pwc-archive/papers-with-abstracts huggingface dataset:
Chat-Edit-3D: Interactive 3D Scene Editing via Text Prompts
Authors: Shuangkang Fang, Yufeng Wang, Yi-Hsuan Tsai, Yi Yang, Wenrui Ding, Shuchang Zhou, Ming-Hsuan Yang
Date: 2024-07-09 00:00:00
Tasks: 3D Object Editing, 3D Reconstruction, 3D scene Editing, 3D Shape Reconstruction From A Single 2D Image, Language Modelling, Large Language Model

Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing. However, existing schemes for 3D scene editing still exhibit certain shortcomings, hindering their further interactive design. Such schemes typically adhere to fixed input patterns, limiting users' flexibility in text input. Moreover, their editing capabilities are constrained by a single or a few 2D visual models and require intricate pipeline design to integrate these models into 3D reconstruction processes. To address the aforementioned issues, we propose a dialogue-based 3D scene editing approach, termed CE3D, which is centered around a large language model that allows for arbitrary textual input from users and interprets their intentions, subsequently facilitating the autonomous invocation of the corresponding visual expert models. Furthermore, we design a scheme utilizing Hash-Atlas to represent 3D scene views, which transfers the editing of 3D scenes onto 2D atlas images. This design achieves complete decoupling between the 2D editing and 3D reconstruction processes, enabling CE3D to flexibly integrate a wide range of existing 2D or 3D visual models without necessitating intricate fusion designs. Experimental results demonstrate that CE3D effectively integrates multiple visual models to achieve diverse editing visual effects, possessing strong scene comprehension and multi-round dialog capabilities. The code is available at https://sk-fun.fun/CE3D.
HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space Model
Authors: Dunbin Shen, Xuanbing Zhu, Jiacheng Tian, Jianjun Liu, Zhenrong Du, Hongyu Wang, Xiaorui Ma
Date: 2024-07-09 00:00:00
Tasks: Contrastive Learning, Earth Observation, Mamba

Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation. However, HTD faces challenges due to limited prior knowledge and spectral variation, leading to underfitting models and unreliable performance. To address these challenges, this paper proposes an efficient self-supervised HTD method with a pyramid state space model (SSM), named HTD-Mamba, which employs spectrally contrastive learning to distinguish between target and background based on the similarity measurement of intrinsic features. Specifically, to obtain sufficient training samples and leverage spatial contextual information, we propose a spatial-encoded spectral augmentation technique that encodes all surrounding pixels within a patch into a transformed view of the center pixel. Additionally, to explore global band correlations, we divide pixels into continuous group-wise spectral embeddings and introduce Mamba to HTD for the first time to model long-range dependencies of the spectral sequence with linear complexity. Furthermore, to alleviate spectral variation and enhance robust representation, we propose a pyramid SSM as a backbone to capture and fuse multiresolution spectral-wise intrinsic features. Extensive experiments conducted on four public datasets demonstrate that the proposed method outperforms state-of-the-art methods in both quantitative and qualitative evaluations. Code is available at \url{https://github.com/shendb2022/HTD-Mamba}.
Event Trojan: Asynchronous Event-based Backdoor Attacks
Authors: Ruofei Wang, Qing Guo, Haoliang Li, Renjie Wan
Date: 2024-07-09 00:00:00
Tasks: Event data classification

