CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Recent years have witnessed rapid progress in monocular human mesh recovery. Despite their impressive performance on public benchmarks existing methods are vulnerable to unusual poses which prevents them from deploying to challenging scenarios such as dance and martial arts. This issue is mainly attributed to the domain gap induced by the data scarcity in relevant cases. Most existing datasets are captured in constrained scenarios and lack samples of such complex movements. For this reason we propose a data collection pipeline comprising automatic crawling precise annotation and hardcase mining. Based on this pipeline we establish a large dataset in a short time. The dataset named HardMo contains 7M images along with precise annotations covering 15 categories of dance and 14 categories of martial arts. Empirically we find that the prediction failure in dance and martial arts is mainly characterized by the misalignment of hand-wrist and foot-ankle. To dig deeper into the two hardcases we leverage the proposed automatic pipeline to filter collected data and construct two subsets named HardMo-Hand and HardMo-Foot. Extensive experiments demonstrate the effectiveness of the annotation pipeline and the data-driven solution to failure cases. Specifically after being trained on HardMo HMR an early pioneering method can even outperform the current state of the art 4DHumans on our benchmarks.
As a problem often encountered in real-world scenarios multi-view multi-label learning has attracted considerable research attention. However due to oversights in data collection and uncertainties in manual annotation real-world data often suffer from incompleteness. Regrettably most existing multi-view multi-label learning methods sidestep missing views and labels. Furthermore they often neglect the potential of harnessing complementary information between views and labels thus constraining their classification capabilities. To address these challenges we propose a view-category interactive sharing transformer tailored for incomplete multi-view multi-label learning. Within this network we incorporate a two-layer transformer module to characterize the interplay between views and labels. Additionally to address view incompleteness a KNN-style missing view generation module is employed. Finally we introduce a view-category consistency guided embedding enhancement module to align different views and improve the discriminating power of the embeddings. Collectively these modules synergistically integrate to classify the incomplete multi-view multi-label data effectively. Extensive experiments substantiate that our approach outperforms the existing state-of-the-art methods.
Humans can infer 3D structure from 2D images of an object based on past experience and improve their 3D understanding as they see more images. Inspired by this behavior we introduce SAP3D a system for 3D reconstruction and novel view synthesis from an arbitrary number of unposed images. Given a few unposed images of an object we adapt a pre-trained view-conditioned diffusion model together with the camera poses of the images via test-time fine-tuning. The adapted diffusion model and the obtained camera poses are then utilized as instance-specific priors for 3D reconstruction and novel view synthesis. We show that as the number of input images increases the performance of our approach improves bridging the gap between optimization-based prior-less 3D reconstruction methods and single-image-to-3D diffusion-based methods. We demonstrate our system on real images as well as standard synthetic benchmarks. Our ablation studies confirm that this adaption behavior is key for more accurate 3D understanding.
Sparse LiDAR point clouds cause severe loss of detail of static structures and reduce the density of static points available for navigation. Reduced density can be detrimental to navigation under several scenarios. We observe that despite high sparsity in most cases the global topology of LiDAR outlining the static structures can be inferred. We utilize this property to obtain a backbone skeleton of a LiDAR scan in the form of a single connected component that is a proxy to its global topology. We utilize the backbone to augment new points along static structures to overcome sparsity. Newly introduced points could correspond to existing static structures or to static points that were earlier obstructed by dynamic objects. To the best of our knowledge we are the first to use such a strategy for sparse LiDAR point clouds. Existing solutions close to our approach fail to identify and preserve the global static LiDAR topology and generate sub-optimal points. We propose GLiDR a Graph Generative network that is topologically regularized using 0-dimensional Persistent Homology (PH) constraints. This enables GLiDR to introduce newer static points along a topologically consistent global static LiDAR backbone.
We propose GLiDR a Graph Generative network that is topologically regularized using 0-dimensional Persistent Homology (PH) constraints. This enables GLiDR to introduce newer static points along a topologically consistent global static LiDAR backbone. GLiDR generates precise static points using 32x sparser dynamic scans and performs better than the baselines across three datasets. GLiDR generates a valuable byproduct - an accurate binary segmentation mask of static and dynamic objects that are helpful for navigation planning and safety in constrained environments. The newly introduced static points allow GLiDR to outperform LiDAR-based navigation using SLAM in several settings.
Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks without dense annotations. However attributed to the frequent coupling of co-occurring objects and the limited supervision from image-level labels the challenging co-occurrence problem is widely present and leads to false activation of objects in WSSS. In this work we devise a 'Separate and Conquer' scheme SeCo to tackle this issue from dimensions of image space and feature space. In the image space we propose to 'separate' the co-occurring objects with image decomposition by subdividing images into patches. Importantly we assign each patch a category tag from Class Activation Maps (CAMs) which spatially helps remove the co-context bias and guide the subsequent representation. In the feature space we propose to 'conquer' the false activation by enhancing semantic representation with multi-granularity knowledge contrast. To this end a dual-teacher-single-student architecture is designed and tag-guided contrast is conducted which guarantee the correctness of knowledge and further facilitate the discrepancy among co-contexts. We streamline the multi-staged WSSS pipeline end-to-end and tackle this issue without external supervision.
To this end a dual-teacher-single-student architecture is designed and tag-guided contrast is conducted which guarantee the correctness of knowledge and further facilitate the discrepancy among co-contexts. We streamline the multi-staged WSSS pipeline end-to-end and tackle this issue without external supervision. Extensive experiments are conducted validating the efficiency of our method and the superiority over previous single-staged and even multi-staged competitors on PASCAL VOC and MS COCO. Code is available at https://github.com/zwyang6/SeCo.git.
Quantized neural networks employ reduced precision representations for both weights and activations. This quantization process significantly reduces the memory requirements and computational complexity of the network. Binary Neural Networks (BNNs) are the extreme quantization case representing values with just one bit. Since the sign function is typically used to map real values to binary values smooth approximations are introduced to mimic the gradients during error backpropagation. Thus the mismatch between the forward and backward models corrupts the direction of the gradient causing training inconsistency problems and performance degradation. In contrast to current BNN approaches we propose to employ a binary periodic (BiPer) function during binarization. Specifically we use a square wave for the forward pass to obtain the binary values and employ the trigonometric sine function with the same period of the square wave as a differentiable surrogate during the backward pass. We demonstrate that this approach can control the quantization error by using the frequency of the periodic function and improves network performance. Extensive experiments validate the effectiveness of BiPer in benchmark datasets and network architectures with improvements of up to 1% and 0.69% with respect to state-of-the-art methods in the classification task over CIFAR-10 and ImageNet respectively.
Extensive experiments validate the effectiveness of BiPer in benchmark datasets and network architectures with improvements of up to 1% and 0.69% with respect to state-of-the-art methods in the classification task over CIFAR-10 and ImageNet respectively. Our code is publicly available at https://github.com/edmav4/BiPer.
Automatic and interactive matting largely improve image matting by respectively alleviating the need for auxiliary input and enabling object selection. Due to different settings on whether prompts exist they either suffer from weakness in instance completeness or region details. Also when dealing with different scenarios directly switching between the two matting models introduces inconvenience and higher workload. Therefore we wonder whether we can alleviate the limitations of both settings while achieving unification to facilitate more convenient use. Our key idea is to offer saliency guidance for automatic mode to enable its attention to detailed regions and also refine the instance completeness in interactive mode by replacing the binary mask guidance with a more probabilistic form. With different guidance for each mode we can achieve unification through adaptable guidance defined as saliency information in automatic mode and user cue for interactive one. It is instantiated as candidate feature in our method an automatic switch for class token in pretrained ViTs and average feature of user prompts controlled by the existence of user prompts. Then we use the candidate feature to generate a probabilistic similarity map as the guidance to alleviate the over-reliance on binary mask. Extensive experiments show that our method can adapt well to both automatic and interactive scenarios with more light-weight framework. Code available at https://github.com/coconuthust/SmartMatting.
In this paper we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data with the overarching objective of attaining robust and universal object segmentation within the event-centric domain. One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery. Capitalizing on the vast repositories of datasets with paired events and RGB images our proposition is to harness and extrapolate the profound knowledge encapsulated within the pre-trained SAM framework. As a cornerstone to achieving this we introduce a multi-scale feature distillation methodology. This methodology rigorously optimizes the alignment of token embeddings originating from event data with their RGB image counterparts thereby preserving and enhancing the robustness of the overall architecture. Considering the distinct significance that token embeddings from intermediate layers hold for higher-level embeddings our strategy is centered on accurately calibrating the pivotal token embeddings. This targeted calibration is aimed at effectively managing the discrepancies in high-level embeddings originating from both the event and image domains. Extensive experiments on different datasets demonstrate the effectiveness of the proposed distillation method. Code in https://github.com/happychenpipi/EventSAM.
