ECCV 2024 Papers

"Availability poisons exploit supervised learning (SL) algorithms by introducing class-related shortcut features in images such that models trained on poisoned data are useless for real-world datasets. Self-supervised learning (SSL), which utilizes augmentations to learn instance discrimination, is regarded as a strong defense against poisoned data. However, by extending the study of SSL across multiple poisons on the CIFAR-10 and ImageNet-100 datasets, we demonstrate that it often performs poorly, far below that of training on clean data. Leveraging the vulnerability of SL to poison attacks, we introduce adversarial training (AT) on SL to obfuscate poison features and guide robust feature learning for SSL. Our proposed defense, designated VESPR (Vulnerability Exploitation of Supervised Poisoning for Robust SSL), surpasses the performance of six previous defenses across seven popular availability poisons. VESPR displays superior performance over all previous defenses, boosting the minimum and average ImageNet-100 test accuracies of poisoned models by 16% and 9%, respectively. Through analysis and ablation studies, we elucidate the mechanisms by which VESPR learns robust class features. Code: https://github.com/JStyborski/ VESPR"

"Hallucinations in vision-language models pose a significant challenge to their reliability, particularly in the generation of long captions. Current methods fall short of accurately identifying and mitigating these hallucinations. To address this issue, we introduce ESREAL, a novel unsupervised reinforcement learning framework designed to suppress the generation of hallucinations through accurate localization and penalization of hallucinated tokens. Initially, ESREAL creates a reconstructed image based on the generated caption and aligns its corresponding regions with those of the original image. This semantic reconstruction aids in identifying both the presence and type of token-level hallucinations within the generated caption. Subsequently, ESREAL computes token-level hallucination scores by assessing the semantic similarity of aligned regions based on the type of hallucination. Finally, ESREAL employs a proximal policy optimization algorithm, where it selectively penalizes hallucinated tokens according to their token-level hallucination scores. Our framework notably reduces hallucinations in LLaVA, InstructBLIP, and mPLUG-Owl2 by 32.81%, 27.08%, and 7.46% on the CHAIR metric. This improvement is achieved solely through signals derived from the image itself, without the need for any image-text pairs."

"Recent advancements in Time-of-Flight (ToF) depth denoising have achieved impressive results in removing Multi-Path Interference (MPI) and shot noise. However, existing methods only utilize a single frame of ToF data, neglecting the correlation between frames. In this paper, we propose the first learning-based framework for multi-frame ToF denoising. Different from existing methods, our framework leverages the correlation between neighboring frames to guide ToF noise removal with a confidence map. Specifically, we introduce a Dual-Correlation Estimation Module, which exploits both intra- and inter-correlation. The intra-correlation explicitly establishes the relevance between the spatial positions of geometric objects within the scene, aiding in depth residual initialization. The inter-correlation discerns variations in ToF noise distribution across different frames, thereby locating the regions with strong ToF noise. To further leverage dual-correlation, we introduce a Confidence-guided Residual Regression Module to predict a confidence map, which guides the residual regression to prioritize the regions with strong ToF noise. The experimental evaluations have consistently shown that our framework outperforms existing ToF denoising methods, highlighting its superior performance in effectively reducing strong ToF noise. The source code is available at https://github.com/gtdong-ustc/multi-frame-tof-denoising."

"∗ Equal contribution Corresponding authorIn this paper, we explore a novel framework, EGIInet (Explicitly Guided Information Interaction Network), a model for View-guided Point cloud Completion (ViPC) task, which aims to restore a complete point cloud from a partial one with a single view image. In comparison with previous methods that relied on the global semantics of input images, EGIInet efficiently combines the information from two modalities by leveraging the geometric nature of the completion task. Specifically, we propose an explicitly guided information interaction strategy supported by modal alignment for point cloud completion. First, in contrast to previous methods which simply use 2D and 3D backbones to encode features respectively, we unified the encoding process to promote modal alignment. Second, we propose a novel explicitly guided information interaction strategy that could help the network identify critical information within images, thus achieving better guidance for completion. Extensive experiments demonstrate the effectiveness of our framework, and we achieved a new state-of-the-art (+16% CD over XMFnet) in benchmark datasets despite using fewer parameters than the previous methods. The pre-trained model and code and are available at https://github.com/WHU-USI3DV/EGIInet."