As asynchronous event data is more frequently engaged in various vision tasks, the risk of backdoor attacks becomes more evident. However, research into the potential risk associated with backdoor attacks in asynchronous event data has been scarce, leaving related tasks vulnerable to potential threats. This paper has uncovered the possibility of directly poisoning event data streams by proposing Event Trojan framework, including two kinds of triggers, i.e., immutable and mutable triggers. Specifically, our two types of event triggers are based on a sequence of simulated event spikes, which can be easily incorporated into any event stream to initiate backdoor attacks. Additionally, for the mutable trigger, we design an adaptive learning mechanism to maximize its aggressiveness. To improve the stealthiness, we introduce a novel loss function that constrains the generated contents of mutable triggers, minimizing the difference between triggers and original events while maintaining effectiveness. Extensive experiments on public event datasets show the effectiveness of the proposed backdoor triggers. We hope that this paper can draw greater attention to the potential threats posed by backdoor attacks on event-based tasks. Our code is available at https://github.com/rfww/EventTrojan.
AstroSpy: On detecting Fake Images in Astronomy via Joint Image-Spectral Representations
Authors: Mohammed Talha Alam, Raza Imam, Mohsen Guizani, Fakhri Karray
Date: 2024-07-09 00:00:00
Tasks: Astronomy, Misinformation

The prevalence of AI-generated imagery has raised concerns about the authenticity of astronomical images, especially with advanced text-to-image models like Stable Diffusion producing highly realistic synthetic samples. Existing detection methods, primarily based on convolutional neural networks (CNNs) or spectral analysis, have limitations when used independently. We present AstroSpy, a hybrid model that integrates both spectral and image features to distinguish real from synthetic astronomical images. Trained on a unique dataset of real NASA images and AI-generated fakes (approximately 18k samples), AstroSpy utilizes a dual-pathway architecture to fuse spatial and spectral information. This approach enables AstroSpy to achieve superior performance in identifying authentic astronomical images. Extensive evaluations demonstrate AstroSpy's effectiveness and robustness, significantly outperforming baseline models in both in-domain and cross-domain tasks, highlighting its potential to combat misinformation in astronomy.
CycleSAM: One-Shot Surgical Scene Segmentation using Cycle-Consistent Feature Matching to Prompt SAM
Authors: Aditya Murali, Pietro Mascagni, Didier Mutter, Nicolas Padoy
Date: 2024-07-09 00:00:00
Tasks: One-Shot Segmentation, Scene Segmentation, Segmentation, Semantic Segmentation

The recently introduced Segment-Anything Model (SAM) has the potential to greatly accelerate the development of segmentation models. However, directly applying SAM to surgical images has key limitations including (1) the requirement of image-specific prompts at test-time, thereby preventing fully automated segmentation, and (2) ineffectiveness due to substantial domain gap between natural and surgical images. In this work, we propose CycleSAM, an approach for one-shot surgical scene segmentation that uses the training image-mask pair at test-time to automatically identify points in the test images that correspond to each object class, which can then be used to prompt SAM to produce object masks. To produce high-fidelity matches, we introduce a novel spatial cycle-consistency constraint that enforces point proposals in the test image to rematch to points within the object foreground region in the training image. Then, to address the domain gap, rather than directly using the visual features from SAM, we employ a ResNet50 encoder pretrained on surgical images in a self-supervised fashion, thereby maintaining high label-efficiency. We evaluate CycleSAM for one-shot segmentation on two diverse surgical semantic segmentation datasets, comprehensively outperforming baseline approaches and reaching up to 50% of fully-supervised performance.
ERQ: Error Reduction for Post-Training Quantization of Vision Transformers
Authors: Yunshan Zhong, Jiawei Hu, You Huang, Yuxin Zhang, Rongrong Ji
Date: 2024-07-09 00:00:00
Tasks: Quantization, regression

Post-training quantization (PTQ) for vision transformers (ViTs) has garnered significant attention due to its efficiency in compressing models. However, existing methods typically overlook the intricate interdependence between quantized weight and activation, leading to considerable quantization error. In this paper, we propose ERQ, a two-step PTQ approach meticulously crafted to sequentially reduce the quantization error arising from activation and weight quantization. ERQ first introduces Activation quantization error reduction (Aqer) that strategically formulates the minimization of activation quantization error as a Ridge Regression problem, tackling it by updating weights with full-precision. Subsequently, ERQ introduces Weight quantization error reduction (Wqer) that adopts an iterative approach to mitigate the quantization error induced by weight quantization. In each iteration, an empirically derived, efficient proxy is employed to refine the rounding directions of quantized weights, coupled with a Ridge Regression solver to curtail weight quantization error. Experimental results attest to the effectiveness of our approach. Notably, ERQ surpasses the state-of-the-art GPTQ by 22.36% in accuracy for W3A4 ViT-S.
LVLM-empowered Multi-modal Representation Learning for Visual Place Recognition
Authors: Teng Wang, Lingquan Meng, Lei Cheng, Changyin Sun
Date: 2024-07-09 00:00:00
Tasks: Instruction Following, Representation Learning, Scene Understanding, visual instruction following, Visual Place Recognition

