pwc-archive/papers-with-abstracts huggingface dataset:
Instance Tracking in 3D Scenes from Egocentric Videos
Authors: Yunhan Zhao, Haoyu Ma, Shu Kong, Charless Fowlkes
Date: 2023-12-07 00:00:00
Proceeding: CVPR 2024 1
Tasks: Human-Object Interaction Detection, Object Tracking

Egocentric sensors such as AR/VR devices capture human-object interactions and offer the potential to provide task-assistance by recalling 3D locations of objects of interest in the surrounding environment. This capability requires instance tracking in real-world 3D scenes from egocentric videos (IT3DEgo). We explore this problem by first introducing a new benchmark dataset, consisting of RGB and depth videos, per-frame camera pose, and instance-level annotations in both 2D camera and 3D world coordinates. We present an evaluation protocol which evaluates tracking performance in 3D coordinates with two settings for enrolling instances to track: (1) single-view online enrollment where an instance is specified on-the-fly based on the human wearer's interactions. and (2) multi-view pre-enrollment where images of an instance to be tracked are stored in memory ahead of time. To address IT3DEgo, we first re-purpose methods from relevant areas, e.g., single object tracking (SOT) -- running SOT methods to track instances in 2D frames and lifting them to 3D using camera pose and depth. We also present a simple method that leverages pretrained segmentation and detection models to generate proposals from RGB frames and match proposals with enrolled instance images. Our experiments show that our method (with no finetuning) significantly outperforms SOT-based approaches in the egocentric setting. We conclude by arguing that the problem of egocentric instance tracking is made easier by leveraging camera pose and using a 3D allocentric (world) coordinate representation.
Multi-strategy Collaborative Optimized YOLOv5s and its Application in Distance Estimation
Authors: Zijian Shen, Zhenping Mu, Xiangxiang Li
Date: 2023-12-07 00:00:00
Tasks: vehicle detection

The increasing accident rate brought about by the explosive growth of automobiles has made the research on active safety systems of automobiles increasingly important. The importance of improving the accuracy of vehicle target detection is self-evident. To achieve the goals of vehicle detection and distance estimation and provide safety warnings, a Distance Estimation Safety Warning System (DESWS) based on a new neural network model (YOLOv5s-SE) by replacing the IoU with DIoU, embedding SE attention module, and a distance estimation method through using the principle of similar triangles was proposed. In addition, a method that can give safety suggestions based on the estimated distance using nonparametric testing was presented in this work. Through the simulation experiment, it was verified that the mAP was improved by 5.5% and the purpose of giving safety suggestions based on the estimated distance information can be achieved.
Do Not DeepFake Me: Privacy-Preserving Neural 3D Head Reconstruction Without Sensitive Images
Authors: Jiayi Kong, Xurui Song, Shuo Huai, Baixin Xu, Jun Luo, Ying He
Date: 2023-12-07 00:00:00
Tasks: Face Swapping, Privacy Preserving

While 3D head reconstruction is widely used for modeling, existing neural reconstruction approaches rely on high-resolution multi-view images, posing notable privacy issues. Individuals are particularly sensitive to facial features, and facial image leakage can lead to many malicious activities, such as unauthorized tracking and deepfake. In contrast, geometric data is less susceptible to misuse due to its complex processing requirements, and absence of facial texture features. In this paper, we propose a novel two-stage 3D facial reconstruction method aimed at avoiding exposure to sensitive facial information while preserving detailed geometric accuracy. Our approach first uses non-sensitive rear-head images for initial geometry and then refines this geometry using processed privacy-removed gradient images. Extensive experiments show that the resulting geometry is comparable to methods using full images, while the process is resistant to DeepFake applications and facial recognition (FR) systems, thereby proving its effectiveness in privacy protection.
Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection
Authors: Tuan Hoang, Santu Rana, Sunil Gupta, Svetha Venkatesh
Date: 2023-12-07 00:00:00
Tasks: Machine Unlearning

Recent data-privacy laws have sparked interest in machine unlearning, which involves removing the effect of specific training samples from a learnt model as if they were never present in the original training dataset. The challenge of machine unlearning is to discard information about the ``forget'' data in the learnt model without altering the knowledge about the remaining dataset and to do so more efficiently than the naive retraining approach. To achieve this, we adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU), in which the model takes steps in the orthogonal direction to the gradient subspaces deemed unimportant for the retaining dataset, so as to its knowledge is preserved. By utilizing Stochastic Gradient Descent (SGD) to update the model weights, our method can efficiently scale to any model and dataset size. We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible. Our code is available at https://github.com/hnanhtuan/projected_gradient_unlearning.
Open-Vocabulary Segmentation with Semantic-Assisted Calibration
Authors: Yong liu, Sule Bai, Guanbin Li, Yitong Wang, Yansong Tang
Date: 2023-12-07 00:00:00
Proceeding: CVPR 2024 1
Tasks: Attribute, Open Vocabulary Semantic Segmentation

