object bounding boxes, and 13,424 interpretation requests across nine challenging request types. Moreover, we propose a new disaster interpretation method employing autonomous agents driven by large language models (LLMs) for task planning and execution, proving its efficacy in handling complex disaster interpretations. The proposed agent-based method solves various complex interpretation requests such as counting, area calculation, and path-finding without human intervention, which traditional single-task approaches cannot handle effectively. Experimental results on RescueADI demonstrate the feasibility of the proposed task and show that our method achieves an accuracy 9% higher than existing VQA methods, highlighting its advantages over conventional disaster interpretation approaches. The dataset will be publicly available.
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation
Authors: Florian Wulff, Bernd Schaeufele, Julian Pfeifer, Ilja Radusch
Date: 2024-10-17 00:00:00
Tasks: 3D Semantic Segmentation, Active Learning, Image Segmentation, LIDAR Semantic Segmentation, Segmentation, Semantic Segmentation

Automated vehicles rely on an accurate and robust perception of the environment. Similarly to automated cars, highly automated trains require an environmental perception. Although there is a lot of research based on either camera or LiDAR sensors in the automotive domain, very few contributions for this task exist yet for automated trains. Additionally, no public dataset or described approach for a 3D LiDAR semantic segmentation in the railway environment exists yet. Thus, we propose an approach for a point-wise 3D semantic segmentation based on the 2DPass network architecture using scans and images jointly. In addition, we present a semi-automated intelligent data annotation approach, which we use to efficiently and accurately label the required dataset recorded on a railway track in Germany. To improve performance despite a still small number of labeled scans, we apply an active learning approach to intelligently select scans for the training dataset. Our contributions are threefold: We annotate rail data including camera and LiDAR data from the railway environment, transfer label the raw LiDAR point clouds using an image segmentation network, and train a state-of-the-art 3D LiDAR semantic segmentation network efficiently leveraging active learning. The trained network achieves good segmentation results with a mean IoU of 71.48% of 9 classes.
Rotating-star Pattern for Camera Calibration
Authors: Zezhun Shi
Date: 2024-10-17 00:00:00
Tasks: Autonomous Driving, Camera Calibration

Camera calibration is fundamental to 3D vision, and the choice of calibration pattern greatly affects the accuracy. To address aberration issue, star-shaped pattern has been proposed as alternatives to traditional checkerboard. However, such pattern suffers from aliasing artifacts. In this paper, we present a novel solution by employing a series of checkerboard patterns rotated around a central point instead of a single star-shaped pattern. We further propose a complete feature extraction algorithm tailored for this design. Experimental results demonstrate that our approach offers improved accuracy over the conventional star-shaped pattern and achieves high stability across varying exposure levels.
Self-Supervised Scene Flow Estimation with Point-Voxel Fusion and Surface Representation
Authors: Xuezhi Xiang, Xi Wang, Lei Zhang, Denis Ombati, Himaloy Himu, XianTong Zhen
Date: 2024-10-17 00:00:00
Tasks: Scene Flow Estimation, Self-supervised Scene Flow Estimation

Scene flow estimation aims to generate the 3D motion field of points between two consecutive frames of point clouds, which has wide applications in various fields. Existing point-based methods ignore the irregularity of point clouds and have difficulty capturing long-range dependencies due to the inefficiency of point-level computation. Voxel-based methods suffer from the loss of detail information. In this paper, we propose a point-voxel fusion method, where we utilize a voxel branch based on sparse grid attention and the shifted window strategy to capture long-range dependencies and a point branch to capture fine-grained features to compensate for the information loss in the voxel branch. In addition, since xyz coordinates are difficult to describe the geometric structure of complex 3D objects in the scene, we explicitly encode the local surface information of the point cloud through the umbrella surface feature extraction (USFE) module. We verify the effectiveness of our method by conducting experiments on the Flyingthings3D and KITTI datasets. Our method outperforms all other self-supervised methods and achieves highly competitive results compared to fully supervised methods. We achieve improvements in all metrics, especially EPE, which is reduced by 8.51% on the KITTIo dataset and 10.52% on the KITTIs dataset, respectively.
GlossyGS: Inverse Rendering of Glossy Objects with 3D Gaussian Splatting
Authors: Shuichang Lai, Letian Huang, Jie Guo, Kai Cheng, Bowen Pan, Xiaoxiao Long, Jiangjing Lyu, Chengfei Lv, Yanwen Guo
Date: 2024-10-17 00:00:00
Tasks: Inverse Rendering, NeRF