"The concept bottleneck model (CBM) is an interpretable-by-design framework that makes decisions by first predicting a set of interpretable concepts, and then predicting the class label based on the given concepts. Existing CBMs are trained with a fixed set of concepts (concepts are either annotated by the dataset or queried from language models). However, this closed-world assumption is unrealistic in practice, as users may wonder about the role of any desired concept in decision-making after the model is deployed. Inspired by the large success of recent vision-language pre-trained models such as CLIP in zero-shot classification, we propose “OpenCBM” to equip the CBM with open vocabulary concepts via: (1) Aligning the feature space of a trainable image feature extractor with that of a CLIP’s image encoder via a prototype based feature alignment; (2) Simultaneously training an image classifier on the downstream dataset; (3) Reconstructing the trained classification head via any set of user-desired textual concepts encoded by CLIP’s text encoder. To reveal potentially missing concepts from users, we further propose to iteratively find the closest concept embedding to the residual parameters during the reconstruction until the residual is small enough. To the best of our knowledge, our “OpenCBM” is the first CBM with concepts of open vocabularies, providing users the unique benefit such as removing, adding, or replacing any desired concept to explain the model’s prediction even after a model is trained. Moreover, our model significantly outperforms the previous state-of-the-art CBM by 9% in the classification accuracy on the benchmark dataset CUB-200-2011."

"Exemplar-free class-incremental learning using a backbone trained from scratch and starting from a small first task presents a significant challenge for continual representation learning. Prototype-based approaches, when continually updated, face the critical issue of semantic drift due to which the old class prototypes drift to different positions in the new feature space. Through an analysis of prototype-based continual learning, we show that forgetting is not due to diminished discriminative power of the feature extractor, and can potentially be corrected by drift compensation. To address this, we propose Learnable Drift Compensation (LDC), which can effectively mitigate drift in any moving backbone, whether supervised or unsupervised. LDC is fast and straightforward to integrate on top of existing continual learning approaches. Furthermore, we showcase how LDC can be applied in combination with self-supervised CL methods, resulting in the first exemplar-free semi-supervised continual learning approach. We achieve state-of-the-art performance in both supervised and semi-supervised settings across multiple datasets. Code is available at https: //github.com/alviur/ldc."

"Semi-supervised learning is a learning method that uses both labeled and unlabeled samples to improve the performance of the model while reducing labeling costs. When there were tens to hundreds of labeled samples, semi-supervised learning methods showed good performance, but most of them showed poor performance when only a small number of labeled samples were given. In this paper, we focus on challenging label-scarce environments, where there are only a few labeled samples per class. Our proposed model, ExMatch, is designed to obtain reliable information from unlabeled samples using self-supervised models and utilize it for semi-supervised learning. In the training process, ExMatch guides the model to maintain an appropriate distribution and resist learning from incorrect pseudo-labels based on the information from self-supervised models and its own model. ExMatch shows stable training progress and the state-of-the-art performance on multiple benchmark datasets. In extremely label-scare situations, performance is improved by about 5% to 21% for CIFAR-10, CIFAR-100 and SVHN. ExMatch also demonstrates significant performance improvements in high-resolution and large-scale dataset such as STL-10, Tiny-ImageNet, and ImageNet."

