Targeted adversarial attack, which aims to mislead a model to recognize any image as a target object by imperceptible perturbations, has become a mainstream tool for vulnerability assessment of deep neural networks (DNNs). Since existing targeted attackers only learn to attack known target classes, they cannot generalize well to unknown classes.To tackle this issue, we propose Generalized Adversarial attacKER (GAKer), which is able to construct adversarial examples to any target class. The core idea behind GAKer is to craft a latently infected representation during adversarial example generation. To this end, the extracted latent representations of the target object are first injected into intermediate features of an input image in an adversarial generator. Then, the generator is optimized to ensure visual consistency with the input image while being close to the target object in the feature space. Since the GAKer is class-agnostic yet model-agnostic, it can be regarded as a general tool that not only reveals the vulnerability of more DNNs but also identifies deficiencies of DNNs in a wider range of classes. Extensive experiments have demonstrated the effectiveness of our proposed method in generating adversarial examples for both known and unknown classes. Notably, compared with other generative methods, our method achieves an approximate 14.13% higher attack success rate for unknown classes and an approximate 4.23% higher success rate for known classes.
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UAVs emerge as the optimal carriers for visual weed identification and integrated pest and disease management in crops. However, the absence of specialized datasets impedes the advancement of model development in this domain. To address this, we have developed the Pests and Diseases Tree dataset (PDT dataset). PDT dataset represents the first high-precision UAV-based dataset for targeted detection of tree pests and diseases, which is collected in real-world operational environments and aims to fill the gap in available datasets for this field. Moreover, by aggregating public datasets and network data, we further introduce the Common Weed and Crop dataset (CWC dataset) to address the challenge of inadequate classification capabilities of test models within datasets for this field. Finally, we propose the YOLO-Dense Pest (DP) model for high-precision object detection of weed, pest, and disease crop images. We re-evaluate the state-of-the-art detection methods with our proposed PDT dataset and CWC dataset, showing the completeness of the dataset and the effectiveness of the YOLO-DP. The proposed PDT dataset, CWC dataset, and YOLO-DP method are presented at https://github.com/eccv-Anonymity/PDTCWCYOLO-DP. (Now it's an anonymous URL for review, and the datasets will be republished on the project home page upon acceptance.)
Text-driven video editing has emerged as a prominent application based on the breakthroughs of image diffusion models. Existing state-of-the-art methods focus on zero-shot frameworks due to limited training data and to limit computational expense. To preserve structure consistency, previous frameworks usually employ Denoising Diffusion Implicit Model (DDIM) inversion to provide inverted noise latents as guidance. The key challenge lies in limiting errors caused by the randomness and inaccuracy in each step of the in the naive DDIM inversion process, which can lead to temporal inconsistency in video editing tasks. Our observation indicates that incorporating temporal keyframe information can alleviate the accumulated error during inversion. In this paper, we propose an effective warping strategy in the feature domain to obtain high-quality DDIM inverted noise latents. Specifically, we shuffle the editing frames randomly in each timestep and use optical flow extracted from the source video to propagate the latent features of the first keyframe to subsequent keyframes. Moreover, we develop a comprehensive zero-shot framework that adapts to this strategy in both the inversion and denoising processes, thereby facilitating the generation of consistent edited videos.
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain. Incorporating the Transformers with SNNs has shown promise for accuracy, yet it is incompetent to capture high-frequency patterns like moving edge and pixel-level brightness changes due to their reliance on global self-attention operations. Porting frequency representations in SNN is challenging yet crucial for event-driven vision. To address this issue, we propose the Spiking Wavelet Transformer (SWformer), an attention-free architecture that effectively learns comprehensive spatial-frequency features in a spike-driven manner by leveraging the sparse wavelet transform. The critical component is a Frequency-aware Spiking Token Mixer (FSTM) with three branches: 1) spiking wavelet learner for spatial-frequency domain learning, 2) convolution-based learner for spatial feature extraction, and 3) spiking pointwise convolution for cross-channel information aggregation. We also adopt negative spike dynamics to strengthen the frequency representation further. This enables the SWformer to outperform vanilla spiking Transformers in capturing high-frequency visual components, as evidenced by our empirical results. Experiments on both static and neuromorphic datasets demonstrate SWformer's effectiveness in capturing spatial-frequency patterns in a multiplication-free, event-driven fashion, outperforming state-of-the-art SNNs. SWformer achieves an over 50 reduction in energy consumption, a 21.1 reduction in parameter count, and a 2.40 performance improvement on the ImageNet dataset compared to vanilla Spiking Transformers.
