pwc-archive/papers-with-abstracts huggingface dataset:
PARA-Drive: Parallelized Architecture for Real-time Autonomous Driving
Authors: Xinshuo Weng, Boris Ivanovic, Yan Wang, Yue Wang, Marco Pavone
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Autonomous Driving, NavSim

    Recent works have proposed end-to-end autonomous vehicle (AV) architectures comprised of differentiable modules achieving state-of-the-art driving performance. While they provide advantages over the traditional perception-prediction-planning pipeline (e.g. removing information bottlenecks between components and alleviating integration challenges) they do so using a diverse combination of tasks modules and their interconnectivity. As of yet however there has been no systematic analysis of the necessity of these modules or the impact of their connectivity placement and internal representations on overall driving performance. Addressing this gap our work conducts a comprehensive exploration of the design space of end-to-end modular AV stacks. Our findings culminate in the development of PARA-Drive: a fully parallel end-to-end AV architecture. PARA-Drive not only achieves state-of-the-art performance in perception prediction and planning but also significantly enhances runtime speed by nearly 3x without compromising on interpretability or safety.
Cross Initialization for Face Personalization of Text-to-Image Models
Authors: Lianyu Pang, Jian Yin, Haoran Xie, Qiping Wang, Qing Li, Xudong Mao
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    Recently there has been a surge in face personalization techniques benefiting from the advanced capabilities of pretrained text-to-image diffusion models. Among these a notable method is Textual Inversion which generates personalized images by inverting given images into textual embeddings. However methods based on Textual Inversion still struggle with balancing the trade-off between reconstruction quality and editability. In this study we examine this issue through the lens of initialization. Upon closely examining traditional initialization methods we identified a significant disparity between the initial and learned embeddings in terms of both scale and orientation. The scale of the learned embedding can be up to 100 times greater than that of the initial embedding. Such a significant change in the embedding could increase the risk of overfitting thereby compromising the editability. Driven by this observation we introduce a novel initialization method termed Cross Initialization that significantly narrows the gap between the initial and learned embeddings. This method not only improves both reconstruction and editability but also reduces the optimization steps from 5000 to 320. Furthermore we apply a regularization term to keep the learned embedding close to the initial embedding. We show that when combined with Cross Initialization this regularization term can effectively improve editability. We provide comprehensive empirical evidence to demonstrate the superior performance of our method compared to the baseline methods. Notably in our experiments Cross Initialization is the only method that successfully edits an individual's facial expression. Additionally a fast version of our method allows for capturing an input image in roughly 26 seconds while surpassing the baseline methods in terms of both reconstruction and editability. Code is available at https://github.com/lyuPang/CrossInitialization.
When Visual Grounding Meets Gigapixel-level Large-scale Scenes: Benchmark and Approach
Authors: Tao Ma, Bing Bai, Haozhe Lin, Heyuan Wang, Yu Wang, Lin Luo, Lu Fang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Scene Understanding, Visual Grounding

    Visual grounding refers to the process of associating natural language expressions with corresponding regions within an image. Existing benchmarks for visual grounding primarily operate within small-scale scenes with a few objects. Nevertheless recent advances in imaging technology have enabled the acquisition of gigapixel-level images providing high-resolution details in large-scale scenes containing numerous objects. To bridge this gap between imaging and computer vision benchmarks and make grounding more practically valuable we introduce a novel dataset named GigaGrounding designed to challenge visual grounding models in gigapixel-level large-scale scenes. We extensively analyze and compare the dataset with existing benchmarks demonstrating that GigaGrounding presents unique challenges such as large-scale scene understanding gigapixel-level resolution significant variations in object scales and the "multi-hop expressions". Furthermore we introduced a simple yet effective grounding approach which employs a "glance-to-zoom-in" paradigm and exhibits enhanced capabilities for addressing the GigaGrounding task. The dataset is available at www.gigavision.ai.
SRTube: Video-Language Pre-Training with Action-Centric Video Tube Features and Semantic Role Labeling
Authors: Ju-Hee Lee, Je-Won Kang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Semantic Role Labeling, Text Matching

