We introduce a novel framework for 3D human avatar generation and personalization, leveraging text prompts to enhance user engagement and customization. Central to our approach are key innovations aimed at overcoming the challenges in photo-realistic avatar synthesis. Firstly, we utilize a conditional Neural Radiance Fields (NeRF) model, trained on a large-scale unannotated multi-view dataset, to create a versatile initial solution space that accelerates and diversifies avatar generation. Secondly, we develop a geometric prior, leveraging the capabilities of Text-to-Image Diffusion Models, to ensure superior view invariance and enable direct optimization of avatar geometry. These foundational ideas are complemented by our optimization pipeline built on Variational Score Distillation (VSD), which mitigates texture loss and over-saturation issues. As supported by our extensive experiments, these strategies collectively enable the creation of custom avatars with unparalleled visual quality and better adherence to input text prompts.
论文检索
输入标题、作者或关键词，从 2,387 篇学术成果中精准定位
Tracking-by-detection (TBD) methods achieve state-of-the-art performance on 3D tracking benchmarks for autonomous driving. On the other hand, tracking-by-attention (TBA) methods have the potential to outperform TBD methods, particularly for long occlusions and challenging detection settings. This work investigates why TBA methods continue to lag in performance behind TBD methods using a LiDAR-based joint detector and tracker called JDT3D. Based on this analysis, we propose two generalizable methods to bridge the gap between TBD and TBA methods: track sampling augmentation and confidence-based query propagation. JDT3D is trained and evaluated on the nuScenes dataset, achieving 0.574 on the AMOTA metric on the nuScenes test set, outperforming all existing LiDAR-based TBA approaches by over 6%. Based on our results, we further discuss some potential challenges with the existing TBA model formulation to explain the continued gap in performance with TBD methods. The implementation of JDT3D can be found at the following link: https://anonymous.4open.science/r/JDT3D-FC12
Generative models are now widely used by graphic designers and artists. Prior works have shown that these models tend to remember and often replicate content from the training data during generation. Hence as their proliferation increase, it has become important to perform a database search to determine whether the properties of the image are attributable to specific training data, every time before a generated image is used for professional purposes. Existing tools for this purpose focus largely on retrieving images of similar semantic content. Meanwhile, many artists are concerned with the extent of style replication in text-to-image models. We present a framework to understand and extract style descriptors from images. Our framework comprises a new dataset curated using the insight that style is a subjective property of an image capturing complex yet meaningful interactions of factors including but not limited to colors, textures, shapes, etc. We also propose a method to extract style descriptors that can be used to attribute style of a generated image to the images used in the training dataset of a text-to-image model. We show promising results in various style retrieval tasks. We also quantitatively and qualitatively analyze style attribution and matching in the Stable Diffusion model.
Multi-agent trajectory prediction is crucial to autonomous driving and understanding the surrounding environment. In recent years, learning-based approaches for multi-agent trajectory prediction, such as primarily relying on graph neural networks, graph transformers, and hypergraph neural networks, have demonstrated outstanding performance on real-world datasets. However, hypergraph transformer-based method for trajectory prediction is yet to be explored. Therefore, we present a MultiscAle Relational Transformer (MART) network for multi-agent trajectory prediction. MART is a hypergraph transformer architecture to consider individual and group behaviors in transformer machinery. The core module of MART is the encoder, comprising a Pair-wise Relational Transformer (PRT) and a Hyper Relational Transformer (HRT). The encoder extends the capabilities of a relational transformer by introducing HRT, which integrates hyperedge features into the transformer mechanism, promoting the attention weights to focus on group-wise relations. In addition, we propose an Adaptive Group Estimator (AGE) designed to infer complex group relations in real-world environments. Extensive experiments on three real-world datasets (NBA, SDD, and ETH-UCY) demonstrate that our method achieves state-of-the-art performance, enhancing ADE/FDE by 3.9%/11.8% on the NBA dataset.
