Public datasets such as KITTI nuScenes and Waymo have played a key role in the research and development of autonomous vehicles and advanced driver assistance systems. However many of these datasets fail to incorporate a full range of driving conditions; some datasets only contain clear-weather conditions underrepresenting or entirely missing colder weather conditions such as snow or autumn scenes with bright colorful foliage. In this paper we present the Michigan State University Four Seasons (MSU-4S) Dataset which contains real-world collections of autonomous vehicle data from varied types of driving scenarios. These scenarios were recorded throughout a full range of seasons and capture clear rainy snowy and fall weather conditions at varying times of day. MSU-4S contains more than 100000 two- and three-dimensional frames for camera lidar and radar data as well as Global Navigation Satellite System (GNSS) wheel speed and steering data all annotated with weather time-of-day and time-of-year. Our data includes cluttered scenes that have large numbers of vehicles and pedestrians; and it also captures industrial scenes busy traffic thoroughfare with traffic lights and numerous signs and scenes with dense foliage. While providing a diverse set of scenes our data incorporate an important feature: virtually every scene and its corresponding lidar camera and radar frames were captured in four different seasons enabling unparalleled object detection analysis and testing of the domain shift problem across weather conditions. In that context we present detailed analyses for 3D and 2D object detection showing a strong domain shift effect among MSU-4S data segments collected across different conditions. MSU-4S will also enable advanced multimodal fusion research including different combinations of camera-lidar-radar fusion which continues to be of strong interest for the computer vision autonomous driving and ADAS development communities. The MSU-4S dataset is available online at https://egr.msu.edu/waves/msu4s.
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Content-aware graphic layout generation aims to automatically arrange visual elements along with a given content such as an e-commerce product image. In this paper we argue that the current layout generation approaches suffer from the limited training data for the high-dimensional layout structure. We show that a simple retrieval augmentation can significantly improve the generation quality. Our model which is named Retrieval-Augmented Layout Transformer (RALF) retrieves nearest neighbor layout examples based on an input image and feeds these results into an autoregressive generator. Our model can apply retrieval augmentation to various controllable generation tasks and yield high-quality layouts within a unified architecture. Our extensive experiments show that RALF successfully generates content-aware layouts in both constrained and unconstrained settings and significantly outperforms the baselines.
This paper presents the first 3D feature tracking method with the corresponding dataset. Our proposed method takes event streams from stereo event cameras as input to predict 3D trajectories of the target features with high-speed motion. To achieve this our method leverages a joint framework to predict the 2D feature motion offsets and the 3D feature spatial position simultaneously. A motion compensation module is leveraged to overcome the feature deformation. A patch matching module based on bi-polarity hypergraph modeling is proposed to robustly estimate the feature spatial position. Meanwhile we collect the first 3D feature tracking dataset with high-speed moving objects and ground truth 3D feature trajectories at 250 FPS named E-3DTrack which can be used as the first high-speed 3D feature tracking benchmark. Our code and dataset could be found at: https://github.com/lisiqi19971013/E-3DTrack.
Existing methods for synthesizing 3D human gestures from speech have shown promising results but they do not explicitly model the impact of emotions on the generated gestures. Instead these methods directly output animations from speech without control over the expressed emotion. To address this limitation we present AMUSE an emotional speech-driven body animation model based on latent diffusion. Our observation is that content (i.e. gestures related to speech rhythm and word utterances) emotion and personal style are separable. To account for this AMUSE maps the driving audio to three disentangled latent vectors: one for content one for emotion and one for personal style. A latent diffusion model trained to generate gesture motion sequences is then conditioned on these latent vectors. Once trained AMUSE synthesizes 3D human gestures directly from speech with control over the expressed emotions and style by combining the content from the driving speech with the emotion and style of another speech sequence. Randomly sampling the noise of the diffusion model further generates variations of the gesture with the same emotional expressivity. Qualitative quantitative and perceptual evaluations demonstrate that AMUSE outputs realistic gesture sequences. Compared to the state of the art the generated gestures are better synchronized with the speech content and better represent the emotion expressed by the input speech. Our code is available at amuse.is.tue.mpg.de.