    In recent years large-scale video-language pre-training (VidLP) has received considerable attention for its effectiveness in relevant tasks. In this paper we propose a novel action-centric VidLP framework that employs video tube features for temporal modeling and language features based on semantic role labeling (SRL). Our video encoder generates multiple tube features along object trajectories identifying action-related regions within videos to overcome the limitations of existing temporal attention mechanisms. Additionally our text encoder incorporates high-level action-related language knowledge previously underutilized in current VidLP models. The SRL captures action-verbs and related semantics among objects in sentences and enhances the ability to perform instance-level text matching thus enriching the cross-modal (CM) alignment process. We also introduce two novel pre-training objectives and a self-supervision strategy to produce a more faithful CM representation. Experimental results demonstrate that our method outperforms existing VidLP frameworks in various downstream tasks and datasets establishing our model a baseline in the modern VidLP framework.
Targeted Representation Alignment for Open-World Semi-Supervised Learning
Authors: Ruixuan Xiao, Lei Feng, Kai Tang, Junbo Zhao, Yixuan Li, Gang Chen, Haobo Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Maximum Separation, Open-World Semi-Supervised Learning

    Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data while unlabeled samples are not only from the labeled known categories but also from novel categories previously unseen. Despite the promise current approaches solely rely on hazardous similarity-based clustering algorithms and give unlabeled samples free rein to spontaneously group into distinct novel class clusters. Nevertheless due to the absence of novel class supervision these methods typically suffer from the representation collapse dilemma---features of different novel categories can get closely intertwined and indistinguishable even collapsing into the same cluster and leading to degraded performance. To alleviate this we propose a novel framework TRAILER which targets to attain an optimal feature arrangement revealed by the recently uncovered neural collapse phenomenon. To fulfill this we adopt targeted prototypes that are pre-assigned uniformly with maximum separation and then progressively align the representations to them. To further tackle the potential downsides of such stringent alignment we encapsulate a sample-target allocation mechanism with coarse-to-fine refinery that is able to infer label assignments with high quality. Extensive experiments demonstrate that TRAILER outperforms current state-of-the-art methods on generic and fine-grained benchmarks. The code is available at https://github.com/Justherozen/TRAILER.
CoralSCOP: Segment any COral Image on this Planet
Authors: Ziqiang Zheng, Haixin Liang, Binh-Son Hua, Yue Him Wong, Put Ang Jr, Apple Pui Yi Chui, Sai-Kit Yeung
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    Underwater visual understanding has recently gained increasing attention within the computer vision community for studying and monitoring underwater ecosystems. Among these coral reefs play an important and intricate role often referred to as the rainforests of the sea due to their rich biodiversity and crucial environmental impact. Existing coral analysis due to its technical complexity requires significant manual work from coral biologists therefore hindering scalable and comprehensive studies. In this paper we introduce CoralSCOP the first foundation model designed for the automatic dense segmentation of coral reefs. CoralSCOP is developed to accurately assign labels to different coral entities addressing the challenges in the semantic analysis of coral imagery. Its main objective is to identify and delineate the irregular boundaries between various coral individuals across different granularities such as coral/non-coral growth form and genus. This task is challenging due to the semantic agnostic nature or fixed limited semantic categories of previous generic segmentation methods which fail to adequately capture the complex characteristics of coral structures. By introducing a novel parallel semantic branch CoralSCOP can produce high-quality coral masks with semantics that enable a wide range of downstream coral reef analysis tasks. We demonstrate that CoralSCOP exhibits a strong zero-shot ability to segment unseen coral images. To effectively train our foundation model we propose CoralMask a new dataset with 41297 densely labeled coral images and 330144 coral masks. We have conducted comprehensive and extensive experiments to demonstrate the advantages of CoralSCOP over existing generalist segmentation algorithms and coral reef analytical approaches.
HIT: Estimating Internal Human Implicit Tissues from the Body Surface
Authors: Marilyn Keller, Vaibhav Arora, Abdelmouttaleb Dakri, Shivam Chandhok, Jürgen Machann, Andreas Fritsche, Michael J. Black, Sergi Pujades
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Anatomy