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and Retention
PDF ↗Scene Graph Generation (SGG) offers a structured representation critical in many computer vision applications.Traditional SGG approaches, however, are limited by a closed-set assumption, restricting their ability to recognize only predefined object and relation categories. To overcome this, we categorize SGG scenarios into four distinct settings based on the node and edge: Closed-set SGG, Open Vocabulary (object) Detection-based SGG (OvD-SGG), Open Vocabulary Relation-based SGG (OvR-SGG), and Open Vocabulary Detection + Relation-based SGG (OvD+R-SGG). While object-centric open vocabulary SGG has been studied recently, the more challenging problem of relation-involved open-vocabulary SGG remains relatively unexplored. To fill this gap, we propose a unified framework named OvSGTR towards fully open vocabulary SGG from a holistic view. The proposed framework is an end-to-end transformer architecture, which learns a visual-concept alignment for both nodes and edges, enabling the model to recognize unseen categories. For the more challenging settings of relation-involved open vocabulary SGG, the proposed approach integrates relation-aware pre-training utilizing image-caption data and retains visual-concept alignment through knowledge distillation. Comprehensive experimental results on the Visual Genome benchmark demonstrate the effectiveness and superiority of the proposed framework.
Semi-supervised learning (SSL) has shown its effectiveness in learning effective 3D representation from a small amount of labelled data while utilizing large unlabelled data. Traditional semi-supervised approaches rely on the fundamental concept of predicting pseudo-labels for unlabeled data and incorporating them into the learning process. However, we identify that the existing methods do not fully utilize all the unlabelled samples and consequently limit their potential performance. To address this issue, we propose AllMatch, a novel SSL-based 3D classification framework that effectively utilizes all the unlabelled samples. AllMatch comprises three modules: (1) an adaptive hard augmentation module that applies relatively hard augmentations to the high-confident unlabeled samples with lower loss values, thereby enhancing the contribution of such samples, (2) an inverse learning module that further improves the utilization of unlabelled data by learning what not to learn, and (3) a contrastive learning module that ensures learning from all the samples in both supervised and unsupervised settings. Comprehensive experiments on two popular 3D datasets demonstrate a performance improvement of up to 11.2% with 1% labelled data, surpassing the SOTA by a significant margin. Furthermore, AllMatch exhibits its efficiency in effectively leveraging all the unlabeled data, demonstrated by the fact that only 10% of labelled data reaches nearly the same performance as fully-supervised learning with all labelled data. The code of our work is available at: https://anonymous.4open.science/r/AllMatch.
Recent advancement in the large-scale pre-training model (such as CLIP) has significantly improved unsupervised domain adaptation (UDA) by leveraging the pre-trained knowledge to bridge the source and target domain gap. Catastrophic forgetting is the main challenge of CLIP in UDA where the traditional fine-tuning to adjust CLIP on a target domain can quickly override CLIP's pre-trained knowledge. To address the above issue, we propose to convert CLIP's features into high-dimensional vector (hypervector) space to utilize the robustness property of hypervector to mitigate catastrophic forgetting. We first study the feature dimension size in the hypervector space to empirically find the dimension threshold that allows enough feature patterns to be redundant to avoid excessive training (thus mitigating catastrophic forgetting). To further utilize the robustness of hypervector, we propose Discrepancy Reduction to reduce the domain shift between source and target domains, and Feature Augmentation to synthesize labeled target domain features from source domain features. We achieved the best results on four public UDA datasets, and we show the generalization of our method to other applications (few-shot learning, continual learning) and the model-agnostic property of our method across vision-language and vision backbones.