Interpretable Measures of Conceptual Similarity by Complexity-Constrained Descriptive Auto-Encoding
PDF ↗Quantifying the degree of similarity between images is a key copyright issue for image-based machine learning. In legal doctrine however determining the degree of similarity between works requires subjective analysis and fact-finders (judges and juries) can demonstrate considerable variability in these subjective judgement calls. Images that are structurally similar can be deemed dissimilar whereas images of completely different scenes can be deemed similar enough to support a claim of copying. We seek to define and compute a notion of "conceptual similarity" among images that captures high-level relations even among images that do not share repeated elements or visually similar components. The idea is to use a base multi-modal model to generate "explanations" (captions) of visual data at increasing levels of complexity. Then similarity can be measured by the length of the caption needed to discriminate between the two images: Two highly dissimilar images can be discriminated early in their description whereas conceptually dissimilar ones will need more detail to be distinguished. We operationalize this definition and show that it correlates with subjective (averaged human evaluation) assessment and beats existing baselines on both image-to-image and text-to-text similarity benchmarks. Beyond just providing a number our method also offers interpretability by pointing to the specific level of granularity of the description where the source data is differentiated.
In recent advancements in high-fidelity image generation Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a key player. However their application at high resolutions presents significant computational challenges. Current methods such as patchifying expedite processes in UNet and Transformer architectures but at the expense of representational capacity. Addressing this we introduce the Diffusion State Space Model (DiffuSSM) an architecture that supplants attention mechanisms with a more scalable state space model backbone. This approach effectively handles higher resolutions without resorting to global compression thus preserving detailed image representation throughout the diffusion process. Our focus on FLOP-efficient architectures in diffusion training marks a significant step forward. Comprehensive evaluations on both ImageNet and LSUN datasets at two resolutions demonstrate that DiffuSSMs are on par or even outperform existing diffusion models with attention modules in FID and Inception Score metrics while significantly reducing total FLOP usage.
Open-world detection poses significant challenges as it requires the detection of any object using either object class labels or free-form texts. Existing related works often use large-scale manual annotated caption datasets for training which are extremely expensive to collect. Instead we propose to transfer knowledge from vision-language models (VLMs) to enrich the open-vocabulary descriptions automatically. Specifically we bootstrap dense synthetic captions using pre-trained VLMs to provide rich descriptions on different regions in images and incorporate these captions to train a novel detector that generalizes to novel concepts. To mitigate the noise caused by hallucination in synthetic captions we also propose a novel hyperbolic vision-language learning approach to impose a hierarchy between visual and caption embeddings. We call our detector "HyperLearner". We conduct extensive experiments on a wide variety of open-world detection benchmarks (COCO LVIS Object Detection in the Wild RefCOCO) and our results show that our model consistently outperforms existing state-of-the-art methods such as GLIP GLIPv2 and Grounding DINO when using the same backbone.
3D asset generation is getting massive amounts of attention inspired by the recent success on text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-image diffusion models in an optimization problem or fine-tune them on synthetic data which often results in non-photorealistic 3D objects without backgrounds. In this paper we present a method that leverages pretrained text-to-image models as a prior and learn to generate multi-view images in a single denoising process from real-world data. Concretely we propose to integrate 3D volume-rendering and cross-frame-attention layers into each block of the existing U-Net network of the text-to-image model. Moreover we design an autoregressive generation that renders more 3D-consistent images at any viewpoint. We train our model on real-world datasets of objects and showcase its capabilities to generate instances with a variety of high-quality shapes and textures in authentic surroundings. Compared to the existing methods the results generated by our method are consistent and have favorable visual quality (-30% FID -37% KID).