Visual place recognition (VPR) remains challenging due to significant viewpoint changes and appearance variations. Mainstream works tackle these challenges by developing various feature aggregation methods to transform deep features into robust and compact global representations. Unfortunately, satisfactory results cannot be achieved under challenging conditions. We start from a new perspective and attempt to build a discriminative global representations by fusing image data and text descriptions of the the visual scene. The motivation is twofold: (1) Current Large Vision-Language Models (LVLMs) demonstrate extraordinary emergent capability in visual instruction following, and thus provide an efficient and flexible manner in generating text descriptions of images; (2) The text descriptions, which provide high-level scene understanding, show strong robustness against environment variations. Although promising, leveraging LVLMs to build multi-modal VPR solutions remains challenging in efficient multi-modal fusion. Furthermore, LVLMs will inevitably produces some inaccurate descriptions, making it even harder. To tackle these challenges, we propose a novel multi-modal VPR solution. It first adapts pre-trained visual and language foundation models to VPR for extracting image and text features, which are then fed into the feature combiner to enhance each other. As the main component, the feature combiner first propose a token-wise attention block to adaptively recalibrate text tokens according to their relevance to the image data, and then develop an efficient cross-attention fusion module to propagate information across different modalities. The enhanced multi-modal features are compressed into the feature descriptor for performing retrieval. Experimental results show that our method outperforms state-of-the-art methods by a large margin with significantly smaller image descriptor dimension.
Towards Physics-informed Cyclic Adversarial Multi-PSF Lensless Imaging
Authors: Abeer Banerjee, Sanjay Singh
Date: 2024-07-09 00:00:00
Tasks: Image Reconstruction, Specificity

Lensless imaging has emerged as a promising field within inverse imaging, offering compact, cost-effective solutions with the potential to revolutionize the computational camera market. By circumventing traditional optical components like lenses and mirrors, novel approaches like mask-based lensless imaging eliminate the need for conventional hardware. However, advancements in lensless image reconstruction, particularly those leveraging Generative Adversarial Networks (GANs), are hindered by the reliance on data-driven training processes, resulting in network specificity to the Point Spread Function (PSF) of the imaging system. This necessitates a complete retraining for minor PSF changes, limiting adaptability and generalizability across diverse imaging scenarios. In this paper, we introduce a novel approach to multi-PSF lensless imaging, employing a dual discriminator cyclic adversarial framework. We propose a unique generator architecture with a sparse convolutional PSF-aware auxiliary branch, coupled with a forward model integrated into the training loop to facilitate physics-informed learning to handle the substantial domain gap between lensless and lensed images. Comprehensive performance evaluation and ablation studies underscore the effectiveness of our model, offering robust and adaptable lensless image reconstruction capabilities. Our method achieves comparable performance to existing PSF-agnostic generative methods for single PSF cases and demonstrates resilience to PSF changes without the need for retraining.
Improving the Transferability of Adversarial Examples by Feature Augmentation
Authors: Donghua Wang, Wen Yao, Tingsong Jiang, Xiaohu Zheng, Junqi Wu, Xiaoqian Chen
Date: 2024-07-09 00:00:00
Tasks: Diversity