"Self-supervised learning methods have demonstrated impressive performance across visual understanding tasks, including human behavior understanding. However, there has been limited work for self-supervised learning for egocentric social videos. Visual processing in such contexts faces several challenges, including noisy input, limited availability of egocentric social data, and the absence of pretrained models tailored to egocentric contexts. We propose , a novel framework leveraging novel-view face synthesis for dynamic perspective data augmentation from abundant exocentric videos and enhance self-supervised learning process for VideoMAE via: 1) reconstructing exocentric videos from masked dynamic perspective videos; and 2) predicting feature representations of a teacher model based on the corresponding exocentric frames. Experimental results demonstrate that consistently excels across diverse social role understanding tasks. It achieves state-of-the-art results in Ego4D’s Talk-to-me challenge (+0.7% mAP, +3.2% Accuracy). For the Look-at-me challenge, it achieves competitive performance with the state-of-the-art (-0.7% mAP, +1.5% Accuracy) without supervised training on external data. On the EasyCom dataset, our method surpasses both supervised Active Speaker Detection approaches and state-of-the-art video encoders (+1.2% mAP, +1.9% Accuracy compared to MARLIN)."

"Recent advancements in ultra-high-resolution unpaired image-to-image translation have aimed to mitigate the constraints imposed by limited GPU memory through patch-wise inference. Nonetheless, existing methods often compromise between the reduction of noticeable tiling artifacts and the preservation of color and hue contrast, attributed to the reliance on global image- or patch-level statistics in the instance normalization layers. In this study, we introduce a Dense Normalization (DN) layer designed to estimate pixel-level statistical moments. This approach effectively diminishes tiling artifacts while concurrently preserving local color and hue contrasts. To address the computational demands of pixel-level estimation, we further propose an efficient interpolation algorithm. Moreover, we invent a parallelism strategy that enables the DN layer to operate in a single pass. Through extensive experiments, we demonstrate that our method surpasses all existing approaches in performance. Notably, our DN layer is hyperparameter-free and can be seamlessly integrated into most unpaired image-to-image translation frameworks without necessitating retraining. Overall, our work paves the way for future exploration in handling images of arbitrary resolutions within the realm of unpaired image-to-image translation. Code is available at: https://github.com/Kaminyou/Dense-Normalization."

"In this paper, we propose EventBind, a novel and effective framework that unleashes the potential of vision-language models (VLMs) for event-based recognition to compensate for the lack of large-scale event-based datasets. In particular, due to the distinct modality gap with the image-text data and the lack of large-scale datasets, learning a common representation space for images, texts, and events is non-trivial. Intuitively, we need to address two key challenges: 1) how to generalize CLIP’s visual encoder to event data while fully leveraging events’ unique properties, , sparsity and high temporal resolution; 2) how to effectively align the multi-modal embeddings, , image, text, and events. Accordingly, we first introduce a novel event encoder that subtly models the temporal information from events and meanwhile generates event prompts for modality bridging. We then design a text encoder that generates content prompts and utilizes hybrid text prompts to enhance EventBind’s generalization ability across diverse datasets. With the proposed event encoder, text encoder, and image encoder, a novel Hierarchical Triple Contrastive Alignment HTCA module is introduced to jointly optimize the correlation and enable efficient knowledge transfer among the three modalities. We evaluate various settings, including fine-tuning and few-shot on three benchmarks and our EventBind achieves new state-of-art accuracy compared with the previous methods, such as on N-Caltech101 (+5.34% and +1.70%) and N-Imagenet (+5.65% and +1.99%) with fine-tuning and 20-shot settings respectively. Moreover, our EventBind can be flexibly extended to the event retrieval task using text or image queries, showing plausible performance."

"We tackle the problem of mosaicing bundle adjustment (i.e., simultaneous refinement of camera orientations and scene map) for a purely rotating event camera. We formulate the problem as a regularized non-linear least squares optimization. The objective function is defined using the linearized event generation model in the camera orientations and the panoramic gradient map of the scene. We show that this BA optimization has an exploitable block-diagonal sparsity structure, so that the problem can be solved efficiently. To the best of our knowledge, this is the first work to leverage such sparsity to speed up the optimization in the context of event-based cameras, without the need to convert events into image-like representations. We evaluate our method, called EMBA, on both synthetic and real-world datasets to show its effectiveness (50% photometric error decrease), yielding results of unprecedented quality. In addition, we demonstrate EMBA using high spatial resolution event cameras, yielding delicate panoramas in the wild, even without an initial map. The code is available at https: //github.com/tub-rip/emba."