Systematic Comparison of Semi-supervised and Self-supervised Learning for Medical Image Classification
PDF ↗In typical medical image classification problems labeled data is scarce while unlabeled data is more available. Semi-supervised learning and self-supervised learning are two different research directions that can improve accuracy by learning from extra unlabeled data. Recent methods from both directions have reported significant gains on traditional benchmarks. Yet past benchmarks do not focus on medical tasks and rarely compare self- and semi- methods together on an equal footing. Furthermore past benchmarks often handle hyperparameter tuning suboptimally. First they may not tune hyperparameters at all leading to underfitting. Second when tuning does occur it often unrealistically uses a labeled validation set that is much larger than the training set. Therefore currently published rankings might not always corroborate with their practical utility This study contributes a systematic evaluation of self- and semi- methods with a unified experimental protocol intended to guide a practitioner with scarce overall labeled data and a limited compute budget. We answer two key questions: Can hyperparameter tuning be effective with realistic-sized validation sets? If so when all methods are tuned well which self- or semi-supervised methods achieve the best accuracy? Our study compares 13 representative semi- and self-supervised methods to strong labeled-set-only baselines on 4 medical datasets. From 20000+ GPU hours of computation we provide valuable best practices to resource-constrained practitioners: hyperparameter tuning is effective and the semi-supervised method known as MixMatch delivers the most reliable gains across 4 datasets.
Video Motion Magnification (VMM) aims to reveal subtle and imperceptible motion information of objects in the macroscopic world. Prior methods directly model the motion field from the Eulerian perspective by Representation Learning that separates shape and texture or Multi-domain Learning from phase fluctuations. Inspired by the frequency spectrum we observe that the low-frequency components with stable energy always possess spatial structure and less noise making them suitable for modeling the subtle motion field. To this end we present FD4MM a new paradigm of Frequency Decoupling for Motion Magnification with a Multi-level Isomorphic Architecture to capture multi-level high-frequency details and a stable low-frequency structure (motion field) in video space. Since high-frequency details and subtle motions are susceptible to information degradation due to their inherent subtlety and unavoidable external interference from noise we carefully design Sparse High/Low-pass Filters to enhance the integrity of details and motion structures and a Sparse Frequency Mixer to promote seamless recoupling. Besides we innovatively design a contrastive regularization for this task to strengthen the model's ability to discriminate irrelevant features reducing undesired motion magnification. Extensive experiments on both Real-world and Synthetic Datasets show that our FD4MM outperforms SOTA methods. Meanwhile FD4MM reduces FLOPs by 1.63xand boosts inference speed by 1.68xthan the latest method. Our code is available at https://github.com/Jiafei127/FD4MM.
While super-resolution (SR) methods based on diffusion models exhibit promising results their practical application is hindered by the substantial number of required inference steps. Recent methods utilize the degraded images in the initial state thereby shortening the Markov chain. Nevertheless these solutions either rely on a precise formulation of the degradation process or still necessitate a relatively lengthy generation path (e.g. 15 iterations). To enhance inference speed we propose a simple yet effective method for achieving single-step SR generation named SinSR. Specifically we first derive a deterministic sampling process from the most recent state-of-the-art (SOTA) method for accelerating diffusion-based SR. This allows the mapping between the input random noise and the generated high-resolution image to be obtained in a reduced and acceptable number of inference steps during training. We show that this deterministic mapping can be distilled into a student model that performs SR within only one inference step. Additionally we propose a novel consistency-preserving loss to simultaneously leverage the ground-truth image during the distillation process ensuring that the performance of the student model is not solely bound by the feature manifold of the teacher model resulting in further performance improvement. Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model in just one sampling step resulting in a remarkable up to x10 speedup for inference. Our code will be released at https://github.com/wyf0912/SinSR/.
SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction
PDF ↗Vision-based perception for autonomous driving requires an explicit modeling of a 3D space where 2D latent representations are mapped and subsequent 3D operators are applied. However operating on dense latent spaces introduces a cubic time and space complexity which limits scalability in terms of perception range or spatial resolution. Existing approaches compress the dense representation using projections like Bird's Eye View (BEV) or Tri-Perspective View (TPV). Although efficient these projections result in information loss especially for tasks like semantic occupancy prediction. To address this we propose SparseOcc an efficient occupancy network inspired by sparse point cloud processing. It utilizes a lossless sparse latent representation with three key innovations. Firstly a 3D sparse diffuser performs latent completion using spatially decomposed 3D sparse convolutional kernels. Secondly a feature pyramid and sparse interpolation enhance scales with information from others. Finally the transformer head is redesigned as a sparse variant. SparseOcc achieves a remarkable 74.9% reduction on FLOPs over the dense baseline. Interestingly it also improves accuracy from 12.8% to 14.1% mIOU which in part can be attributed to the sparse representation's ability to avoid hallucinations on empty voxels.