Despite the success of input transformation-based attacks on boosting adversarial transferability, the performance is unsatisfying due to the ignorance of the discrepancy across models. In this paper, we propose a simple but effective feature augmentation attack (FAUG) method, which improves adversarial transferability without introducing extra computation costs. Specifically, we inject the random noise into the intermediate features of the model to enlarge the diversity of the attack gradient, thereby mitigating the risk of overfitting to the specific model and notably amplifying adversarial transferability. Moreover, our method can be combined with existing gradient attacks to augment their performance further. Extensive experiments conducted on the ImageNet dataset across CNN and transformer models corroborate the efficacy of our method, e.g., we achieve improvement of +26.22% and +5.57% on input transformation-based attacks and combination methods, respectively.
Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification
Authors: Zitai Wang, Qianqian Xu, Zhiyong Yang, Peisong Wen, Yuan He, Xiaochun Cao, Qingming Huang
Date: 2024-07-09 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label ranking, which returns multiple top-ranked labels for each instance, has a wide range of applications for visual tasks. Due to its complicated setting, prior arts have proposed various measures to evaluate model performances. However, both theoretical analysis and empirical observations show that a model might perform inconsistently on different measures. To bridge this gap, this paper proposes a novel measure named Top-K Pairwise Ranking (TKPR), and a series of analyses show that TKPR is compatible with existing ranking-based measures. In light of this, we further establish an empirical surrogate risk minimization framework for TKPR. On one hand, the proposed framework enjoys convex surrogate losses with the theoretical support of Fisher consistency. On the other hand, we establish a sharp generalization bound for the proposed framework based on a novel technique named data-dependent contraction. Finally, empirical results on benchmark datasets validate the effectiveness of the proposed framework.
Self-supervised visual learning from interactions with objects
Authors: Arthur Aubret, Céline Teulière, Jochen Triesch
Date: 2024-07-09 00:00:00
Tasks: Object, Representation Learning, Self-Supervised Learning

Self-supervised learning (SSL) has revolutionized visual representation learning, but has not achieved the robustness of human vision. A reason for this could be that SSL does not leverage all the data available to humans during learning. When learning about an object, humans often purposefully turn or move around objects and research suggests that these interactions can substantially enhance their learning. Here we explore whether such object-related actions can boost SSL. For this, we extract the actions performed to change from one ego-centric view of an object to another in four video datasets. We then introduce a new loss function to learn visual and action embeddings by aligning the performed action with the representations of two images extracted from the same clip. This permits the performed actions to structure the latent visual representation. Our experiments show that our method consistently outperforms previous methods on downstream category recognition. In our analysis, we find that the observed improvement is associated with a better viewpoint-wise alignment of different objects from the same category. Overall, our work demonstrates that embodied interactions with objects can improve SSL of object categories.
PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision
Authors: Chengjie Wang, Chengming Xu, Zhenye Gan, Jianlong Hu, Wenbing Zhu, Lizhuag Ma
Date: 2024-07-09 00:00:00
Tasks: Anomaly Detection, Binary Classification

Positive and Unlabeled (PU) learning, a binary classification model trained with only positive and unlabeled data, generally suffers from overfitted risk estimation due to inconsistent data distributions. To address this, we introduce a pseudo-supervised PU learning framework (PSPU), in which we train the PU model first, use it to gather confident samples for the pseudo supervision, and then apply these supervision to correct the PU model's weights by leveraging non-PU objectives. We also incorporate an additional consistency loss to mitigate noisy sample effects. Our PSPU outperforms recent PU learning methods significantly on MNIST, CIFAR-10, CIFAR-100 in both balanced and imbalanced settings, and enjoys competitive performance on MVTecAD for industrial anomaly detection.
Universal Multi-view Black-box Attack against Object Detectors via Layout Optimization
Authors: Donghua Wang, Wen Yao, Tingsong Jiang, Chao Li, Xiaoqian Chen
Date: 2024-07-09 00:00:00
Tasks: Object