WTS: A Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding
PDF ↗In this paper, we address the challenge of fine-grained video event understanding in traffic scenarios, vital for autonomous driving and safety. Traditional datasets focus on driver or vehicle behavior, often neglecting pedestrian perspectives. To fill this gap, we introduce the WTS dataset, highlighting detailed behaviors of both vehicles and pedestrians across over 1.2k video events in over hundreds traffic scenarios. WTS integrates diverse perspectives from vehicle ego and fixed overhead cameras in a vehicle-infrastructure cooperative environment, enriched with comprehensive textual descriptions and unique 3D Gaze data for a synchronized 2D/3D view, focusing on pedestrian analysis. We also provide annotations for 5k publicly sourced pedestrian-related traffic videos. Additionally, we introduce LLMScorer, an LLM-based evaluation metric to align inference captions with ground truth. Using WTS, we establish a benchmark for dense video-to-text tasks, exploring state-of-the-art Vision-Language Models with an instance-aware VideoLLM method as a baseline. WTS aims to advance fine-grained video event understanding, enhancing traffic safety and autonomous driving development. Dataset page: https://woven-visionai.github.io/wts-dataset-homepage/.
Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation
PDF ↗Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliablility of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid erroneous pseudo-labels. However, high confidence does not guarantee correct pseudo-labels especially in the initial training iterations. In this paper, we propose a novel approach to reliably learn from pseudo-labels. First, we unify the predictions from a trained object detector and a semantic segmentation model to identify reliable pseudo-label pixels. Second, we assign different learning weights to pseudo-labeled pixels to avoid noisy training signals. To determine these weights, we first use the reliable pseudo-label pixels identified from the first step and labeled pixels to construct a prototype for each class. Then, the per-pixel weight is the structural similarity between the pixel and the prototype measured via rank-statistics similarity. This metric is robust to noise, making it better suited for comparing features from unlabeled images, particularly in the initial training phases where wrong pseudo labels are prone to occur. We show that our method can be easily integrated into four semi-supervised semantic segmentation frameworks, and improves them in both Cityscapes and Pascal VOC datasets.
FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation models
PDF ↗Estimating the 6D pose of objects unseen during training is highly desirable yet challenging. Zero-shot object 6D pose estimation methods address this challenge by leveraging additional task-specific supervision provided by large-scale, photo-realistic synthetic datasets. However, their performance heavily depends on the quality and diversity of rendered data and they require extensive training. In this work, we show how to tackle the same task but without training on specific data. We propose FreeZe, a novel solution that harnesses the capabilities of pre-trained geometric and vision foundation models. FreeZe leverages 3D geometric descriptors learned from unrelated 3D point clouds and 2D visual features learned from web-scale 2D images to generate discriminative 3D point-level descriptors. We then estimate the 6D pose of unseen objects by 3D registration based on RANSAC. We also introduce a novel algorithm to solve ambiguous cases due to geometrically symmetric objects that is based on visual features. We comprehensively evaluate FreeZe across the seven core datasets of the BOP Benchmark, which include over a hundred 3D objects and 20,000 images captured in various scenarios. FreeZe consistently outperforms all state-of-the-art approaches, including competitors extensively trained on synthetic 6D pose estimation data. We will release the source code publicly.