Frugal 3D Point Cloud Model Training via Progressive Near Point Filtering and Fused Aggregation
PDF ↗The increasing demand on higher accuracy and the rapid growth of 3D point cloud datasets have led to significantly higher training costs for 3D point cloud models in terms of both computation and memory bandwidth. Despite this, research on reducing this cost is relatively sparse. This paper identifies inefficiencies of unique operations in the 3D point cloud training pipeline: farthest point sampling (FPS) and forward and backward aggregation passes. To address the inefficiencies, we propose novel training optimizations that reduce redundant computation and memory accesses resulting from the operations. Firstly, we introduce Lightweight FPS (L-FPS), which employs progressive near point filtering to eliminate the redundant distance calculations inherent in the original farthest point sampling. Secondly, we introduce the fused aggregation technique, which utilizes kernel fusion to reduce redundant memory accesses during the forward and backward aggregation passes. We apply these techniques to state-of-the-art PointNet-based models and evaluate their performance on NVIDIA RTX 3090 GPU. Our experimental results demonstrate 2.25x training time reduction on average with no accuracy drop.
Large language models are built on top of a transformer-based architecture to process textual inputs. For example, the LLaMA family of models stand out among many open-source implementations. Can the same transformer be used to process 2D images? In this paper, we answer this question by unveiling a LLaMA-like vision transformer in plain and pyramid forms, termed VisionLLaMA, which is tailored for this purpose. We extensively evaluate its effectiveness using typical pre-training paradigms in a good portion of downstream tasks of image perception and especially image generation. In many cases, VisionLLaMA has exhibited substantial gains over the previous state-of-the-art vision transformers. We believe that VisionLLaMA can serve as a strong new baseline model for vision understanding and generation.
Vision Transformer (ViT) has achieved excellent performance and demonstrated its promising potential in various computer vision tasks. The wide deployment of ViT in real-world tasks requires a thorough understanding of the societal impact of the model. However, most ViT-based works do not take fairness into account and it is unclear whether directly applying CNN-oriented debiased algorithm to ViT is feasible. Moreover, previous works typically sacrifice accuracy for fairness. Therefore, we aim to develop an algorithm that improves accuracy without sacrificing fairness. In this paper, we propose FairViT, a novel fair ViT framework. To this end, we introduce a novel distance loss and deploy adaptive fairness-aware masks on attention layers updating with model parameters. Experimental results show FairViT can achieve accuracy better than other alternatives, even with competitive computational efficiency. Furthermore, FairViT achieves appreciable fairness results.
Transformer-based architectures have become the de-facto standard models for diverse vision tasks owing to their superior performance. As the size of the models continues to scale up, model distillation becomes extremely important in various real applications, particularly on devices limited by computational resources. However, prevailing knowledge distillation methods exhibit diminished efficacy when confronted with a large capacity gap between the teacher and the student, e.g, 10x compression rate. In this paper, we present a novel approach named Automatic Multi-step Distillation (AMD) for large-scale vision model compression. In particular, our distillation process unfolds across multiple steps. Initially, the teacher undergoes distillation to form an intermediate teacher-assistant model, which is subsequently distilled further to the student. An efficient and effective optimization framework is introduced to automatically identify the optimal teacher-assistant that leads to the maximal student performance. We conduct extensive experiments on multiple image classification datasets, including CIFAR-10, CIFAR-100, and ImageNet. The findings consistently reveal that our approach outperforms several established baselines, paving a path for future knowledge distillation methods on large-scale vision models.
We propose Gaussian Frosting, a novel mesh-based representation for high-quality rendering and editing of complex 3D effects in real-time. Our approach builds on the recent 3D Gaussian Splatting framework, which optimizes a set of 3D Gaussians to approximate a radiance field from images. We propose first extracting a base mesh from Gaussians during optimization, then building and refining an adaptive layer of Gaussians with a variable thickness around the mesh to better capture the fine details and volumetric effects near the surface, such as hair or grass. We call this layer Gaussian Frosting, as it resembles a coating of frosting on a cake. The fuzzier the material, the thicker the frosting. We also introduce a parameterization of the Gaussians to enforce them to stay inside the frosting layer and automatically adjust their parameters when deforming, rescaling, editing or animating the mesh. Our representation allows for efficient rendering using Gaussian splatting, as well as editing and animation by modifying the base mesh. We demonstrate the effectiveness of our method on various synthetic and real scenes, and show that it outperforms existing surface-based approaches. We will release our code and a web-based viewer as additional contributions.