"Head pose estimation (HPE) is crucial for various applications, including human-computer interaction, augmented reality, and driver monitoring. However, traditional RGB-based methods struggle in challenging conditions like sudden movement and extreme lighting. Event cameras, as a neuromorphic sensor, have the advantages of high temporal resolution and high dynamic range, offering a promising solution for HPE. However, the lack of paired event and head pose data hinders the full potential of event-based HPE. To address this, we introduce two large-scale, diverse event-based head pose datasets encompassing 282 sequences across different resolutions and scenarios. Furthermore, we propose the event-based HPE network, featuring two novel modules: the Event Spatial-Temporal Fusion (ESTF) module and the Event Motion Perceptual Attention (EMPA) module. The ESTF module effectively combines spatial and temporal information from the event streams, while the EMPA module captures crucial motion details across the scene using a large receptive field. We also propose a unified loss function to optimize the network using both angle and rotation matrix information. Extensive experiments demonstrate the superior performance of our network on both datasets, showcasing its effectiveness in handling HPE across various challenging scenarios. The datasets and code are available at https://github. com/Jiahui-Yuan-1/EVHPE."

"Detecting and magnifying imperceptible high-frequency motions in real-world scenarios has substantial implications for industrial and medical applications. These motions are characterized by small amplitudes and high frequencies. Traditional motion magnification methods rely on costly high-speed cameras or active light sources, which limit the scope of their applications. In this work, we propose a dual-camera system consisting of an event camera and a conventional RGB camera for video motion magnification, providing temporally-dense information from the event stream and spatially-dense data from the RGB images. This innovative combination enables a broad and cost-effective amplification of high-frequency motions. By revisiting the physical camera model, we observe that estimating motion direction and magnitude necessitates the integration of event streams with additional image features. On this basis, we propose a novel deep network tailored for event-based motion magnification. Our approach utilizes the Second-order Recurrent Propagation module to proficiently interpolate multiple frames while addressing artifacts and distortions induced by magnified motions. Additionally, we employ a temporal filter to distinguish between noise and useful signals, thus minimizing the impact of noise. We also introduced the first event-based motion magnification dataset, which includes a synthetic subset and a real-captured subset for training and benchmarking. Through extensive experiments in magnifying small-amplitude, high-frequency motions, we demonstrate the effectiveness and accuracy of our dual-camera system and network, offering a cost-effective and flexible solution for motion detection and magnification. Project website: https://openimaginglab.github.io/emm"

"Predicting a potential collision with leading vehicles is an essential functionality of any autonomous/assisted driving system. One bottleneck of existing vision-based solutions is that their updating rate is limited to the frame rate of standard cameras used. In this paper, we present a novel method that estimates the time to collision using a neuromorphic event-based camera, a biologically inspired visual sensor that can sense at exactly the same rate as scene dynamics. The core of the proposed algorithm consists of a two-step approach for efficient and accurate geometric model fitting on event data in a coarse-to-fine manner. The first step is a robust linear solver based on a novel geometric measurement that overcomes the partial observability of event-based normal flow. The second step further refines the resulting model via a spatio-temporal registration process formulated as a nonlinear optimization problem. Experiments on both synthetic and real data demonstrate the effectiveness of the proposed method, outperforming other alternative methods in terms of efficiency and accuracy. Dataset used in this paper can be found at https://nail-hnu.github.io/EventAidedTTC/."

