ECCV 2024 论文列表

"Generating realistic audio for human actions is important for many applications, such as creating sound effects for films or virtual reality games. Existing approaches implicitly assume total correspondence between the video and audio during training, yet many sounds happen off-screen and have weak to no correspondence with the visuals—resulting in uncontrolled ambient sounds or hallucinations at test time. We propose a novel ambient-aware audio generation model, AV-LDM. We devise a novel audio-conditioning mechanism to learn to disentangle foreground action sounds from the ambient background sounds in in-the-wild training videos. Given a novel silent video, our model uses retrieval-augmented generation to create audio that matches the visual content both semantically and temporally. We train and evaluate our model on two in-the-wild egocentric video datasets, Ego4D and EPIC-KITCHENS, and we introduce Ego4D-Sounds—1.2M curated clips with action-audio correspondence. Our model outperforms an array of existing methods, allows controllable generation of the ambient sound, and even shows promise for generalizing to computer graphics game clips. Overall, our approach is the first to focus video-to-audio generation faithfully on the observed visual content despite training from uncurated clips with natural background sounds."

"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 forecasting1 . 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. 1 Code: https://github.com/alfredgu001324/MapBEVPrediction"

"In recent times, the need for effective super-resolution (SR) techniques has surged, especially for large-scale images ranging 2K to 8K resolutions. For DNN-based SISR, decomposing images into overlapping patches is typically necessary due to computational constraints. In such patch-decomposing scheme, one can allocate computational resources differently based on each patch’s difficulty to further improve efficiency while maintaining SR performance. However, this approach has a limitation: computational resources is uniformly allocated within a patch, leading to lower efficiency when the patch contain pixels with varying levels of restoration difficulty. To address the issue, we propose the Pixel-level Classifier for Single Image Super-Resolution (PCSR), a novel method designed to distribute computational resources adaptively at the pixel level. A PCSR model comprises a backbone, a pixel-level classifier, and a set of pixel-level upsamplers with varying capacities. The pixel-level classifier assigns each pixel to an appropriate upsampler based on its restoration difficulty, thereby optimizing computational resource usage. Our method allows for performance and computational cost balance during inference without re-training. Our experiments demonstrate PCSR’s advantage over existing patch-distributing methods in PSNR-FLOP trade-offs across different backbone models and benchmarks. The code will be available at https://github.com/3587jjh/PCSR."

"Diffusion models have significantly advanced the state of the art in image, audio, and video generation tasks. However, their applications in practical scenarios are hindered by slow inference speed. Drawing inspiration from the consistency models, we propose the Sub-Path Linear Approximation Model (SPLAM), which can accelerate diffusion models while maintaining high-quality image generation. SPLAM treats the PF-ODE trajectory as a series of PF-ODE sub-paths divided by sampled points, and harnesses sub-path linear (SL) ODEs to form a progressive and continuous error estimation along each individual PF-ODE sub-path. The optimization on such SL-ODEs allows SPLAM to construct denoising mapping with smaller cumulative approximated error. An efficient distillation method is also developed to facilitate the incorporation of pre-trained diffusion models, such as latent diffusion models. The extensive experimental results demonstrate SPLAM achieves remarkable training efficiency, requiring only 6 A100 GPU days to produce a high-quality generative model capable of 2 to 4-step generation. Comprehensive evaluations on LAION, MS COCO 2014, and MS COCO 2017 datasets also illustrate that SPLAM surpasses the existing acceleration methods in few-step generation tasks, achieving state-of-the-art performance both on FID and the quality of the generated images."

"This paper attempts to address the object repetition issue in patch-wise higher-resolution image generation. We propose AccDiffusion, an accurate method for patch-wise higher-resolution image generation without training. An in-depth analysis in this paper reveals an identical text prompt for different patches causes repeated object generation, while no prompt compromises the image details. Therefore, our AccDiffusion, for the first time, proposes to decouple the vanilla image-content-aware prompt into a set of patch-content-aware prompts, each of which serves as a more precise description of an image patch. Besides, AccDiffusion also introduces dilated sampling with window interaction for better global consistency in higher-resolution image generation. Experimental comparison with existing methods demonstrates that our AccDiffusion effectively addresses the issue of repeated object generation and leads to better performance in higher-resolution image generation. Our code is released at https://github. com/lzhxmu/AccDiffusion."