The human visual system is well-tuned to detect faces of all shapes and sizes. While this brings obvious survival advantages, such as a better chance of spotting unknown predators in the bush, it also leads to spurious face detections. Face pareidolia'' describes the perception of face-like structure among otherwise random stimuli: seeing faces in coffee stains or clouds in the sky. In this paper, we study face pareidolia from a computer vision perspective. We present an image dataset ofFaces in Things'', consisting of five thousand images from the web with human-annotated pareidolic faces. Using this dataset, we examine the extent to which a state-of-the-art human face detector exhibits pareidolia, and find a significant behavioral gap between humans and machines. We explore a variety of different strategies to close this gap and discover that the evolutionary need for humans to detect animal faces, as well as human faces, explains some of this gap. Finally, we propose a simple statistical model of pareidolia in images. Through studies on human subjects and our pareidolic face detectors we confirm a key prediction of our model regarding what image conditions are most likely to induce pareidolia.
Real-world image super-resolution deals with complex and unknown degradations, making it challenging to produce plausible results in a single step. In this work, we propose a transformer model with an iterative generation process that iteratively refines the results based on predicted confidences. It allows the model to focus on regions with low confidences and generate more confident and accurate results. Specifically, our model learns to predict the visual tokens of the high-resolution image and their corresponding confidence scores, conditioned on the low-resolution image. By keeping only the most confident tokens at each iteration and re-predicting the other tokens in the next iteration, our model generates all high-resolution tokens within a few steps. To ensure consistency with the low-resolution input image, we further propose a conditional controlling module that utilizes the low-resolution image to control the decoding process from high-resolution tokens to image pixels. Experiments demonstrate that our model achieves state-of-the-art performance on real-world datasets while requiring fewer iteration steps compared to recent diffusion models.
Compressed sensing (CS) has emerged to overcome the inefficiency of Nyquist sampling. However, traditional optimization-based reconstruction is slow and can not yield an exact image in practice. Deep learning-based reconstruction has been a promising alternative to optimization-based reconstruction, outperforming it in accuracy and computation speed. Finding an efficient sampling method with deep learning-based reconstruction, especially for Fourier CS remains a challenge. Existing joint optimization of sampling-reconstruction works (H1) optimize the sampling mask but have low potential as it is not adaptive to each data point. Adaptive sampling (H2) has also disadvantages of difficult optimization and Pareto sub-optimality. Here, we propose a novel adaptive selection of sampling-reconstruction (H1.5) framework that selects the best sampling mask and reconstruction network for each input data. We provide theorems that our method has a higher potential than H1 and effectively solves the Pareto sub-optimality problem in sampling-reconstruction by using separate reconstruction networks for different sampling masks. To select the best sampling mask, we propose to quantify the high-frequency Bayesian uncertainty of the input, using a super-resolution space generation model. Our method outperforms joint optimization of sampling-reconstruction (H1) and adaptive sampling (H2) by achieving significant improvements on several Fourier CS problems.
Recent advances in document forgery techniques produce malicious yet nearly visually untraceable alterations, imposing a big challenge for document tampering localization (DTL). Despite significant recent progress, there has been surprisingly limited exploration of adversarial robustness in DTL. This paper presents the first effort to uncover the vulnerability of most existing DTL models to adversarial attacks, highlighting the need for greater attention within the DTL community. In pursuit of robust DTL, we demonstrate that adversarial training can promote the model's robustness and effectively protect against adversarial attacks. As a notable advancement, we further introduce a latent manifold adversarial training approach that enhances adversarial robustness in DTL by incorporating perturbations on the latent manifold of adversarial examples, rather than exclusively relying on label-guided information. Extensive experiments on DTL benchmark datasets shows the necessity of adversarial training and our proposed manifold-based method significantly improves the adversarial robustness on both white-box and black-box attacks.