Solving the inverse problem of microscopy deconvolution with a residual Beylkin-Coifman-Rokhlin neural network
PDF ↗Optic deconvolution in light microscopy (LM) refers to recovering the object details from images, revealing the ground truth of samples. Traditional explicit methods in LM rely on the point spread function (PSF) during image acquisition. Yet, these approaches often fall short due to inaccurate PSF models and noise artifacts, hampering the overall restoration quality. In this paper, we approached the optic deconvolution as an inverse problem. Motivated by the nonstandard-form compression scheme introduced by Beylkin, Coifman, and Rokhlin (BCR), we proposed an innovative physics-informed neural network Multi-Stage Residual-BCR Net (m-rBCR) to approximate the optic deconvolution. We validated the m-rBCR model on four microscopy datasets - two simulated microscopy datasets from ImageNet and BioSR, real dSTORM microscopy images, and real widefield microscopy images. In contrast to the explicit deconvolution methods (e.g. Richardson-Lucy) and other state-of-the-art NN models (U-Net, DDPM, CARE, DnCNN, ESRGAN, RCAN, Noise2Noise, MPRNet, and MIMO-U-Net), the m-rBCR model demonstrates superior performance to other candidates by PSNR and SSIM in two real microscopy datasets and the simulated BioSR dataset. In the simulated ImageNet dataset, m-rBCR ranks the second-best place (right after MIMO-U-Net). With the backbone from the optical physics, m-rBCR exploits the trainable parameters with better performances (from ~30 times fewer than the benchmark MIMO-U-Net to ~210 times than ESRGAN). This enables m-rBCR to achieve a shorter runtime (from ~3 times faster than MIMO-U-Net to ~300 times faster than DDPM). To summarize, by leveraging physics constraints our model reduced potentially redundant parameters significantly in expertise-oriented NN candidates and achieved high efficiency with superior performance.
Sparsification-based pruning has been an important category in model compression. Existing methods commonly set sparsity-inducing penalty terms to suppress the importance of dropped weights, which is regarded as suppressed sparsification paradigm. However, this paradigm inactivates the to-be-dropped part of networks causing capacity damage before pruning, thereby leading to performance degradation. To alleviate this issue, we first study and reveal the relative sparsity effect in emerging stimulative training and then propose an enhanced sparsification paradigm framework named STP for structured pruning, which maintains the magnitude of dropped weights and enhances the expressivity of kept weights by self-distillation. Besides, to obtain a relatively optimal architecture of the final network, we propose multi-dimension architecture space and a knowledge distillation guided exploration strategy. To reduce the huge capacity gap of distillation, we propose a subnet mutating expansion technique. Extensive experiments on various benchmarks indicate the effectiveness of STP. Specifically, without pre-training or fine-tuning, our method consistently achieves superior performance at different budgets, especially under extremely aggressive pruning scenarios, e.g., remaining 95.11% Top-1 accuracy (72.43% in 76.15%) while reducing 85% FLOPs for ResNet-50 on ImageNet. Codes will be released.
Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifier
PDF ↗Incorporating pixel contextual information is critical for accurate segmentation. In this paper, we show that an effective way to incorporate contextual information is through a patch-based classifier. This patch classifier is trained to identify classes present within an image region, which facilitates the elimination of distractors and enhances the classification of small object segments. Specifically, we introduce \textbf{Multi-scale Patch-based Multi-label Classifier} (MPMC), a novel plug-in module designed for existing semi-supervised segmentation (SSS) frameworks. MPMC offers patch-level supervision, enabling the discrimination of pixel regions of different classes within a patch. Furthermore, MPMC learns an adaptive pseudo-label weight, using patch-level classification to alleviate the impact of the teacher’s noisy pseudo-label supervision on the student. This lightweight module can be integrated into any SSS framework, significantly enhancing their performance. We demonstrate the efficacy of our proposed MPMC by integrating it into four SSS methodologies and improving them across two natural image and one medical segmentation dataset, notably improving the segmentation results of the baselines across all the three datasets.
While recent advances in 3D-aware Generative Adversarial Networks (GANs) have aided the development of near-frontal view human face synthesis, the challenge of comprehensively synthesizing a full 3D head viewable from all angles still persists. Although PanoHead proves the possibilities of using a large-scale dataset with images of both frontal and back views for full-head synthesis, it often causes artifacts for back views. Based on our in-depth analysis, we found the reasons are mainly twofold. First, from network architecture perspective, we found each plane in the utilized tri-plane/tri-grid representation space tends to confuse the features from both sides, causing mirroring'' artifacts (e.g., the glasses appear in the back). Second, from data supervision aspect, we found that existing discriminator training in 3D GANs only focuses on the quality of the rendered image itself, and does not care about its plausibility with the perspective from which it was rendered. This makes it possible to generateface'' in the non-frontal view, due to its easiness to fool the discriminator. In response, we propose SphereHead, a novel tri-plane representation in the spherical coordinate system that fits the human head's geometric characteristics and efficiently mitigates many of the generated artifacts. We further introduce a view-image consistency loss for the discriminator to emphasize the correspondence of the camera labels and the images. The combination of these efforts results in visually superior outcomes with significantly fewer artifacts. Our code and dataset is publicly available at https://lhyfst.github.io/spherehead/.