"Introducing event cameras into video super-resolution (VSR) shows great promise. In practice, however, integrating event data as a new modality necessitates a laborious model architecture design. This not only consumes substantial time and effort but also disregards valuable insights from successful existing VSR models. Furthermore, the resource-intensive process of retraining these newly designed models exacerbates the challenge. In this paper, inspired by the recent success of parameter-efficient tuning in reducing the number of trainable parameters of a pre-trained model for downstream tasks, we introduce the Event AdapTER (EATER) for VSR. EATER efficiently utilizes knowledge of VSR models at the feature level through two lightweight and trainable components: the event-adapted alignment (EAA) unit and the event-adapted fusion (EAF) unit. The EAA unit aligns multiple frames based on the event stream in a coarse-to-fine manner, while the EAF unit efficiently fuses frames with the event stream through a multi-scale design. Thanks to both units, EATER outperforms the full fine-tuning approach with parameter efficiency, as demonstrated by comprehensive experiments. Z. Xiao and D. Kai — Equal contribution."

"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, , 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."

"This paper introduces a self-supervised learning framework designed for pre-training neural networks tailored to dense prediction tasks using event camera data. Our approach utilizes solely event data for training. Transferring achievements from dense RGB pre-training directly to event camera data yields subpar performance. This is attributed to the spatial sparsity inherent in an event image (converted from event data), where many pixels do not contain information. To mitigate this sparsity issue, we encode an event image into event patch features, automatically mine contextual similarity relationships among patches, group the patch features into distinctive contexts, and enforce context-to-context similarities to learn discriminative event features. For training our framework, we curate a synthetic event camera dataset featuring diverse scene and motion patterns. Transfer learning performance on downstream dense prediction tasks illustrates the superiority of our method over state-of-the-art approaches."

"Machine learning models are vulnerable to tiny adversarial input perturbations optimized to cause a very large output error. To measure this vulnerability, we need reliable methods that can find such adversarial perturbations. For image classification models, evaluation methodologies have emerged that have stood the test of time. However, we argue that in the area of semantic segmentation, a good approximation of the sensitivity to adversarial perturbations requires significantly more effort than what is currently considered satisfactory. To support this claim, we re-evaluate a number of well-known robust segmentation models in an extensive empirical study. We propose new attacks and combine them with the strongest attacks available in the literature. We also analyze the sensitivity of the models in fine detail. The results indicate that most of the state-of-the-art models have a dramatically larger sensitivity to adversarial perturbations than previously reported. We also demonstrate a size-bias: small objects are often more easily attacked, even if the large objects are robust, a phenomenon not revealed by current evaluation metrics. Our results also demonstrate that a diverse set of strong attacks is necessary, because different models are often vulnerable to different attacks. Our implementation is available at https://github.com/szegedai/Robust-Segmentation-Evaluation."

"Despite significant progress in generative AI, comprehensive evaluation remains challenging because of the lack of effective metrics and standardized benchmarks. For instance, the widely-used CLIPScore measures the alignment between a (generated) image and text prompt, but it fails to produce reliable scores for complex prompts involving compositions of objects, attributes, and relations. One reason is that text encoders of CLIP can notoriously act as a “bag of words”, conflating prompts such as the horse is eating the grass with the grass is eating the horse [?, ?, ?]. To address this, we introduce the VQAScore, which uses a visual-question-answering (VQA) model to produce an alignment score by computing the probability of a Yes answer to a simple Does this figure show {text}? question. Though simpler than prior art, VQAScore computed with off-the-shelf models produces state-of-the-art results across many (8) image-text alignment benchmarks. We also compute VQAScore with an in-house model that follows best practices in the literature. For example, we use a bidirectional image-question encoder that allows image embeddings to depend on the question being asked (and vice versa). Our in-house model, CLIP-FlanT5, outperforms even the strongest baselines that make use of the proprietary GPT-4V. Interestingly, although we train with only images, VQAScore can also align text with video and 3D models. VQAScore allows researchers to benchmark text-to-visual generation using complex texts that capture the compositional structure of real-world prompts. Towards this end, we introduce GenAI-Bench, a more challenging benchmark with 1,600 compositional text prompts that require parsing scenes, objects, attributes, relationships, and high-order reasoning such as comparison and logic. GenAI-Bench also collects over 15,000 human ratings for leading image and video models such as Stable Diffusion, DALL-E 3, Midjourney, and Gen2. We open-source our data, model, and code at link."