"Many classical parametric 3D shape models exist, but creating novel shapes with such models requires expert knowledge of their parameters. For example, imagine creating a specific type of tree using procedural graphics or a new kind of animal from a statistical shape model. Our key idea is to leverage language to control such existing models to produce novel shapes. This involves learning a mapping between the latent space of a vision-language model and the parameter space of the 3D model, which we do using a small set of shape and text pairs. Our hypothesis is that mapping from language to parameters allows us to generate parameters for objects that were never seen during training. If the mapping between language and parameters is sufficiently smooth, then interpolation or generalization in language should translate appropriately into novel 3D shapes. We test our approach with two very different types of parametric shape models (quadrupeds and arboreal trees). We use a learned statistical shape model of quadrupeds and show that we can use text to generate new animals not present during training. In particular, we demonstrate state-of-the-art shape estimation of 3D dogs. This work also constitutes the first language-driven method for generating 3D trees. Finally, embedding images in the CLIP latent space enables us to generate animals and trees directly from images."

"Facial Action Units (AU) is a vital concept in the realm of affective computing, and AU detection has always been a hot research topic. Existing methods suffer from overfitting issues due to the utilization of a large number of learnable parameters on scarce AU-annotated datasets or heavy reliance on substantial additional relevant data. Parameter-Efficient Transfer Learning (PETL) provides a promising paradigm to address these challenges, whereas its existing methods lack design for AU characteristics. Therefore, we innovatively investigate PETL paradigm to AU detection, introducing AUFormer and proposing a novel Mixture-of-Knowledge Expert (MoKE) collaboration mechanism. An individual MoKE specific to a certain AU with minimal learnable parameters first integrates personalized multi-scale and correlation knowledge. Then the MoKE collaborates with other MoKEs in the expert group to obtain aggregated information and inject it into the frozen Vision Transformer (ViT) to achieve parameter-efficient AU detection. Additionally, we design a Margin-truncated Difficulty-aware Weighted Asymmetric Loss (MDWA-Loss), which can encourage the model to focus more on activated AUs, differentiate the difficulty of unactivated AUs, and discard potential mislabeled samples. Extensive experiments from various perspectives, including within-domain, cross-domain, data efficiency, and micro-expression domain, demonstrate AUFormer’s state-of-the-art performance and robust generalization abilities without relying on additional relevant data. The code for AUFormer is available at https://github.com/yuankaishen2001/AUFormer."

"Aiming to enhance Face Recognition (FR) on Low-Quality (LQ) inputs, recent studies suggest incorporating synthetic LQ samples into training. Although promising, the quality factors that are considered in these works are general rather than FR-specific, , atmospheric turbulence, resolution, . Motivated by the observation of the vulnerability of current FR models to even small Face Alignment Errors (FAE) in LQ images, we present a simple yet effective method that considers FAE as another quality factor that is tailored to FR. We seek to improve LQ FR by enhancing FR models’ robustness to FAE. To this aim, we formalize the problem as a combination of differentiable spatial transformations and adversarial data augmentation in FR. We perturb the alignment of the training samples using a controllable spatial transformation and enrich the training with samples expressing FAE. We demonstrate the benefits of the proposed method by conducting evaluations on IJB-B, IJB-C, IJB-S (+4.3% Rank1), and TinyFace (+2.63%). https://github.com/msed-Ebrahimi/ARoFace"

"Referring Expression Comprehension (REC) aims to ground the target object based on a given referring expression, which requires expensive instance-level annotations for training. To address this issue, recent advances explore an efficient one-stage weakly supervised REC model called RefCLIP. Particularly, RefCLIP utilizes anchor features of pre-trained one-stage detection networks to represent candidate objects and conducts anchor-text ranking to locate the referent. Despite the effectiveness, we identify that visual semantics of RefCLIP are ambiguous and insufficient for weakly supervised REC modeling. To address this issue, we propose a novel method that enriches visual semantics with various prompt information, called anchor-based prompt learning (APL). Specifically, APL contains an innovative anchor-based prompt encoder (APE) to produce discriminative prompts covering three aspects of REC modeling, e.g., position, color and category. These prompts are dynamically fused into anchor features to improve the visual description power. In addition, we propose two novel auxiliary objectives to achieve accurate vision-language alignment in APL, namely text reconstruction loss and visual alignment loss. To validate APL, we conduct extensive experiments on four REC benchmarks, namely RefCOCO, RefCOCO+, RefCOCOg and ReferIt. Experimental results not only show the state-of-the-art performance of APL against existing methods on four benchmarks, e.g., +6.44% over RefCLIP on RefCOCO, but also confirm its strong generalization ability on weakly supervised referring expression segmentation. Source codes released at: https://github.com/Yaxin9Luo/APL."