AdversariaLeak: External Information Leakage Attack Using Adversarial Samples on Face Recognition Systems
PDF ↗Face recognition (FR) systems are vulnerable to external information leakage (EIL) attacks, which can reveal sensitive information about the training data, thus compromising the confidentiality of the company's proprietary and the privacy of the individuals concerned. Existing EIL attacks mainly rely on unrealistic assumptions, such as a high query budget for the attacker and massive computational power, resulting in impractical EIL attacks. We present AdversariaLeak, a novel and practical query-based EIL attack that targets the face verification model of the FR systems by using carefully selected adversarial samples. AdversariaLeak uses substitute models to craft adversarial samples, which are then handpicked to infer sensitive information. Our extensive evaluation on the MAAD-Face and CelebA datasets, which includes over 200 different target models, shows that AdversariaLeak outperforms state-of-the-art EIL attacks in inferring the property that best characterizes the FR model's training set while maintaining a small query budget and practical attacker assumptions.
LLMCO4MR: LLMs-aided Neural Combinatorial Optimization for Ancient Manuscript Restoration from Fragments with Case Studies on Dunhuang
PDF ↗Restoring ancient manuscripts fragments, such as those from Dunhuang, is crucial for preserving human historical culture. However, their worldwide dispersal and the shifts in cultural and historical contexts pose significant restoration challenges. Traditional archaeological efforts primarily focus on manually piecing major fragments together, yet the vast majority of small, more intricate pieces remain largely unexplored, which is technically due to their irregular shapes, sparse textual content, and extensive combinatorial space for reassembly. In this paper, we formalize the task of restoring the ancient manuscript from fragments as a cardinality-constrained combinatorial optimization problem, and propose a general framework named LLMCO4MS: (Multimodal) Large Language Model-aided Combinatorial Optimization Neural Networks for Ancient Manuscript Restoration. Specifically, LLMCO4MS encapsulates a neural combinatorial solver equipped with a differentiable optimal transport (OT) layer, to efficiently predict the Top-K likely reassembly candidates. Innovatively, the Multimodal Large Language Model (MLLM) is then adopted and prompted to yield pairwise matching confidence and relative directions for final restoration. Extensive experiments on both synthetic data and real-world famous Dunhuang fragments demonstrate superior performance of our approach. In particular, LLMCO4MS has facilitated the discovery of previously unknown civilian economic documents from the 10-th century in real-world applications.
Text-to-3D synthesis has recently emerged as a new approach to sampling 3D models by adopting pretrained text-to-image models as guiding visual priors. An intriguing but underexplored problem with existing text-to-3D methods is that 3D models obtained from the sampling-by-optimization procedure tend to have mode collapses, and hence poor diversity in their results. In this paper, we provide an analysis and identify potential causes of such a limited diversity, which motivates us to devise a new method that considers the joint generation of different 3D models from the same text prompt. We propose to use augmented text prompts via textual inversion of reference images to diversify the joint generation. We show that our method leads to improved diversity in text-to-3D synthesis qualitatively and quantitatively.
6D Object Pose Estimation is a critical yet challenging task in the field of computer vision, distinguished from more traditional 2D tasks by its lack of large-scale datasets. This scarcity hampers comprehensive evaluation of model performance and consequently, limits research development while also restricting the applicability of research across diverse domains due to the limited number of instances or categories available. To address these issues and facilitate progress in this field, this paper introduces Omni6DPose, a substantial dataset characterized by its diversity in object categories, large scale, and variety in object materials. Omni6DPose is divided into three main components: ROPE (Real 6D Object Pose Estimation Dataset), which includes 270,000 images annotated with over one million annotations across 600 instances in 140 categories; SOPE(Simulated 6D Object Pose Estimation Dataset), consisting of 350,000 images created in a mixed reality setting with depth simulation, annotated with over one million annotations across 4,000 instances in the same 140 categories; and the manually aligned real scanned objects used in both ROPE and SOPE. Omni6DPose is inherently challenging due to the substantial variations and ambiguities. To address this challenge, we propose GenPose++, a novel framework incorporating a pre-trained 2D foundational model to enhance generalization capabilities and employing a diffusion-based generative approach to adeptly manage ambiguity issues. And, this paper provides a comprehensive benchmarking analysis to evaluate the performance of previous methods on this large-scale dataset in the realms of 6D object pose estimation and pose tracking.