This paper studies open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with generalized contextual prior of CLIP. As the core of open-vocabulary understanding, alignment of visual content with the semantics of unbounded text has become the bottleneck of this field. To address this challenge, recent works propose to utilize CLIP as an additional classifier and aggregate model predictions with CLIP classification results. Despite their remarkable progress, performance of OVS methods in relevant scenarios is still unsatisfactory compared with supervised counterparts. We attribute this to the in-vocabulary embedding and domain-biased CLIP prediction. To this end, we present a Semantic-assisted CAlibration Network (SCAN). In SCAN, we incorporate generalized semantic prior of CLIP into proposal embedding to avoid collapsing on known categories. Besides, a contextual shift strategy is applied to mitigate the lack of global context and unnatural background noise. With above designs, SCAN achieves state-of-the-art performance on all popular open-vocabulary segmentation benchmarks. Furthermore, we also focus on the problem of existing evaluation system that ignores semantic duplication across categories, and propose a new metric called Semantic-Guided IoU (SG-IoU).
MEVG: Multi-event Video Generation with Text-to-Video Models
Authors: Gyeongrok Oh, Jaehwan Jeong, Sieun Kim, Wonmin Byeon, Jinkyu Kim, Sungwoong Kim, Sangpil Kim
Date: 2023-12-07 00:00:00
Tasks: Video Generation

We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a pre-trained diffusion-based text-to-video generative model without a fine-tuning process. Specifically, we propose a last frame-aware diffusion process to preserve visual coherence between consecutive videos where each video consists of different events by initializing the latent and simultaneously adjusting noise in the latent to enhance the motion dynamic in a generated video. Furthermore, we find that the iterative update of latent vectors by referring to all the preceding frames maintains the global appearance across the frames in a video clip. To handle dynamic text input for video generation, we utilize a novel prompt generator that transfers course text messages from the user into the multiple optimal prompts for the text-to-video diffusion model. Extensive experiments and user studies show that our proposed method is superior to other video-generative models in terms of temporal coherency of content and semantics. Video examples are available on our project page: https://kuai-lab.github.io/eccv2024mevg.
Large Language Models are Good Prompt Learners for Low-Shot Image Classification
Authors: Zhaoheng Zheng, Jingmin Wei, Xuefeng Hu, Haidong Zhu, Ram Nevatia
Date: 2023-12-07 00:00:00
Proceeding: CVPR 2024 1
Tasks: Classification, Few-Shot Image Classification, image-classification, Image Classification, Prompt Learning, Zero-shot Generalization

Low-shot image classification, where training images are limited or inaccessible, has benefited from recent progress on pre-trained vision-language (VL) models with strong generalizability, e.g. CLIP. Prompt learning methods built with VL models generate text features from the class names that only have confined class-specific information. Large Language Models (LLMs), with their vast encyclopedic knowledge, emerge as the complement. Thus, in this paper, we discuss the integration of LLMs to enhance pre-trained VL models, specifically on low-shot classification. However, the domain gap between language and vision blocks the direct application of LLMs. Thus, we propose LLaMP, Large Language Models as Prompt learners, that produces adaptive prompts for the CLIP text encoder, establishing it as the connecting bridge. Experiments show that, compared with other state-of-the-art prompt learning methods, LLaMP yields better performance on both zero-shot generalization and few-shot image classification, over a spectrum of 11 datasets. Code will be made available at: https://github.com/zhaohengz/LLaMP.
Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidance
Authors: Thomas Westfechtel, Dexuan Zhang, Tatsuya Harada
Date: 2023-12-07 00:00:00
Tasks: Domain Adaptation, Knowledge Distillation, Language Modelling, Self-Knowledge Distillation, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) tries to overcome the tedious work of labeling data by leveraging a labeled source dataset and transferring its knowledge to a similar but different target dataset. Meanwhile, current vision-language models exhibit remarkable zero-shot prediction capabilities. In this work, we combine knowledge gained through UDA with the inherent knowledge of vision-language models. We introduce a strong-weak guidance learning scheme that employs zero-shot predictions to help align the source and target dataset. For the strong guidance, we expand the source dataset with the most confident samples of the target dataset. Additionally, we employ a knowledge distillation loss as weak guidance. The strong guidance uses hard labels but is only applied to the most confident predictions from the target dataset. Conversely, the weak guidance is employed to the whole dataset but uses soft labels. The weak guidance is implemented as a knowledge distillation loss with (shifted) zero-shot predictions. We show that our method complements and benefits from prompt adaptation techniques for vision-language models. We conduct experiments and ablation studies on three benchmarks (OfficeHome, VisDA, and DomainNet), outperforming state-of-the-art methods. Our ablation studies further demonstrate the contributions of different components of our algorithm.
An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything
Authors: Israt Zarin Era, Imtiaz Ahmed, Zhichao Liu, Srinjoy Das
Date: 2023-12-07 00:00:00
Tasks: Anomaly Detection, Astronomy, Image Segmentation, Segmentation, Semantic Segmentation

Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-generated prompts to enhance their performance. In the Laser Additive Manufacturing (LAM) domain, accurate image-based defect segmentation is imperative to ensure product quality and facilitate real-time process control. However, such tasks are often characterized by multiple challenges including the absence of labels and the requirement for low latency inference among others. Porosity is a very common defect in LAM due to lack of fusion, entrapped gas, and keyholes, directly affecting mechanical properties like tensile strength, stiffness, and hardness, thereby compromising the quality of the final product. To address these issues, we construct a framework for image segmentation using a state-of-the-art Vision Transformer (ViT) based Foundation model (Segment Anything Model) with a novel multi-point prompt generation scheme using unsupervised clustering. Utilizing our framework we perform porosity segmentation in a case study of laser-based powder bed fusion (L-PBF) and obtain high accuracy without using any labeled data to guide the prompt tuning process. By capitalizing on lightweight foundation model inference combined with unsupervised prompt generation, we envision constructing a real-time anomaly detection pipeline that could revolutionize current laser additive manufacturing processes, thereby facilitating the shift towards Industry 4.0 and promoting defect-free production along with operational efficiency.
Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching
Authors: Junsheng Zhou, Baorui Ma, Wenyuan Zhang, Yi Fang, Yu-Shen Liu, Zhizhong Han
Date: 2023-12-07 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Triplet

Cross-modality registration between 2D images from cameras and 3D point clouds from LiDARs is a crucial task in computer vision and robotic. Previous methods estimate 2D-3D correspondences by matching point and pixel patterns learned by neural networks, and use Perspective-n-Points (PnP) to estimate rigid transformation during post-processing. However, these methods struggle to map points and pixels to a shared latent space robustly since points and pixels have very different characteristics with patterns learned in different manners (MLP and CNN), and they also fail to construct supervision directly on the transformation since the PnP is non-differentiable, which leads to unstable registration results. To address these problems, we propose to learn a structured cross-modality latent space to represent pixel features and 3D features via a differentiable probabilistic PnP solver. Specifically, we design a triplet network to learn VoxelPoint-to-Pixel matching, where we represent 3D elements using both voxels and points to learn the cross-modality latent space with pixels. We design both the voxel and pixel branch based on CNNs to operate convolutions on voxels/pixels represented in grids, and integrate an additional point branch to regain the information lost during voxelization. We train our framework end-to-end by imposing supervisions directly on the predicted pose distribution with a probabilistic PnP solver. To explore distinctive patterns of cross-modality features, we design a novel loss with adaptive-weighted optimization for cross-modality feature description. The experimental results on KITTI and nuScenes datasets show significant improvements over the state-of-the-art methods. The code and models are available at https://github.com/junshengzhou/VP2P-Match.
Residual Graph Convolutional Network for Bird's-Eye-View Semantic Segmentation
Authors: Qiuxiao Chen, Xiaojun Qi
Date: 2023-12-07 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Bird's-Eye View Semantic Segmentation, Data Augmentation, Segmentation, Semantic Segmentation

Retrieving spatial information and understanding the semantic information of the surroundings are important for Bird's-Eye-View (BEV) semantic segmentation. In the application of autonomous driving, autonomous vehicles need to be aware of their surroundings to drive safely. However, current BEV semantic segmentation techniques, deep Convolutional Neural Networks (CNNs) and transformers, have difficulties in obtaining the global semantic relationships of the surroundings at the early layers of the network. In this paper, we propose to incorporate a novel Residual Graph Convolutional (RGC) module in deep CNNs to acquire both the global information and the region-level semantic relationship in the multi-view image domain. Specifically, the RGC module employs a non-overlapping graph space projection to efficiently project the complete BEV information into graph space. It then builds interconnected spatial and channel graphs to extract spatial information between each node and channel information within each node (i.e., extract contextual relationships of the global features). Furthermore, it uses a downsample residual process to enhance the coordinate feature reuse to maintain the global information. The segmentation data augmentation and alignment module helps to simultaneously augment and align BEV features and ground truth to geometrically preserve their alignment to achieve better segmentation results. Our experimental results on the nuScenes benchmark dataset demonstrate that the RGC network outperforms four state-of-the-art networks and its four variants in terms of IoU and mIoU. The proposed RGC network achieves a higher mIoU of 3.1% than the best state-of-the-art network, BEVFusion. Code and models will be released.
DiffusionPhase: Motion Diffusion in Frequency Domain
Authors: Weilin Wan, Yiming Huang, Shutong Wu, Taku Komura, Wenping Wang, Dinesh Jayaraman, Lingjie Liu
Date: 2023-12-07 00:00:00
Tasks: Diversity, Motion Generation