"This work investigates the problem of instance-level image retrieval re-ranking with the constraint of memory efficiency, ultimately aiming to limit memory usage to 1KB per image. Departing from the prevalent focus on performance enhancements, this work prioritizes the crucial trade-off between performance and memory requirements. The proposed model uses a transformer-based architecture designed to estimate image-to-image similarity by capturing interactions within and across images based on their local descriptors. A distinctive property of the model is the capability for asymmetric similarity estimation. Database images are represented with a smaller number of descriptors compared to query images, enabling performance improvements without increasing memory consumption. To ensure adaptability across different applications, a universal model is introduced that adjusts to a varying number of local descriptors during the testing phase. Results on standard benchmarks demonstrate the superiority of our approach over both hand-crafted and learned models. In particular, compared with current state-of-the-art methods that overlook their memory footprint, our approach not only attains superior performance but does so with a significantly reduced memory footprint. The code and pretrained models are publicly available at: https://github.com/pavelsuma/ames"

"Egocentric videos provide a unique perspective into individuals’ daily experiences, yet their unstructured nature presents challenges for perception. In this paper, we introduce , a novel approach aimed at enhancing the comprehension of very-long egocentric videos. Inspired by the human’s ability to maintain information from a single watching, focuses on constructing a self-contained representations from one egocentric video, capturing key locations and object interactions. This representation is semantic-free and facilitates multiple queries without the need to reprocess the entire visual content. Additionally, to evaluate our understanding of very-long egocentric videos, we introduce the new (), composed of more than 20K of highly challenging visual queries from EPIC-KITCHENS. These queries cover different levels of video reasoning (sequencing, concurrency and temporal grounding) to assess detailed video understanding capabilities. We showcase improved performance of on , surpassing other video QA baselines by a substantial margin."

"Transformer-based architectures have become the de-facto standard models for diverse vision tasks owing to their superior performance. As the size of these transformer-based models continues to scale up, model distillation becomes extremely important in real-world deployments, 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, 10× 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 AMD outperforms several established baselines, paving a path for future knowledge distillation methods on large-scale vision models."

"We propose Image Content Appeal Assessment (), a novel metric that quantifies the level of positive interest an image’s content generates for viewers, such as the appeal of food in a photograph. This is fundamentally different from traditional Image-Aesthetics Assessment (IAA), which judges an image’s artistic quality. While previous studies often confuse the concepts of “aesthetics” and “appeal,” our work addresses this by being the first to study explicitly. To do this, we propose a novel system that automates dataset creation and implements algorithms to estimate and boost content appeal. We use our pipeline to generate two large-scale datasets (70K+ images each) in diverse domains (food and room interior design) to train our models, which revealed little correlation between content appeal and aesthetics. Our user study, with more than 76% of participants preferring the appeal-enhanced images, confirms that our appeal ratings accurately reflect user preferences, establishing as a unique evaluative criterion. Our code and datasets are available at https://github.com/SherryXTChen/AID-Appeal."

"Object counting methods typically rely on manually annotated datasets. The cost of creating such datasets has restricted the versatility of these networks to count objects from specific classes (such as humans or penguins), and counting objects from diverse categories remains a challenge. The availability of robust text-to-image latent diffusion models (LDMs) raises the question of whether these models can be utilized to generate counting datasets. However, LDMs struggle to create images with an exact number of objects based solely on text prompts but they can be used to offer a dependable sorting signal by adding and removing objects within an image. Leveraging this data, we initially introduce an unsupervised sorting methodology to learn object-related features that are subsequently refined and anchored for counting purposes using counting data generated by LDMs. Further, we present a density classifier-guided method for dividing an image into patches containing objects that can be reliably counted. Consequently, we can generate counting data for any type of object and count them in an unsupervised manner. Our approach outperforms unsupervised and few-shot alternatives and is not restricted to specific object classes for which counting data is available. Code available at: github.com/adrian-dalessandro/AFreeCA."

"Short-Term object-interaction Anticipation (STA) consists of detecting the location of the next-active objects, the noun and verb categories of the interaction, and the time to contact from the observation of egocentric video. This ability is fundamental for wearable assistants or human-robot interaction to understand the user’s goals, but there is still room for improvement to perform STA in a precise and reliable way. In this work, we improve the performance of STA predictions with two contributions: 1) We propose STAformer, a novel attention-based architecture integrating frame-guided temporal pooling, dual image-video attention, and multiscale feature fusion to support STA predictions from an image-input video pair; 2) We introduce two novel modules to ground STA predictions on human behavior by modeling affordances. First, we integrate an environment affordance model which acts as a persistent memory of interactions that can take place in a given physical scene. Second, we predict interaction hotspots from the observation of hands and object trajectories, increasing confidence in STA predictions localized around the hotspot. Our results show significant relative Overall Top-5 mAP improvements of up to +45% on Ego4D and +42% on a novel set of curated EPIC-Kitchens STA labels. We will release the code, annotations, and pre-extracted affordances on Ego4D and EPIC-Kitchens to encourage future research in this area."