We propose OmniNOCS: a large-scale monocular dataset with 3D Normalized Object Coordinate Space (NOCS), object masks, and 3D bounding box annotations for indoor and outdoor scenes. OmniNOCS has 20 times more object classes and 200 times more instances than existing NOCs datasets (NOCS-Real275, Wild6D). We use OmniNOCS to train a novel, transformer-based monocular NOCS prediction model (NOCSformer) that can predict accurate NOCS, instance masks and poses from 2D object detections across diverse classes. It is the first NOCS model that can generalize to a broad range of classes when prompted with 2D boxes. We evaluate our model on the task of 3D oriented bounding box prediction, where it achieves comparable results to state-of-the-art 3D detection methods such as Cube R-CNN. Unlike other 3D detection methods, our model also provides detailed and accurate 3D object shape and segmentation. We propose a novel benchmark for the task of NOCS prediction based on OmniNOCS, which we hope will serve as a useful baseline for future work in this area. Our dataset and code are at the project website: https://omninocs.github.io
Federated learning (FL) is a general principle for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical applications, but its standard training paradigm requires the clients to backpropagate through the model to compute gradients. Since these clients are typically edge devices and not fully trusted, executing backpropagation on them incurs computational and storage overhead as well as white-box vulnerability. In light of this, we develop backpropagation-free federated learning, dubbed BAFFLE, in which backpropagation is replaced by multiple forward processes to estimate gradients. BAFFLE is 1) memory-efficient and easily fits uploading bandwidth; 2) compatible with inference-only hardware optimization and model quantization or pruning; and 3) well-suited to trusted execution environments, because the clients in BAFFLE only execute forward propagation and return a set of scalars to the server. Empirically we use BAFFLE to train deep models from scratch or to finetune pretrained models, achieving acceptable results.
OP-Align: Object-level and Part-level Alignment for Self-supervised Category-level Articulated Object Pose Estimation
PDF ↗Category-level articulated object pose estimation task focuses on the pose estimation of unknown articulated objects within known categories. Despite its significance, this task remains challenging due to the varying shapes and poses of objects, expensive dataset annotation costs, and complex real-world environments. In this paper, we propose a novel self-supervised approach that leverages a single-frame point cloud to accomplish the aforementioned task. Our model consistently generates canonical reconstruction with a canonical pose and joint state for the entire input object, and it estimates object-level pose that reduce overall pose variance and part-level poses that align each part of the input with its corresponding part of the reconstruction. Experimental results demonstrate our approach achieves state-of-the-art performance to other self-supervised methods and comparable performance to other supervised methods. To assess the performance of our model in real-world scenarios, we also introduce a novel real-world articulated object benchmark dataset.
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 outperforms previous methods on downstream category recognition. In contrast to previous findings, our analysis suggests that the exact trade-off between viewpoint sensitivity/invariance is of modest importance for this. We rather 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.
Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations
PDF ↗We introduce Affective Visual Dialog , an emotion explanation and reasoning task as a testbed for research on understanding constructed emotions in response to visually grounded conversations. The task involves three skills: (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction and (3) Affective explanation generation based on the dialog. Our key contribution is the collection of a large-scale dataset, dubbed AffectVisDial, consisting of 50K 10-turn visually grounded dialogs as well as concluding emotion attributions and dialog-informed textual emotion explanations, resulting in a total of 27,180 working hours. Notably, the dataset spans a broad range of visual stimuli, covering human heritage and contemporary life, with an average per-turn answer length of about 12 words — 5 times that of the VisDial dataset — and explanations exceeding 28 words on average. We explain our determining design decisions in collecting the dataset, data inclusion and exclusion criteria starting from over 100K dialogs for quality control, and introduce the questioner and answerer tasks that are associated with the participants in the conversation. We propose and demonstrate solid Affective Visual Dialog baselines adapted from state-of-the-art multimodal models. Remarkably, the responses generated by our models show promising emotional reasoning abilities in response to visually grounded conversations. The dataset and code will be publicly available