CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Deep-learning-based gaze estimation approaches often suffer from notable performance degradation in unseen target domains. One of the primary reasons is that the Fully Connected layer is highly prone to overfitting when mapping the high-dimensional image feature to 3D gaze. In this paper we propose Analytical Gaze Generalization framework (AGG) to improve the generalization ability of gaze estimation models without touching target domain data. The AGG consists of two modules the Geodesic Projection Module (GPM) and the Sphere-Oriented Training (SOT). GPM is a generalizable replacement of FC layer which projects high-dimensional image features to 3D space analytically to extract the principle components of gaze. Then we propose Sphere-Oriented Training (SOT) to incorporate the GPM into the training process and further improve cross-domain performances. Experimental results demonstrate that the AGG effectively alleviate the overfitting problem and consistently improves the cross-domain gaze estimation accuracy in 12 cross-domain settings without requiring any target domain data. The insight from the Analytical Gaze Generalization framework has the potential to benefit other regression tasks with physical meanings.
Referring image segmentation (RIS) aims to precisely segment referents in images through corresponding natural language expressions yet relying on cost-intensive mask annotations. Weakly supervised RIS thus learns from image-text pairs to pixel-level semantics which is challenging for segmenting fine-grained masks. A natural approach to enhancing segmentation precision is to empower weakly supervised RIS with the image segmentation foundation model SAM. Nevertheless we observe that simply integrating SAM yields limited benefits and can even lead to performance regression due to the inevitable noise issues and challenges in excessive focus on object parts. In this paper we present an innovative framework Point PrompTing (PPT) incorporated with the proposed multi-source curriculum learning strategy to address these challenges. Specifically the core of PPT is a point generator that not only harnesses CLIP's text-image alignment capability and SAM's powerful mask generation ability but also generates negative point prompts to address the noisy and excessive focus issues inherently and effectively. In addition we introduce a curriculum learning strategy with object-centric images to help PPT gradually learn from simpler yet precise semantic alignment to more complex RIS.
In addition we introduce a curriculum learning strategy with object-centric images to help PPT gradually learn from simpler yet precise semantic alignment to more complex RIS. Experiments demonstrate that our PPT significantly and consistently outperforms prior weakly supervised techniques on mIoU by 11.34% 14.14% and 6.97% across RefCOCO RefCOCO+ and G-Ref respectively.
In this paper we make the first attempt at achieving the cross-modal (i.e. image-to-events) adaptation for event-based object recognition without accessing any labeled source image data owning to privacy and commercial issues. Tackling this novel problem is non-trivial due to the novelty of event cameras and the distinct modality gap between images and events. In particular as only the source model is available a hurdle is how to extract the knowledge from the source model by only using the unlabeled target event data while achieving knowledge transfer. To this end we propose a novel framework dubbed EventDance for this unsupervised source-free cross-modal adaptation problem. Importantly inspired by event-to-video reconstruction methods we propose a reconstruction-based modality bridging (RMB) module which reconstructs intensity frames from events in a self-supervised manner. This makes it possible to build up the surrogate images to extract the knowledge (i.e. labels) from the source model. We then propose a multi-representation knowledge adaptation (MKA) module that transfers the knowledge to target models learning events with multiple representation types for fully exploring the spatiotemporal information of events.
labels) from the source model. We then propose a multi-representation knowledge adaptation (MKA) module that transfers the knowledge to target models learning events with multiple representation types for fully exploring the spatiotemporal information of events. The two modules connecting the source and target models are mutually updated so as to achieve the best performance. Experiments on three benchmark datasets with two adaption settings show that EventDance is on par with prior methods utilizing the source data.
In the realm of medical 3D data such as CT and MRI images prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution. The lowered resolution between adjacent slices poses challenges hindering optimal viewing experiences and impeding the development of robust downstream analysis algorithms. Various volumetric super-resolution algorithms aim to surmount these challenges enhancing inter-slice resolution and overall 3D medical imaging quality. However existing approaches confront inherent challenges: 1) often tailored to specific upsampling factors lacking flexibility for diverse clinical scenarios; 2) newly generated slices frequently suffer from over-smoothing degrading fine details and leading to inter-slice inconsistency. In response this study presents CycleINR a novel enhanced Implicit Neural Representation model for 3D medical data volumetric super-resolution. Leveraging the continuity of the learned implicit function the CycleINR model can achieve results with arbitrary up-sampling rates eliminating the need for separate training. Additionally we enhance the grid sampling in CycleINR with a local attention mechanism and mitigate over-smoothing by integrating cycle-consistent loss. We introduce a new metric Slice-wise Noise Level Inconsistency (SNLI) to quantitatively assess inter-slice noise level inconsistency.