This work presents AnyDoor a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations with desired shapes. Instead of tuning parameters for each object our model is trained only once and effortlessly generalizes to diverse object-scene combinations at the inference stage. Such a challenging zero-shot setting requires an adequate characterization of a certain object. To this end we complement the commonly used identity feature with detail features which are carefully designed to maintain appearance details yet allow versatile local variations(e.g. lighting orientation posture etc.) supporting the object in favorably blending with different surroundings. We further propose to borrow knowledge from video datasets where we can observe various forms (i.e. along the time axis) of a single object leading to stronger model generalizability and robustness. Extensive experiments demonstrate the superiority of our approach over existing alternatives as well as its great potential in real-world applications such as virtual try-on shape editing and object swapping.
The prevalent approaches of unsupervised 3D object detection follow cluster-based pseudo-label generation and iterative self-training processes. However the challenge arises due to the sparsity of LiDAR scans which leads to pseudo-labels with erroneous size and position resulting in subpar detection performance. To tackle this problem this paper introduces a Commonsense Prototype-based Detector termed CPD for unsupervised 3D object detection. CPD first constructs Commonsense Prototype (CProto) characterized by high-quality bounding box and dense points based on commonsense intuition. Subsequently CPD refines the low-quality pseudo-labels by leveraging the size prior from CProto. Furthermore CPD enhances the detection accuracy of sparsely scanned objects by the geometric knowledge from CProto. CPD outperforms state-of-the-art unsupervised 3D detectors on Waymo Open Dataset (WOD) PandaSet and KITTI datasets by a large margin. Besides by training CPD on WOD and testing on KITTI CPD attains 90.85% and 81.01% 3D Average Precision on easy and moderate car classes respectively.
Besides by training CPD on WOD and testing on KITTI CPD attains 90.85% and 81.01% 3D Average Precision on easy and moderate car classes respectively. These achievements position CPD in close proximity to fully supervised detectors highlighting the significance of our method. The code will be available at https://github.com/hailanyi/CPD.
Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. At each navigation step the agent selects from possible candidate locations and then makes the move. For better navigation planning the lookahead exploration strategy aims to effectively evaluate the agent's next action by accurately anticipating the future environment of candidate locations. To this end some existing works predict RGB images for future environments while this strategy suffers from image distortion and high computational cost. To address these issues we propose the pre-trained hierarchical neural radiance representation model (HNR) to produce multi-level semantic features for future environments which are more robust and efficient than pixel-wise RGB reconstruction. Furthermore with the predicted future environmental representations our lookahead VLN model is able to construct the navigable future path tree and select the optimal path via efficient parallel evaluation. Extensive experiments on the VLN-CE datasets confirm the effectiveness of our method.
Prominent solutions for medical image segmentation are typically tailored for automatic or interactive setups posing challenges in facilitating progress achieved in one task to another. This also necessitates separate models for each task duplicating both training time and parameters. To address above issues we introduce S2VNet a universal framework that leverages Slice-to-Volume propagation to unify automatic/interactive segmentation within a single model and one training session. Inspired by clustering-based segmentation techniques S2VNet makes full use of the slice-wise structure of volumetric data by initializing cluster centers from the cluster results of previous slice. This enables knowledge acquired from prior slices to assist in the segmentation of the current slice further efficiently bridging the communication between remote slices using mere 2D networks. Moreover such a framework readily accommodates inter- active segmentation with no architectural change simply by initializing centroids from user inputs. S2VNet distinguishes itself by swift inference speeds and reduced memory consumption compared to prevailing 3D solutions. It can also handle multi-class interactions with each of them serving to initialize different centroids. Experiments on three benchmarks demonstrate S2VNet surpasses task-specified solutions on both automatic/interactive setups.
Vision-language pre-trained models have achieved impressive performance on various downstream tasks. However their large model sizes hinder their utilization on platforms with limited computational resources. We find that directly using smaller pre-trained models and applying magnitude-based pruning on CLIP models leads to inflexibility and inferior performance. Recent efforts for VLP compression either adopt uni-modal compression metrics resulting in limited performance or involve costly mask-search processes with learnable masks. In this paper we first propose the Module-wise Pruning Error (MoPE) metric accurately assessing CLIP module importance by performance decline on cross-modal tasks. Using the MoPE metric we introduce a unified pruning framework applicable to both pre-training and task-specific fine-tuning compression stages. For pre-training MoPE-CLIP effectively leverages knowledge from the teacher model significantly reducing pre-training costs while maintaining strong zero-shot capabilities. For fine-tuning consecutive pruning from width to depth yields highly competitive task-specific models. Extensive experiments in two stages demonstrate the effectiveness of the MoPE metric and MoPE-CLIP outperforms previous state-of-the-art VLP compression methods.