    The creation of personalized anatomical digital twins is important in the fields of medicine computer graphics sports science and biomechanics. To observe a subject's anatomy expensive medical devices (MRI or CT) are required and the creation of the digital model is often time-consuming and involves manual effort. Instead we leverage the fact that the shape of the body surface is correlated with the internal anatomy; e.g. from surface observations alone one can predict body composition and skeletal structure. In this work we go further and learn to infer the 3D location of three important anatomic tissues: subcutaneous adipose tissue (fat) lean tissue (muscles and organs) and long bones. To learn to infer these tissues we tackle several key challenges. We first create a dataset of human tissues by segmenting full-body MRI scans and registering the SMPL body mesh to the body surface. With this dataset we train HIT (Human Implicit Tissues) an implicit function that given a point inside a body predicts its tissue class. HIT leverages the SMPL body model shape and pose parameters to canonicalize the medical data. Unlike SMPL which is trained from upright 3D scans MRI scans are acquired with subjects lying on a table resulting in significant soft-tissue deformation. Consequently HIT uses a learned volumetric deformation field that undoes these deformations. Since HIT is parameterized by SMPL we can repose bodies or change the shape of subjects and the internal structures deform appropriately. We perform extensive experiments to validate HIT's ability to predict a plausible internal structure for novel subjects. The dataset and HIT model are available at https://hit.is.tue.mpg.de to foster future research in this direction.
Improved Self-Training for Test-Time Adaptation
Authors: Jing Ma
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Model Optimization, Pseudo Label, Test-time Adaptation

    Test-time adaptation (TTA) is a technique to improve the performance of a pre-trained source model on a target distribution without using any labeled data. However existing self-trained TTA methods often face the challenges of unreliable pseudo-labels and unstable model optimization. In this paper we propose an Improved Self-Training (IST) approach which addresses these challenges by enhancing the pseudo-label quality and stabilizing the adaptation process. Specifically we use a simple augmentation strategy to generate multiple views of each test sample and construct a graph structure to correct the pseudo-labels based on the similarity of the latent features. Moreover we adopt a parameter moving average scheme to smooth the model updates and prevent catastrophic forgetting. Instead of using a model with fixed label space we explore the adaptability of the foundation model CLIP to various downstream tasks at test time. Extensive experiments on various benchmarks show that IST can achieve significant and consistent improvements over the existing TTA methods in classification detection and segmentation tasks.
S2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering
Authors: Zhen Long, Qiyuan Wang, Yazhou Ren, Yipeng Liu, Ce Zhu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Clustering, CPU, Graph Similarity

    Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However current methods mainly seek the consensus embedding feature for clustering by exploring global correlations between anchor graphs or projection matrices.In this paper we propose a simple yet efficient scalable multi-view tensor clustering (S2MVTC) approach where our focus is on learning correlations of embedding features within and across views. Specifically we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it. Additionally we build a novel tensor low-frequency approximation (TLFA) operator which incorporates graph similarity into embedding feature learning efficiently achieving smooth representation of embedding features within different views. Furthermore consensus constraints are applied to embedding features to ensure inter-view semantic consistency. Experimental results on six large-scale multi-view datasets demonstrate that S2MVTC significantly outperforms state-of-the-art algorithms in terms of clustering performance and CPU execution time especially when handling massive data. The code of S2MVTC is publicly available at https://github.com/longzhen520/S2MVTC.
Positive-Unlabeled Learning by Latent Group-Aware Meta Disambiguation
Authors: Lin Long, Haobo Wang, Zhijie Jiang, Lei Feng, Chang Yao, Gang Chen, Junbo Zhao
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Binary Classification, Contrastive Learning, Meta-Learning

    Positive-Unlabeled (PU) learning aims to train a binary classifier using minimal positive data supplemented by a substantially larger pool of unlabeled data in the specific absence of explicitly annotated negatives. Despite its straightforward nature as a binary classification task the currently best-performing PU algorithms still largely lag behind the supervised counterpart. In this work we identify that the primary bottleneck lies in the difficulty of deriving discriminative representations under unreliable binary supervision with poor semantics which subsequently hinders the common label disambiguation procedures. To cope with this problem we propose a novel PU learning framework namely Latent Group-Aware Meta Disambiguation (LaGAM) which incorporates a hierarchical contrastive learning module to extract the underlying grouping semantics within PU data and produce compact representations. As a result LaGAM enables a more aggressive label disambiguation strategy where we enhance the robustness of training by iteratively distilling the true labels of unlabeled data directly through meta-learning. Extensive experiments show that LaGAM significantly outperforms the current state-of-the-art methods by an average of 6.8% accuracy on common benchmarks approaching the supervised baseline. We also provide comprehensive ablations as well as visualized analysis to verify the effectiveness of our LaGAM.
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity Training
Authors: Di Ming, Peng Ren, Yunlong Wang, Xin Feng
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Adversarial Attack, image-classification, Image Classification, object-detection, Object Detection, Quantization, Semantic Segmentation