Object detectors have demonstrated vulnerability to adversarial examples crafted by small perturbations that can deceive the object detector. Existing adversarial attacks mainly focus on white-box attacks and are merely valid at a specific viewpoint, while the universal multi-view black-box attack is less explored, limiting their generalization in practice. In this paper, we propose a novel universal multi-view black-box attack against object detectors, which optimizes a universal adversarial UV texture constructed by multiple image stickers for a 3D object via the designed layout optimization algorithm. Specifically, we treat the placement of image stickers on the UV texture as a circle-based layout optimization problem, whose objective is to find the optimal circle layout filled with image stickers so that it can deceive the object detector under the multi-view scenario. To ensure reasonable placement of image stickers, two constraints are elaborately devised. To optimize the layout, we adopt the random search algorithm enhanced by the devised important-aware selection strategy to find the most appropriate image sticker for each circle from the image sticker pools. Extensive experiments conducted on four common object detectors suggested that the detection performance decreases by a large magnitude of 74.29% on average in multi-view scenarios. Additionally, a novel evaluation tool based on the photo-realistic simulator is designed to assess the texture-based attack fairly.
MRI Volume-Based Robust Brain Age Estimation Using Weight-Shared Spatial Attention in 3D CNNs
Authors: Vamshi Krishna Kancharla, Neelam Sinha
Date: 2024-07-09 00:00:00
Tasks: Age Estimation

Important applications of advancements in machine learning, are in the area of healthcare, more so for neurological disorder detection. A crucial step towards understanding the neurological status, is to estimate the brain age using structural MRI volumes, in order to measure its deviation from chronological age. Factors that contribute to brain age are best captured using a data-driven approach, such as deep learning. However, it places a huge demand on the availability of diverse datasets. In this work, we propose a robust brain age estimation paradigm that utilizes a 3D CNN model, by-passing the need for model-retraining across datasets. The proposed model consists of seven 3D CNN layers, with a shared spatial attention layer incorporated at each CNN layer followed by five dense layers. The novelty of the proposed method lies in the idea of spatial attention module, with shared weights across the CNN layers. This weight sharing ensures directed attention to specific brain regions, for localizing age-related features within the data, lending robustness. The proposed model, trained on ADNI dataset comprising 516 T1 weighted MRI volumes of healthy subjects, resulted in Mean Absolute Error (MAE) of 1.662 years, which is an improvement of 1.688 years over the state-of-the-art (SOTA) model, based on disjoint test samples from the same repository. To illustrate generalizability, the same pipeline was utilized on volumes from a publicly available source called OASIS3. From OASIS3, MRI volumes 890 healthy subjects were utilized resulting in MAE of 2.265 years. Due to diversity in acquisitions across multiple sites, races and genetic factors, traditional CNN models are not guaranteed to prioritize brain regions crucial for age estimation. In contrast, the proposed weight-shared spatial attention module, directs attention on specific regions, required for the estimation.
Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention
Authors: Xunjiang Gu, Guanyu Song, Igor Gilitschenski, Marco Pavone, Boris Ivanovic
Date: 2024-07-09 00:00:00
Tasks: Autonomous Driving, Decoder, Trajectory Forecasting, Trajectory Prediction

Understanding road geometry is a critical component of the autonomous vehicle (AV) stack. While high-definition (HD) maps can readily provide such information, they suffer from high labeling and maintenance costs. Accordingly, many recent works have proposed methods for estimating HD maps online from sensor data. The vast majority of recent approaches encode multi-camera observations into an intermediate representation, e.g., a bird's eye view (BEV) grid, and produce vector map elements via a decoder. While this architecture is performant, it decimates much of the information encoded in the intermediate representation, preventing downstream tasks (e.g., behavior prediction) from leveraging them. In this work, we propose exposing the rich internal features of online map estimation methods and show how they enable more tightly integrating online mapping with trajectory forecasting. In doing so, we find that directly accessing internal BEV features yields up to 73% faster inference speeds and up to 29% more accurate predictions on the real-world nuScenes dataset.