Reconstructing objects from posed images is a crucial and complex task in computer graphics and computer vision. While NeRF-based neural reconstruction methods have exhibited impressive reconstruction ability, they tend to be time-comsuming. Recent strategies have adopted 3D Gaussian Splatting (3D-GS) for inverse rendering, which have led to quick and effective outcomes. However, these techniques generally have difficulty in producing believable geometries and materials for glossy objects, a challenge that stems from the inherent ambiguities of inverse rendering. To address this, we introduce GlossyGS, an innovative 3D-GS-based inverse rendering framework that aims to precisely reconstruct the geometry and materials of glossy objects by integrating material priors. The key idea is the use of micro-facet geometry segmentation prior, which helps to reduce the intrinsic ambiguities and improve the decomposition of geometries and materials. Additionally, we introduce a normal map prefiltering strategy to more accurately simulate the normal distribution of reflective surfaces. These strategies are integrated into a hybrid geometry and material representation that employs both explicit and implicit methods to depict glossy objects. We demonstrate through quantitative analysis and qualitative visualization that the proposed method is effective to reconstruct high-fidelity geometries and materials of glossy objects, and performs favorably against state-of-the-arts.
Inadequate contrast ratio of road markings as an indicator for ADAS failure
Authors: Novel Certad, Cristina Olaverri-Monreal, Friedrich Wiesinger, Tomasz E. Burghardt
Date: 2024-10-17 00:00:00
Tasks: Lane Detection, Trajectory Planning

Road markings were reported as critical road safety features, equally needed for both human drivers and for machine vision technologies utilised by advanced driver assistance systems (ADAS) and in driving automation. Visibility of road markings is achieved because of their colour contrasting with the roadway surface. During recent testing of an open-source camera-based ADAS under several visibility conditions (day, night, rain, glare), significant failures in trajectory planning were recorded and quantified. Consistently, better ADAS reliability under poor visibility conditions was achieved with Type II road markings (i.e. structured markings, facilitating moisture drainage) as compared to Type I road marking (i.e. flat lines). To further understand these failures, analysis of contrast ratio of road markings, which the tested ADAS was detecting for traffic lane recognition, was performed. The highest contrast ratio (greater than 0.5, calculated per Michelson equation) was measured at night in the absence of confounding factors, with statistically significant difference of 0.1 in favour of Type II road markings over Type I. Under daylight conditions, contrast ratio was reduced, with slightly higher values measured with Type I. The presence of rain or wet roads caused the deterioration of the contrast ratio, with Type II road markings exhibiting significantly higher contrast ratio than Type I, even though the values were low (less than 0.1). These findings matched the output of the ADAS related to traffic lane detection and underlined the importance of road marking visibility. Inadequate lane recognition by ADAS was associated with very low contrast ratio of road markings indeed. Importantly, specific minimum contrast ratio value could not be found, which was due to the complexity of ADAS algorithms...
Enhancing Dataset Distillation via Label Inconsistency Elimination and Learning Pattern Refinement
Authors: Chuhao Zhou, Chenxi Jiang, Yi Xie, Haozhi Cao, Jianfei Yang
Date: 2024-10-17 00:00:00
Tasks: Dataset Distillation

Dataset Distillation (DD) seeks to create a condensed dataset that, when used to train a model, enables the model to achieve performance similar to that of a model trained on the entire original dataset. It relieves the model training from processing massive data and thus reduces the computation resources, storage, and time costs. This paper illustrates our solution that ranks 1st in the ECCV-2024 Data Distillation Challenge (track 1). Our solution, Modified Difficulty-Aligned Trajectory Matching (M-DATM), introduces two key modifications to the original state-of-the-art method DATM: (1) the soft labels learned by DATM do not achieve one-to-one correspondence with the counterparts generated by the official evaluation script, so we remove the soft labels technique to alleviate such inconsistency; (2) since the removal of soft labels makes it harder for the synthetic dataset to learn late trajectory information, particularly on Tiny ImageNet, we reduce the matching range, allowing the synthetic data to concentrate more on the easier patterns. In the final evaluation, our M-DATM achieved accuracies of 0.4061 and 0.1831 on the CIFAR-100 and Tiny ImageNet datasets, ranking 1st in the Fixed Images Per Class (IPC) Track.
Reference-Based Post-OCR Processing with LLM for Diacritic Languages
Authors: Thao Do
Date: 2024-10-17 00:00:00
Tasks: Optical Character Recognition (OCR)