Weakly supervised video anomaly detection (WSVAD) is a challenging task. Generating fine-grained pseudo-labels based on weak-label and then self-training a classifier is currently a promising solution. However since the existing methods use only RGB visual modality and the utilization of category text information is neglected thus limiting the generation of more accurate pseudo-labels and affecting the performance of self-training. Inspired by the manual labeling process based on the event description in this paper we propose a novel pseudo-label generation and self-training framework based on Text Prompt with Normality Guidance (TPWNG) for WSVAD. Our idea is to transfer the rich language-visual knowledge of the contrastive language-image pre-training (CLIP) model for aligning the video event description text and corresponding video frames to generate pseudo-labels. Specifically We first fine-tune the CLIP for domain adaptation by designing two ranking losses and a distributional inconsistency loss. Further we propose a learnable text prompt mechanism with the assist of a normality visual prompt to further improve the matching accuracy of video event description text and video frames. Then we design a pseudo-label generation module based on the normality guidance to infer reliable frame-level pseudo-labels. Finally we introduce a temporal context self-adaptive learning module to learn the temporal dependencies of different video events more flexibly and accurately. Extensive experiments show that our method achieves state-of-the-art performance on two benchmark datasets UCF-Crime and XD-Violence demonstrating the effectiveness of our proposed method.
In this paper we uncover the untapped potential of diffusion U-Net which serves as a "free lunch" that substantially improves the generation quality on the fly. We initially investigate the key contributions of the U-Net architecture to the denoising process and identify that its main backbone primarily contributes to denoising whereas its skip connections mainly introduce high-frequency features into the decoder module causing the potential neglect of crucial functions intrinsic to the backbone network. Capitalizing on this discovery we propose a simple yet effective method termed "FreeU" which enhances generation quality without additional training or finetuning. Our key insight is to strategically re-weight the contributions sourced from the U-Net's skip connections and backbone feature maps to leverage the strengths of both components of the U-Net architecture. Promising results on image and video generation tasks demonstrate that our FreeU can be readily integrated to existing diffusion models e.g. Stable Diffusion DreamBooth and ControlNet to improve the generation quality with only a few lines of code. All you need is to adjust two scaling factors during inference.
3D Morphable Models (3DMMs) provide promising 3D face reconstructions in various applications. However existing methods struggle to reconstruct faces with extreme expressions due to deficiencies in supervisory signals such as sparse or inaccurate landmarks. Segmentation information contains effective geometric contexts for face reconstruction. Certain attempts intuitively depend on differentiable renderers to compare the rendered silhouettes of reconstruction with segmentation which is prone to issues like local optima and gradient instability. In this paper we fully utilize the facial part segmentation geometry by introducing Part Re-projection Distance Loss (PRDL). Specifically PRDL transforms facial part segmentation into 2D points and re-projects the reconstruction onto the image plane. Subsequently by introducing grid anchors and computing different statistical distances from these anchors to the point sets PRDL establishes geometry descriptors to optimize the distribution of the point sets for face reconstruction. PRDL exhibits a clear gradient compared to the renderer-based methods and presents state-of-the-art reconstruction performance in extensive quantitative and qualitative experiments. Our project is available at https://github.com/wang-zidu/3DDFA-V3.
This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations. We devise an advanced reflection collection pipeline that is highly adaptable to a wide range of real-world reflection scenarios and incurs reduced costs in collecting large-scale aligned reflection pairs. In the process we develop a large-scale high-quality reflection dataset named Reflection Removal in the Wild (RRW). RRW contains over 14950 high-resolution real-world reflection pairs a dataset forty-five times larger than its predecessors. Regarding perception of reflection locations we identify that numerous virtual reflection objects visible in reflection images are not present in the corresponding ground-truth images. This observation drawn from the aligned pairs leads us to conceive the Maximum Reflection Filter (MaxRF). The MaxRF could accurately and explicitly characterize reflection locations from pairs of images. Building upon this we design a reflection location-aware cascaded framework specifically tailored for SIRR. Powered by these innovative techniques our solution achieves superior performance than current leading methods across multiple real-world benchmarks. Codes and datasets are available at \href https://github.com/zhuyr97/Reflection_RemoVal_CVPR2024 \color blue here .