    Deep neural networks (DNNs) are vulnerable to highly transferable adversarial attacks. Especially many studies have shown that sparse attacks pose a significant threat to DNNs on account of their exceptional imperceptibility. Current sparse attack methods mostly limit only the magnitude and number of perturbations while generally overlooking the location of the perturbations resulting in decreased performances on attack transferability. A subset of studies indicates that perturbations existing in the significant regions with rich classification-relevant features are more effective. Leveraging this insight we introduce the structural sparsity constraint in the framework of generative models to limit the perturbation positions. To ensure that the perturbations are generated towards classification-relevant regions we propose an exact group sparsity training method to learn pixel-level and group-level sparsity. For purpose of improving the effectiveness of sparse training we further put forward masked quantization network and multi-stage optimization algorithm in the training process. Utilizing CNNs as surrogate models extensive experiments demonstrate that our method has higher transferability in image classification attack compared to state-of-the-art methods at approximately same sparsity levels. In cross-model ViT object detection and semantic segmentation attack tasks we also achieve a better attack success rate. Code is available at https://github.com/MisterRpeng/EGS-TSSA.
Unknown Prompt the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
Authors: Mainak Singha, Ankit Jha, Shirsha Bose, Ashwin Nair, Moloud Abdar, Biplab Banerjee
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Domain Generalization, Language Modelling, Multi-class Classification, Prompt Learning

    We delve into Open Domain Generalization (ODG) marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to ODG face limitations due to constrained generalizations of traditional CNN backbones and errors in detecting target open samples in the absence of prior knowledge. Addressing these pitfalls we introduce ODG-CLIP harnessing the semantic prowess of the vision-language model CLIP. Our framework brings forth three primary innovations: Firstly distinct from prevailing paradigms we conceptualize ODG as a multi-class classification challenge encompassing both known and novel categories. Central to our approach is modeling a unique prompt tailored for detecting unknown class samples and to train this we employ a readily accessible stable diffusion model elegantly generating proxy images for the open class. Secondly aiming for domain-tailored classification (prompt) weights while ensuring a balance of precision and simplicity we devise a novel visual style-centric prompt learning mechanism. Finally we infuse images with class-discriminative knowledge derived from the prompt space to augment the fidelity of CLIP's visual embeddings. We introduce a novel objective to safeguard the continuity of this infused semantic intel across domains especially for the shared classes. Through rigorous testing on diverse datasets covering closed and open-set DG contexts ODG-CLIP demonstrates clear supremacy consistently outpacing peers with performance boosts between 8%-16%. Code will be available at https://github.com/mainaksingha01/ODG-CLIP.
Fine-Grained Bipartite Concept Factorization for Clustering
Authors: Chong Peng, Pengfei Zhang, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Clustering, graph partitioning

    In this paper we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph which provides comprehensive and complementary neighbor information of the data. The factor matrices are learned with bipartite graph partitioning which exploits explicit cluster structure of the data and is more geared towards clustering application. We develop an effective and efficient optimization algorithm for our method and provide elegant theoretical results about the convergence. Extensive experimental results confirm the effectiveness of the proposed method.
Just Add ?! Pose Induced Video Transformers for Understanding Activities of Daily Living
Authors: Dominick Reilly, Srijan Das
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Action Recognition, Temporal Action Localization

    Video transformers have become the de facto standard for human action recognition yet their exclusive reliance on the RGB modality still limits their adoption in certain domains. One such domain is Activities of Daily Living (ADL) where RGB alone is not sufficient to distinguish between visually similar actions or actions observed from multiple viewpoints. To facilitate the adoption of video transformers for ADL we hypothesize that the augmentation of RGB with human pose information known for its sensitivity to fine-grained motion and multiple viewpoints is essential. Consequently we introduce the first Pose Induced Video Transformer: PI-ViT (or ?-ViT) a novel approach that augments the RGB representations learned by video transformers with 2D and 3D pose information. The key elements of ?-ViT are two plug-in modules 2D Skeleton Induction Module and 3D Skeleton Induction Module that are responsible for inducing 2D and 3D pose information into the RGB representations. These modules operate by performing pose-aware auxiliary tasks a design choice that allows ?-ViT to discard the modules during inference. Notably ?-ViT achieves the state-of-the-art performance on three prominent ADL datasets encompassing both real-world and large-scale RGB-D datasets without requiring poses or additional computational overhead at inference.