Additionally we enhance the grid sampling in CycleINR with a local attention mechanism and mitigate over-smoothing by integrating cycle-consistent loss. We introduce a new metric Slice-wise Noise Level Inconsistency (SNLI) to quantitatively assess inter-slice noise level inconsistency. The effectiveness of our approach is demonstrated through image quality evaluations on an in-house dataset and a downstream task analysis on the Medical Segmentation Decathlon liver tumor dataset.
Pre-trained models with large-scale training data such as CLIP and Stable Diffusion have demonstrated remarkable performance in various high-level computer vision tasks such as image understanding and generation from language descriptions. Yet their potential for low-level tasks such as image restoration remains relatively unexplored. In this paper we explore such models to enhance image restoration. As off-the-shelf features (OSF) from pre-trained models do not directly serve image restoration we propose to learn an additional lightweight module called Pre-Train-Guided Refinement Module (PTG-RM) to refine restoration results of a target restoration network with OSF. PTG-RM consists of two components Pre-Train-Guided Spatial-Varying Enhancement (PTG-SVE) and Pre-Train-Guided Channel-Spatial Attention (PTG-CSA). PTG-SVE enables optimal short- and long-range neural operations while PTG-CSA enhances spatial-channel attention for restoration-related learning. Extensive experiments demonstrate that PTG-RM with its compact size (<1M parameters) effectively enhances restoration performance of various models across different tasks including low-light enhancement deraining deblurring and denoising.
Face Video Retouching is a complex task that often requires labor-intensive manual editing. Conventional image retouching methods perform less satisfactorily in terms of generalization performance and stability when applied to videos without exploiting the correlation among frames. To address this issue we propose a Video Retouching transformEr to remove facial imperfections in videos which is referred to as VRetouchEr. Specifically we estimate the apparent motion of imperfections between two consecutive frames and the resulting displacement vectors are used to refine the imperfection map which is synthesized from the current frame together with the corresponding encoder features. The flow-based imperfection refinement is critical for precise and stable retouching across frames. To leverage the temporal contextual information we inject the refined imperfection map into each transformer block for multi-frame masked attention computation such that we can capture the interdependence between the current frame and multiple reference frames. As a result the imperfection regions can be replaced with normal skin with high fidelity while at the same time keeping the other regions unchanged. Extensive experiments are performed to verify the superiority of VRetouchEr over state-of-the-art image retouching methods in terms of fidelity and stability.
Deep neural networks (DNNs) are vulnerable to highly transferable adversarial attacks. Especially many studies have shown that sparse attacks pose a significant threat to DNNs on account of their exceptional imperceptibility. Current sparse attack methods mostly limit only the magnitude and number of perturbations while generally overlooking the location of the perturbations resulting in decreased performances on attack transferability. A subset of studies indicates that perturbations existing in the significant regions with rich classification-relevant features are more effective. Leveraging this insight we introduce the structural sparsity constraint in the framework of generative models to limit the perturbation positions. To ensure that the perturbations are generated towards classification-relevant regions we propose an exact group sparsity training method to learn pixel-level and group-level sparsity. For purpose of improving the effectiveness of sparse training we further put forward masked quantization network and multi-stage optimization algorithm in the training process. Utilizing CNNs as surrogate models extensive experiments demonstrate that our method has higher transferability in image classification attack compared to state-of-the-art methods at approximately same sparsity levels. In cross-model ViT object detection and semantic segmentation attack tasks we also achieve a better attack success rate.
Utilizing CNNs as surrogate models extensive experiments demonstrate that our method has higher transferability in image classification attack compared to state-of-the-art methods at approximately same sparsity levels. In cross-model ViT object detection and semantic segmentation attack tasks we also achieve a better attack success rate. Code is available at https://github.com/MisterRpeng/EGS-TSSA.
The rise of multimodal large language models (MLLMs) has spurred interest in language-based driving tasks. However existing research typically focuses on limited tasks and often omits key multi-view and temporal information which is crucial for robust autonomous driving. To bridge these gaps we introduce NuInstruct a novel dataset with 91K multi-view video-QA pairs across 17 subtasks where each task demands holistic information (e.g. temporal multi-view and spatial) significantly elevating the challenge level. To obtain NuInstruct we propose a novel SQL-based method to generate instruction-response pairs automatically which is inspired by the driving logical progression of humans. We further present BEV-InMLLM an end-to-end method for efficiently deriving instruction-aware Bird's-Eye-View (BEV) features language-aligned for large language models. BEV-InMLLM integrates multi-view spatial awareness and temporal semantics to enhance MLLMs' capabilities on NuInstruct tasks. Moreover our proposed BEV injection module is a plug-and-play method for existing MLLMs. Our experiments on NuInstruct demonstrate that BEV-InMLLM significantly outperforms existing MLLMs e.g 9% improvement on various tasks.