We introduce SynCLR a novel approach for learning visual representations exclusively from synthetic images without any real data. We synthesize a large dataset of image captions using LLMs then use an off-the-shelf text-to-image model to generate multiple images corresponding to each synthetic caption. We perform visual representation learning on these synthetic images via contrastive learning treating images sharing the same caption as positive pairs. The resulting representations demonstrate remarkable transferability competing favorably with other general-purpose visual representation learners such as CLIP and DINO v2 in image classification tasks. Furthermore in dense prediction tasks such as semantic segmentation SynCLR outperforms previous self-supervised methods by a significant margin e.g. improving over MAE and iBOT by 5.0 and 3.1 mIoU on ADE20k for ViT-B/16.
Unprocessed RAW video has shown distinct advantages over sRGB video in video editing and computer vision tasks. However capturing RAW video is challenging due to limitations in bandwidth and storage. Various methods have been proposed to address similar issues in single image RAW capture through de-rendering. These methods utilize both the metadata and the sRGB image to perform sRGB-to-RAW de-rendering and recover high-quality single-frame RAW data. However metadata-based methods always require additional computation for online metadata generation imposing severe burden on mobile camera device for high frame rate RAW video capture. To address this issue we propose a framework that utilizes frame affinity to achieve high-quality sRGB-to-RAW video reconstruction. Our approach consists of two main steps. The first step temporal affinity prior extraction uses motion information between adjacent frames to obtain a reference RAW image. The second step spatial feature fusion and mapping learns a pixel-level mapping function using scene-specific and position-specific features provided by the previous frame. Our method can be easily applied to current mobile camera equipment without complicated adaptations or added burden. To demonstrate the effectiveness of our approach we introduce the first RAW Video De-rendering Benchmark. In this benchmark our method outperforms state-of-the-art RAW image reconstruction methods even without image-level metadata.
Vision Transformer (ViT) has shown high potential in video recognition owing to its flexible design adaptable self-attention mechanisms and the efficacy of masked pre-training. Yet it remains unclear how to adapt these pre-trained short-term ViTs for temporal action detection (TAD) in untrimmed videos. The existing works treat them as off-the-shelf feature extractors for each short-trimmed snippet without capturing the fine-grained relation among different snippets in a broader temporal context. To mitigate this issue this paper focuses on designing a new mechanism for adapting these pre-trained ViT models as a unified long-form video transformer to fully unleash its modeling power in capturing inter-snippet relation while still keeping low computation overhead and memory consumption for efficient TAD. To this end we design effective cross-snippet propagation modules to gradually exchange short-term video information among different snippets from two levels. For inner-backbone information propagation we introduce a cross-snippet propagation strategy to enable multi-snippet temporal feature interaction inside the backbone. For post-backbone information propagation we propose temporal transformer layers for further clip-level modeling.
For inner-backbone information propagation we introduce a cross-snippet propagation strategy to enable multi-snippet temporal feature interaction inside the backbone. For post-backbone information propagation we propose temporal transformer layers for further clip-level modeling. With the plain ViT-B pre-trained with VideoMAE our end-to-end temporal action detector (ViT-TAD) yields a very competitive performance to previous temporal action detectors riching up to 69.5 average mAP on THUMOS14 37.40 average mAP on ActivityNet-1.3 and 17.20 average mAP on FineAction.
Large-scale black-box models have become ubiquitous across numerous applications. Understanding the influence of individual training data sources on predictions made by these models is crucial for improving their trustworthiness. Current influence estimation techniques involve computing gradients for every training point or repeated training on different subsets. These approaches face obvious computational challenges when scaled up to large datasets and models. In this paper we introduce and explore the Mirrored Influence Hypothesis highlighting a reciprocal nature of influence between training and test data. Specifically it suggests that evaluating the influence of training data on test predictions can be reformulated as an equivalent yet inverse problem: assessing how the predictions for training samples would be altered if the model were trained on specific test samples. Through both empirical and theoretical validations we demonstrate the wide applicability of our hypothesis. Inspired by this we introduce a new method for estimating the influence of training data which requires calculating gradients for specific test samples paired with a forward pass for each training point. This approach can capitalize on the common asymmetry in scenarios where the number of test samples under concurrent examination is much smaller than the scale of the training dataset thus gaining a significant improvement in efficiency compared to existing approaches.