"Point cloud completion involves inferring missing parts of 3D objects from incomplete point cloud data. It requires a model that understands the global structure of the object and reconstructs local details. To this end, we propose a global perception and local attention network, termed AEDNet, for point cloud completion. The proposed AEDNet utilizes designed adaptive point cloud embedding and disentanglement (AED) module in both the encoder and decoder to globally embed and locally disentangle the given point cloud. In the AED module, we introduce a global embedding operator that employs the devised slot attention to compose point clouds into different embeddings, each focusing on specific parts of 3D objects. Then, we proposed a multiview-aware disentanglement operator to disentangle geometric information from those embeddings in the 3D viewpoints generated on a unit sphere. These 3D viewpoints enable us to observe point clouds from the outside rather than from within, resulting in a comprehensive understanding of their geometry. Additionally, the arbitrary number of points and point-wise features can be disentangled by changing the number of viewpoints, reaching high flexibility. Experiments show that our proposed method achieves state-of-the-art results on both MVP and PCN datasets."

"Recovering camera poses from a set of images is a foundational task in 3D computer vision, which powers key applications such as 3D scene/object reconstructions. Classic methods often depend on feature correspondence, such as keypoints, which require the input images to have large overlap and small viewpoint changes. Such requirements present considerable challenges in scenarios with sparse views. Recent data-driven approaches aim to directly output camera poses, either through regressing the 6DoF camera poses or formulating rotation as a probability distribution. However, each approach has its limitations. On one hand, directly regressing the camera poses can be ill-posed, since it assumes a single mode, which is not true under symmetry and leads to sub-optimal solutions. On the other hand, probabilistic approaches are capable of modeling the symmetry ambiguity, yet they sample the entire space of rotation uniformly by brute-force. This leads to an inevitable trade-off between high sample density, which improves model precision, and sample efficiency that determines the runtime. In this paper, we propose to unify the two frameworks by employing a generator and a discriminator: the generator is trained to output multiple hypotheses of 6DoF camera pose to represent a distribution and handle multi-mode ambiguity, and the discriminator is trained to identify the hypothesis that best explains the data. This allows to combine the best of both worlds, achieving substantially higher precision as well as lower runtime than previous methods in empirical evaluations."

"In the field of autonomous driving, online High-definition (HD) map construction is crucial for planning tasks. Recent studies have developed several high-performance HD map construction models to meet the demand. However, the point sequences generated by recent HD map construction models are jittery or jagged due to prediction bias and impact subsequent tasks. To mitigate this jitter issue, we propose the Anti-Disturbance Map construction framework (ADMap), which contains Multi-scale Perception Neck (MPN), Instance Interactive Attention (IIA), and Vector Direction Difference Loss (VDDL). By exploring the point sequence relations between and within instances in a cascading manner, our proposed ADMap effectively monitors the point sequence prediction process, and achieves state-of-the-art performance on the nuScenes and Argoverse2 datasets. Extensive results demonstrate its ability to produce stable and reliable map elements in complex and changing driving scenarios."

"The field of visual Industrial Anomaly Detection (IAD) has brought forth many new semi-supervised learning methods in recent years. At the same time, there have been few new datasets for benchmarking the methods. The most popular dataset is MVTec-AD dataset, because of its diversity of categories and availability of industrial objects. But many methods already achieve AUROC scores of more than 99 % on the MVTec-AD dataset. The defects of the categories that the dataset provides appear to be easily detectable. Furthermore, there is no existing approach to statistically describe the defects that need to be found in IAD datasets. This paper presents a new dataset for visual industrial anomaly detection and a novel approach for Anomaly Detection Dataset Difficulty assessment with the AD3 score. The new dataset named VIADUCT contains 49 categories and 10,986 high resolution images from eleven different sectors. Through the support of several manufacturing companies, numerous real inspection problems are presented through the dataset. It contains a large number of different defects with detailed pixel-wise annotations. The VIADUCT dataset is compared with other state of the art datasets to underline its added value. Therefore, we provide an overview for each dataset regarding the number of categories, images, defect categories and defects. In addition to these obvious comparisons the defects of the datasets are described with the AD3 score. This novel score is used to analyze the size of the defects and the similarity between the defect and its corresponding object. Using seven selected methods from industrial anomaly detection, a benchmark is performed on the new dataset, showing that there is still potential for improvement. It is shown that the VIADUCT dataset is the largest dataset in the field of image-based industrial anomaly detection. In addition to its very small defects which are hard to recognize, the dataset also offers the greatest variance of possible defects and the most defect classes. Describing the datasets with AD3 score it can be found that VIADUCT dataset have the most inconspicuous defects. With the AD3 score we are able to create a-priori knowledge for every single defect in IAD datasets. The AD3 score correlates with the results of the IAD method benchmark, showing that it can be used to estimate defect detection difficulty. In the future, new objects can be assessed to see whether defects can be recognized using IAD methods before an energy-intensive benchmark is performed. The simple calculation of the AD3 score generates valuable a-priori knowledge and can save resources."