Moreover our proposed BEV injection module is a plug-and-play method for existing MLLMs. Our experiments on NuInstruct demonstrate that BEV-InMLLM significantly outperforms existing MLLMs e.g 9% improvement on various tasks. We release our NuInstruct at https://github.com/xmed-lab/NuInstruct.
Super-resolution (SR) and image generation are important tasks in computer vision and are widely adopted in real-world applications. Most existing methods however generate images only at fixed-scale magnification and suffer from over-smoothing and artifacts. Additionally they do not offer enough diversity of output images nor image consistency at different scales. Most relevant work applied Implicit Neural Representation (INR) to the denoising diffusion model to obtain continuous-resolution yet diverse and high-quality SR results. Since this model operates in the image space the larger the resolution of image is produced the more memory and inference time is required and it also does not maintain scale-specific consistency. We propose a novel pipeline that can super-resolve an input image or generate from a random noise a novel image at arbitrary scales. The method consists of a pretrained auto-encoder a latent diffusion model and an implicit neural decoder and their learning strategies. The proposed method adopts diffusion processes in a latent space thus efficient yet aligned with output image space decoded by MLPs at arbitrary scales. More specifically our arbitrary-scale decoder is designed by the symmetric decoder w/o up-scaling from the pretrained auto-encoder and Local Implicit Image Function (LIIF) in series.
The proposed method adopts diffusion processes in a latent space thus efficient yet aligned with output image space decoded by MLPs at arbitrary scales. More specifically our arbitrary-scale decoder is designed by the symmetric decoder w/o up-scaling from the pretrained auto-encoder and Local Implicit Image Function (LIIF) in series. The latent diffusion process is learnt by the denoising and the alignment losses jointly. Errors in output images are backpropagated via the fixed decoder improving the quality of output images. In the extensive experiments using multiple public benchmarks on the two tasks i.e. image super-resolution and novel image generation at arbitrary scales the proposed method outperforms relevant methods in metrics of image quality diversity and scale consistency. It is significantly better than the relevant prior-art in the inference speed and memory usage.
Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities encompassing a wide range from 3D shapes to images and audio. Within the realm of 3D shape representation Neural Signed Distance Functions (SDF) have demonstrated remarkable potential in faithfully encoding intricate shape geometry. However learning SDFs from 3D point clouds in the absence of ground truth supervision remains a very challenging task. In this paper we propose a method to infer occupancy fields instead of SDFs as they are easier to learn from sparse inputs. We leverage a margin-based uncertainty measure to differentiably sample from the decision boundary of the occupancy function and supervise the sampled boundary points using the input point cloud. We further stabilise the optimization process at the early stages of the training by biasing the occupancy function towards minimal entropy fields while maximizing its entropy at the input point cloud. Through extensive experiments and evaluations we illustrate the efficacy of our proposed method highlighting its capacity to improve implicit shape inference with respect to baselines and the state-of-the-art using synthetic and real data.
This paper introduces a novel approach to learning instance segmentation using extreme points i.e. the topmost leftmost bottommost and rightmost points of each object. These points are readily available in the modern bounding box annotation process while offering strong clues for precise segmentation and thus allows to improve performance at the same annotation cost with box-supervised methods. Our work considers extreme points as a part of the true instance mask and propagates them to identify potential foreground and background points which are all together used for training a pseudo label generator. Then pseudo labels given by the generator are in turn used for supervised learning of our final model. On three public benchmarks our method significantly outperforms existing box-supervised methods further narrowing the gap with its fully supervised counterpart. In particular our model generates high-quality masks when a target object is separated into multiple parts where previous box-supervised methods often fail.
We propose a novel method for 3D point cloud action recognition. Understanding human actions in RGB videos has been widely studied in recent years however its 3D point cloud counterpart remains under-explored despite the clear value that 3D information may bring. This is mostly due to the inherent limitation of the point cloud data modality---lack of structure permutation invariance and varying number of points---which makes it difficult to learn a spatio-temporal representation. To address this limitation we propose the 3DinAction pipeline that first estimates patches moving in time (t-patches) as a key building block alongside a hierarchical architecture that learns an informative spatio-temporal representation. We show that our method achieves improved performance on existing datasets including DFAUST and IKEA ASM. Code is publicly available at https://github.com/sitzikbs/3dincaction
Diffusion models have recently revolutionized the field of image synthesis due to their ability to generate photorealistic images. However one of the major drawbacks of diffusion models is that the image generation process is costly. A large image-to-image network has to be applied many times to iteratively refine an image from random noise. While many recent works propose techniques to reduce the number of required steps they generally treat the underlying denoising network as a black box. In this work we investigate the behavior of the layers within the network and find that 1) the layers' output changes smoothly over time 2) the layers show distinct patterns of change and 3) the change from step to step is often very small. We hypothesize that many layer computations in the denoising network are redundant. Leveraging this we introduce Block Caching in which we reuse outputs from layer blocks of previous steps to speed up inference. Furthermore we propose a technique to automatically determine caching schedules based on each block's changes over timesteps. In our experiments we show through FID human evaluation and qualitative analysis that Block Caching allows to generate images with higher visual quality at the same computational cost.