Audio-visual saliency prediction can draw support from diverse modality complements but further performance enhancement is still challenged by customized architectures as well as task-specific loss functions. In recent studies denoising diffusion models have shown more promising in unifying task frameworks owing to their inherent ability of generalization. Following this motivation a novel Diffusion architecture for generalized audio-visual Saliency prediction (DiffSal) is proposed in this work which formulates the prediction problem as a conditional generative task of the saliency map by utilizing input audio and video as the conditions. Based on the spatio-temporal audio-visual features an extra network Saliency-UNet is designed to perform multi-modal attention modulation for progressive refinement of the ground-truth saliency map from the noisy map. Extensive experiments demonstrate that the proposed DiffSal can achieve excellent performance across six challenging audio-visual benchmarks with an average relative improvement of 6.3% over the previous state-of-the-art results by six metrics.
Multiple instance learning (MIL) is the most widely used framework in computational pathology encompassing sub-typing diagnosis prognosis and more. However the existing MIL paradigm typically requires an offline instance feature extractor such as a pre-trained ResNet or a foundation model. This approach lacks the capability for feature fine-tuning within the specific downstream tasks limiting its adaptability and performance. To address this issue we propose a Re-embedded Regional Transformer (RRT) for re-embedding the instance features online which captures fine-grained local features and establishes connections across different regions. Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator RRT is tailored to re-embed instance features online. It serves as a portable module that can seamlessly integrate into mainstream MIL models. Extensive experimental results on common computational pathology tasks validate that: 1) feature re-embedding improves the performance of MIL models based on ResNet-50 features to the level of foundation model features and further enhances the performance of foundation model features; 2) the RRT can introduce more significant performance improvements to various MIL models; 3) RRT-MIL as an RRT-enhanced AB-MIL outperforms other latest methods by a large margin. The code is available at: https://github.com/DearCaat/RRT-MIL.
Recent methods for label-free 3D semantic segmentation aim to assist 3D model training by leveraging the open-world recognition ability of pre-trained vision language models. However these methods usually suffer from inconsistent and noisy pseudo-labels provided by the vision language models. To address this issue we present a hierarchical intra-modal correlation learning framework that captures visual and geometric correlations in 3D scenes at three levels: intra-set intra-scene and inter-scene to help learn more compact 3D representations. We refine pseudo-labels using intra-set correlations within each geometric consistency set and align features of visually and geometrically similar points using intra-scene and inter-scene correlation learning. We also introduce a feedback mechanism to distill the correlation learning capability into the 3D model. Experiments on both indoor and outdoor datasets show the superiority of our method. We achieve a state-of-the-art 36.6% mIoU on the ScanNet dataset and a 23.0% mIoU on the nuScenes dataset with improvements of 7.8% mIoU and 2.2% mIoU compared with previous SOTA. We also provide theoretical analysis and qualitative visualization results to discuss the mechanism and conduct thorough ablation studies to support the effectiveness of our framework.
Remarkable strides have been made in reconstructing static scenes or human bodies from monocular videos. Yet the two problems have largely been approached independently without much synergy. Most visual SLAM methods can only reconstruct camera trajectories and scene structures up to scale while most HMR methods reconstruct human meshes in metric scale but fall short in reasoning with cameras and scenes. This work introduces Synergistic Camera and Human Reconstruction (SynCHMR) to marry the best of both worlds. Specifically we design Human-aware Metric SLAM to reconstruct metric-scale camera poses and scene point clouds using camera-frame HMR as a strong prior addressing depth scale and dynamic ambiguities. Conditioning on the dense scene recovered we further learn a Scene-aware SMPL Denoiser to enhance world-frame HMR by incorporating spatiotemporal coherency and dynamic scene constraints. Together they lead to consistent reconstructions of camera trajectories human meshes and dense scene point clouds in a common world frame.