In this study, we introduce a learning-based method for generating high-quality human motion sequences from text descriptions (e.g., ``A person walks forward"). Existing techniques struggle with motion diversity and smooth transitions in generating arbitrary-length motion sequences, due to limited text-to-motion datasets and the pose representations used that often lack expressiveness or compactness. To address these issues, we propose the first method for text-conditioned human motion generation in the frequency domain of motions. We develop a network encoder that converts the motion space into a compact yet expressive parameterized phase space with high-frequency details encoded, capturing the local periodicity of motions in time and space with high accuracy. We also introduce a conditional diffusion model for predicting periodic motion parameters based on text descriptions and a start pose, efficiently achieving smooth transitions between motion sequences associated with different text descriptions. Experiments demonstrate that our approach outperforms current methods in generating a broader variety of high-quality motions, and synthesizing long sequences with natural transitions.
ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Authors: Mingwu Zheng, Haiyu Zhang, Hongyu Yang, Liming Chen, Di Huang
Date: 2023-12-07 00:00:00
Tasks: Face Model, Face Reconstruction

Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications. However, the challenges persist due to the limitations imposed by data discretization and model linearity, which hinder the precise capture of identity and expression clues in current studies. This paper presents a novel 3D morphable face model, named ImFace++, to learn a sophisticated and continuous space with implicit neural representations. ImFace++ first constructs two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, which simultaneously facilitate automatic learning of point-to-point correspondences across diverse facial shapes. To capture more sophisticated facial details, a refinement displacement field within the template space is further incorporated, enabling fine-grained learning of individual-specific facial details. Furthermore, a Neural Blend-Field is designed to reinforce the representation capabilities through adaptive blending of an array of local fields. In addition to ImFace++, we devise an improved learning strategy to extend expression embeddings, allowing for a broader range of expression variations. Comprehensive qualitative and quantitative evaluation demonstrates that ImFace++ significantly advances the state-of-the-art in terms of both face reconstruction fidelity and correspondence accuracy.
PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation
Authors: Ardian Umam, Cheng-Kun Yang, Min-Hung Chen, Jen-Hui Chuang, Yen-Yu Lin
Date: 2023-12-07 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Part Segmentation, Language Modeling, Language Modelling, Segmentation

This paper proposes a cross-modal distillation framework, PartDistill, which transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D shape part segmentation. PartDistill addresses three major challenges in this task: the lack of 3D segmentation in invisible or undetected regions in the 2D projections, inconsistent 2D predictions by VLMs, and the lack of knowledge accumulation across different 3D shapes. PartDistill consists of a teacher network that uses a VLM to make 2D predictions and a student network that learns from the 2D predictions while extracting geometrical features from multiple 3D shapes to carry out 3D part segmentation. A bi-directional distillation, including forward and backward distillations, is carried out within the framework, where the former forward distills the 2D predictions to the student network, and the latter improves the quality of the 2D predictions, which subsequently enhances the final 3D segmentation. Moreover, PartDistill can exploit generative models that facilitate effortless 3D shape creation for generating knowledge sources to be distilled. Through extensive experiments, PartDistill boosts the existing methods with substantial margins on widely used ShapeNetPart and PartNetE datasets, by more than 15% and 12% higher mIoU scores, respectively. The code for this work is available at https://github.com/ardianumam/PartDistill.
Natural-language-driven Simulation Benchmark and Copilot for Efficient Production of Object Interactions in Virtual Road Scenes
Authors: Kairui Yang, Zihao Guo, Gengjie Lin, Haotian Dong, Die Zuo, Jibin Peng, Zhao Huang, Zhecheng Xu, Fupeng Li, Ziyun Bai, Di Lin
Date: 2023-12-07 00:00:00
Tasks: Autonomous Driving, Object

We advocate the idea of the natural-language-driven(NLD) simulation to efficiently produce the object interactions between multiple objects in the virtual road scenes, for teaching and testing the autonomous driving systems that should take quick action to avoid collision with obstacles with unpredictable motions. The NLD simulation allows the brief natural-language description to control the object interactions, significantly reducing the human efforts for creating a large amount of interaction data. To facilitate the research of NLD simulation, we collect the Language-to-Interaction(L2I) benchmark dataset with 120,000 natural-language descriptions of object interactions in 6 common types of road topologies. Each description is associated with the programming code, which the graphic render can use to visually reconstruct the object interactions in the virtual scenes. As a methodology contribution, we design SimCopilot to translate the interaction descriptions to the renderable code. We use the L2I dataset to evaluate SimCopilot's abilities to control the object motions, generate complex interactions, and generalize interactions across road topologies. The L2I dataset and the evaluation results motivate the relevant research of the NLD simulation.