Furthermore we propose a technique to automatically determine caching schedules based on each block's changes over timesteps. In our experiments we show through FID human evaluation and qualitative analysis that Block Caching allows to generate images with higher visual quality at the same computational cost. We demonstrate this for different state-of-the-art models (LDM and EMU) and solvers (DDIM and DPM).
Medical generative models acknowledged for their high-quality sample generation ability have accelerated the fast growth of medical applications. However recent works concentrate on separate medical generation models for distinct medical tasks and are restricted to inadequate medical multi-modal knowledge constraining medical comprehensive diagnosis. In this paper we propose MedM2G a Medical Multi-Modal Generative framework with the key innovation to align extract and generate medical multi-modal within a unified model. Extending beyond single or two medical modalities we efficiently align medical multi-modal through the central alignment approach in the unified space. Significantly our framework extracts valuable clinical knowledge by preserving the medical visual invariant of each imaging modal thereby enhancing specific medical information for multi-modal generation. By conditioning the adaptive cross-guided parameters into the multi-flow diffusion framework our model promotes flexible interactions among medical multi-modal for generation. MedM2G is the first medical generative model that unifies medical generation tasks of text-to-image image-to-text and unified generation of medical modalities (CT MRI X-ray). It performs 5 medical generation tasks across 10 datasets consistently outperforming various state-of-the-art works.
In the field of class incremental learning (CIL) generative replay has become increasingly prominent as a method to mitigate the catastrophic forgetting alongside the continuous improvements in generative models. However its application in class incremental object detection (CIOD) has been significantly limited primarily due to the complexities of scenes involving multiple labels. In this paper we propose a novel approach called stable diffusion deep generative replay (SDDGR) for CIOD. Our method utilizes a diffusion-based generative model with pre-trained text-to-image diffusion networks to generate realistic and diverse synthetic images. SDDGR incorporates an iterative refinement strategy to produce high-quality images encompassing old classes. Additionally we adopt an L2 knowledge distillation technique to improve the retention of prior knowledge in synthetic images. Furthermore our approach includes pseudo-labeling for old objects within new task images preventing misclassification as background elements. Extensive experiments on the COCO 2017 dataset demonstrate that SDDGR significantly outperforms existing algorithms achieving a new state-of-the-art in various CIOD scenarios.
Reconstructing dynamic objects from monocular videos is a severely underconstrained and challenging problem and recent work has approached it in various directions. However owing to the ill-posed nature of this problem there has been no solution that can provide consistent high-quality novel views from camera positions that are significantly different from the training views. In this work we introduce Neural Parametric Gaussians (NPGs) to take on this challenge by imposing a two-stage approach: first we fit a low-rank neural deformation model which then is used as regularization for non-rigid reconstruction in the second stage. The first stage learns the object's deformations such that it preserves consistency in novel views. The second stage obtains high reconstruction quality by optimizing 3D Gaussians that are driven by the coarse model. To this end we introduce a local 3D Gaussian representation where temporally shared Gaussians are anchored in and deformed by local oriented volumes. The resulting combined model can be rendered as radiance fields resulting in high-quality photo-realistic reconstructions of the non-rigidly deforming objects. We demonstrate that NPGs achieve superior results compared to previous works especially in challenging scenarios with few multi-view cues.
Deep learning-based monocular depth estimation (MDE) extensively applied in autonomous driving is known to be vulnerable to adversarial attacks. Previous physical attacks against MDE models rely on 2D adversarial patches so they only affect a small localized region in the MDE map but fail under various viewpoints. To address these limitations we propose 3D Depth Fool (3D^2Fool) the first 3D texture-based adversarial attack against MDE models. 3D^2Fool is specifically optimized to generate 3D adversarial textures agnostic to model types of vehicles and to have improved robustness in bad weather conditions such as rain and fog. Experimental results validate the superior performance of our 3D^2Fool across various scenarios including vehicles MDE models weather conditions and viewpoints. Real-world experiments with printed 3D textures on physical vehicle models further demonstrate that our 3D^2Fool can cause an MDE error of over 10 meters.