Extracting fine-grained OCR text from aged documents in diacritic languages remains challenging due to unexpected artifacts, time-induced degradation, and lack of datasets. While standalone spell correction approaches have been proposed, they show limited performance for historical documents due to numerous possible OCR error combinations and differences between modern and classical corpus distributions. We propose a method utilizing available content-focused ebooks as a reference base to correct imperfect OCR-generated text, supported by large language models. This technique generates high-precision pseudo-page-to-page labels for diacritic languages, where small strokes pose significant challenges in historical conditions. The pipeline eliminates various types of noise from aged documents and addresses issues such as missing characters, words, and disordered sequences. Our post-processing method, which generated a large OCR dataset of classical Vietnamese books, achieved a mean grading score of 8.72 on a 10-point scale. This outperformed the state-of-the-art transformer-based Vietnamese spell correction model, which scored 7.03 when evaluated on a sampled subset of the dataset. We also trained a baseline OCR model to assess and compare it with well-known engines. Experimental results demonstrate the strength of our baseline model compared to widely used open-source solutions. The resulting dataset will be released publicly to support future studies.
LESS: Label-Efficient and Single-Stage Referring 3D Segmentation
Authors: Xuexun Liu, Xiaoxu Xu, Jinlong Li, Qiudan Zhang, Xu Wang, Nicu Sebe, Lin Ma
Date: 2024-10-17 00:00:00
Tasks: cross-modal alignment, Instance Segmentation, Segmentation, Semantic Segmentation, Sentence

Referring 3D Segmentation is a visual-language task that segments all points of the specified object from a 3D point cloud described by a sentence of query. Previous works perform a two-stage paradigm, first conducting language-agnostic instance segmentation then matching with given text query. However, the semantic concepts from text query and visual cues are separately interacted during the training, and both instance and semantic labels for each object are required, which is time consuming and human-labor intensive. To mitigate these issues, we propose a novel Referring 3D Segmentation pipeline, Label-Efficient and Single-Stage, dubbed LESS, which is only under the supervision of efficient binary mask. Specifically, we design a Point-Word Cross-Modal Alignment module for aligning the fine-grained features of points and textual embedding. Query Mask Predictor module and Query-Sentence Alignment module are introduced for coarse-grained alignment between masks and query. Furthermore, we propose an area regularization loss, which coarsely reduces irrelevant background predictions on a large scale. Besides, a point-to-point contrastive loss is proposed concentrating on distinguishing points with subtly similar features. Through extensive experiments, we achieve state-of-the-art performance on ScanRefer dataset by surpassing the previous methods about 3.7% mIoU using only binary labels. Code is available at https://github.com/mellody11/LESS.
Composing Novel Classes: A Concept-Driven Approach to Generalized Category Discovery
Authors: Chuyu Zhang, Peiyan Gu, Xueyang Yu, Xuming He
Date: 2024-10-17 00:00:00

We tackle the generalized category discovery (GCD) problem, which aims to discover novel classes in unlabeled datasets by leveraging the knowledge of known classes. Previous works utilize the known class knowledge through shared representation spaces. Despite their progress, our analysis experiments show that novel classes can achieve impressive clustering results on the feature space of a known class pre-trained model, suggesting that existing methods may not fully utilize known class knowledge. To address it, we introduce a novel concept learning framework for GCD, named ConceptGCD, that categorizes concepts into two types: derivable and underivable from known class concepts, and adopts a stage-wise learning strategy to learn them separately. Specifically, our framework first extracts known class concepts by a known class pre-trained model and then produces derivable concepts from them by a generator layer with a covariance-augmented loss. Subsequently, we expand the generator layer to learn underivable concepts in a balanced manner ensured by a concept score normalization strategy and integrate a contrastive loss to preserve previously learned concepts. Extensive experiments on various benchmark datasets demonstrate the superiority of our approach over the previous state-of-the-art methods. Code will be available soon.
Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization
Authors: Yanan Guo, Ying Xie, Ying Chang, Benkui Zhang, Bo Jia, Lin Cao
Date: 2024-10-17 00:00:00
Tasks: Novel View Synthesis

Novel view synthesis has made significant progress in the field of 3D computer vision. However, the rendering of view-consistent novel views from imperfect camera poses remains challenging. In this paper, we introduce a hybrid bundle-adjusting 3D Gaussians model that enables view-consistent rendering with pose optimization. This model jointly extract image-based and neural 3D representations to simultaneously generate view-consistent images and camera poses within forward-facing scenes. The effective of our model is demonstrated through extensive experiments conducted on both real and synthetic datasets. These experiments clearly illustrate that our model can effectively optimize neural scene representations while simultaneously resolving significant camera pose misalignments. The source code is available at https://github.com/Bistu3DV/hybridBA.