In this paper, we propose a new knowledge transfer paradigm called Knowledge in One Prompt (KiOP). This paradigm encapsulates knowledge from various models into a solitary prompt without altering the original models or requiring access to the training data, which enables us to achieve efficient and convenient knowledge transfer in more realistic scenarios. From a practicality standpoint, this paradigm not only for the first time proves the effectiveness of Visual Prompt in data inaccessible contexts, but also solves the problems of low model reusability and high storage resource consumption faced by traditional Data-Free Knowledge Transfer, which means that we can realize the parallel knowledge transfer of multiple models without modifying any source model. Extensive experiments across various datasets and models demonstrate the efficacy of the proposed KiOP knowledge transfer paradigm. Without access to real training data and with rigorous storage capacity constraints, it is also capable of yielding considerable outcomes when dealing with cross-model backbone setups and handling parallel knowledge transfer processing requests with multiple (more than 2) models.
Towards Dual Transparent Liquid Level Estimation in Biomedical Lab: Dataset, Methods and Practice
PDF ↗“Dual Transparent Liquid” refers to a liquid and its container, both being transparent. Accurately estimating the levels of such a liquid from arbitrary viewpoints is fundamental and crucial, especially in AI-guided autonomous biomedical laboratories for tasks like liquid dispensing, aspiration, and mixing. However, current methods for estimating liquid level focus on scenarios of a single instance captured from a fixed view. We propose a new paradigm for dual transparent liquid level estimation, including a dataset, methods, and practices. The dual transparent liquid dataset, named DTLD, comprises 27,458 images with four object instances captured from multiple views across three biomedical lab scenes. Based on DTLD, we propose an end-to-end learning method for detecting the liquid contact line, followed by an approach to estimate the liquid level. To enhance contact line detection, a color rectification module is proposed to stabilize the color distribution at the local region of the air-liquid interface. Additionally, our method surpasses the current best approach, reducing the mean absolute percentage error by a percentage of 43.4. The dataset and code will be available at https://github.com/dualtransparency/TCLD.
Pre-trained models are widely used in machine learning (ML) due to the minimal demand for computational resources and training data. Recent studies show that the pre-trained model are vulnerable to backdoor attacks. Additionally, prior studies on hardware security have indicated that ML systems could potentially be compromised through bit flip attacks using Rowhammer. In this paper, we introduce \textbf{WBP} (i.e., weight bit poisoning), a novel attack framework that allows an attacker to implant a task-agnostic backdoor into the victim model \emph{during the fine-tuning process} through limited \emph{weight bit flips}. Notably, WBP aims to directly maximize the distance of output representations for normal and triggered inputs. We evaluate WBP on state-of-the-art CNNs and Vision Transformer models with a variety of downstream tasks. Our experimental results demonstrate that, without any prior knowledge of fine-tuning datasets, WBP can compromise a wide range of downstream tasks with a 99.3% attack success rate on average by flipping as few as 11 bits among millions of parameters.
Establishing reliable correspondences is essential for registration tasks such as 3D and 2D3D registration. Existing methods commonly leverage geometric or semantic point features to generate potential correspondences. However, these features may face challenges such as large deformation, scale inconsistency, and ambiguous matching problems (e.g., symmetry). Additionally, many previous methods, which rely on single-pass prediction, may struggle with local minima in complex scenarios. To mitigate these challenges, we introduce a diffusion matching model for robust correspondence construction. Our approach treats correspondence estimation as a denoising diffusion process within the doubly stochastic matrix space, which gradually denoises (refines) a doubly stochastic matching matrix to the ground-truth one for high-quality correspondence estimation. It involves a forward diffusion process that gradually introduces Gaussian noise into the ground truth matching matrix and a reverse denoising process that iteratively refines the noisy matching matrix. In particular, the feature extraction from the backbone occurs only once during the inference phase. Our lightweight denoising module utilizes the same feature at each reverse sampling step. Evaluation of our method on both 3D and 2D3D registration tasks confirms its effectiveness. The code will be made available online.