CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
We address the problem of regressing 3D human pose and shape from a single image with a focus on 3D accuracy. The current best methods leverage large datasets of 3D pseudo-ground-truth (p-GT) and 2D keypoints leading to robust performance. With such methods however we observe a paradoxical decline in 3D pose accuracy with increasing 2D accuracy. This is caused by biases in the p-GT and the use of an approximate camera projection model. We quantify the error induced by current camera models and show that fitting 2D keypoints and p-GT accurately causes incorrect 3D poses. Our analysis defines the invalid distances within which minimizing 2D and p-GT losses is detrimental. We use this to formulate a new loss "Threshold-Adaptive Loss Scaling" (TALS) that penalizes gross 2D and p-GT errors but not smaller ones. With such a loss there are many 3D poses that could equally explain the 2D evidence. To reduce this ambiguity we need a prior over valid human poses but such priors can introduce unwanted bias.
With such a loss there are many 3D poses that could equally explain the 2D evidence. To reduce this ambiguity we need a prior over valid human poses but such priors can introduce unwanted bias. To address this we exploit a tokenized representation of human pose and reformulate the problem as token prediction. This restricts the estimated poses to the space of valid poses effectively improving robustness to occlusion. Extensive experiments on the EMDB and 3DPW datasets show that our reformulated loss and tokenization allows us to train on in-the-wild data while improving 3D accuracy over the state-of-the-art. Our models and code are available for research at https://tokenhmr.is.tue.mpg.de.
This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage modular reasoning framework. Previous modular methods have shown promise with a single planning stage ungrounded in visual content. However through a simple and effective baseline we find that such systems can lead to brittle behavior in practice for challenging videoQA settings. Thus unlike traditional single-stage planning methods we propose a multi-stage system consisting of an event parser a grounding stage and a final reasoning stage in conjunction with an external memory. All stages are training-free and performed using few-shot prompting of large models creating interpretable intermediate outputs at each stage. By decomposing the underlying planning and task complexity our method MoReVQA improves over prior work on standard videoQA benchmarks (NExT-QA iVQA EgoSchema and ActivityNet-QA) with state-of-the-art results and extensions to related tasks (grounded videoQA paragraph captioning).
Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently there is a lack of focus on guiding this delicate trade-off. In this study we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices providing insights into the tuning dynamics of existing methods. Building upon this understanding we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://github.com/zstarN70/RLRR.git.
We propose FaceCom a method for 3D facial shape completion which delivers high-fidelity results for incomplete facial inputs of arbitrary forms. Unlike end-to-end shape completion methods based on point clouds or voxels our approach relies on a mesh-based generative network that is easy to optimize enabling it to handle shape completion for irregular facial scans. We first train a shape generator on a mixed 3D facial dataset containing 2405 identities. Based on the incomplete facial input we fit complete faces using an optimization approach under image inpainting guidance. The completion results are refined through a post-processing step. FaceCom demonstrates the ability to effectively and naturally complete facial scan data with varying missing regions and degrees of missing areas. Our method can be used in medical prosthetic fabrication and the registration of deficient scanning data. Our experimental results demonstrate that FaceCom achieves exceptional performance in fitting and shape completion tasks.
Lifelong person re-identification (LReID) suffers from the catastrophic forgetting problem when learning from non-stationary data. Existing exemplar-based and knowledge distillation-based LReID methods encounter data privacy and limited acquisition capacity respectively. In this paper we instead introduce the prototype which is under-investigated in LReID to better balance knowledge forgetting and acquisition. Existing prototype-based works primarily focus on the classification task where the prototypes are set as discrete points or statistical distributions. However they either discard the distribution information or omit instance-level diversity which are crucial fine-grained clues for LReID. To address the above problems we propose Distribution-aware Knowledge Prototyping (DKP) where the instance-level diversity of each sample is modeled to transfer comprehensive fine-grained knowledge for prototyping and facilitating LReID learning. Specifically an Instance-level Distribution Modeling network is proposed to capture the local diversity of each instance. Then the Distribution-oriented Prototype Generation algorithm transforms the instance-level diversity into identity-level distributions as prototypes which is further explored by the designed Prototype-based Knowledge Transfer module to enhance the knowledge anti-forgetting and acquisition capacity of the LReID model.
Then the Distribution-oriented Prototype Generation algorithm transforms the instance-level diversity into identity-level distributions as prototypes which is further explored by the designed Prototype-based Knowledge Transfer module to enhance the knowledge anti-forgetting and acquisition capacity of the LReID model. Extensive experiments verify that our method achieves superior plasticity and stability balancing and outperforms existing LReID methods by 8.1%/9.1% average mAP/R@1 improvement. The code is available at https://github.com/zhoujiahuan1991/CVPR2024-DKP
We present a lightweight solution for estimating spatially-coherent indoor lighting from a single RGB image. Previous methods for estimating illumination using volumetric representations have overlooked the sparse distribution of light sources in space necessitating substantial memory and computational resources for achieving high-quality results. We introduce a unified voxel octree-based illumination estimation framework to produce 3D spatially-coherent lighting. Additionally a differentiable voxel octree cone tracing rendering layer is proposed to eliminate regular volumetric representation throughout the entire process and ensure the retention of features across different frequency domains. This reduction significantly decreases spatial usage and required floating-point operations without substantially compromising precision. Experimental results demonstrate that our approach achieves high-quality coherent estimation with minimal cost compared to previous methods.
The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations. Training such models with a discriminative objective function has proven successful but requires good positive and negative samples. However the free-form nature and the open vocabulary of object descriptions make the space of negatives extremely large. Prior works randomly sample negatives or use rule-based techniques to build them. In contrast we propose to leverage the vast knowledge built into modern generative models to automatically build negatives that are more relevant to the original data. Specifically we use large-language-models to generate negative text descriptions and text-to-image diffusion models to also generate corresponding negative images. Our experimental analysis confirms the relevance of the generated negative data and its use in language-based detectors improves performance on two complex benchmarks. Code is available at https://github.com/xiaofeng94/Gen-Enhanced-Negs.
In precision agriculture the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield. The current machine vision model requires a large volume of data to achieve high performance. However there are approximately 5.5 million different insect species in the world. None of the existing insect datasets can cover even a fraction of them due to varying geographic locations and acquisition costs. In this paper we introduce a novel "Insect-1M" dataset a game-changing resource poised to revolutionize insect-related foundation model training. Covering a vast spectrum of insect species our dataset including 1 million images with dense identification labels of taxonomy hierarchy and insect descriptions offers a panoramic view of entomology enabling foundation models to comprehend visual and semantic information about insects like never before. Then to efficiently establish an Insect Foundation Model we develop a micro-feature self-supervised learning method with a Patch-wise Relevant Attention mechanism capable of discerning the subtle differences among insect images. In addition we introduce Description Consistency loss to improve micro-feature modeling via insect descriptions. Through our experiments we illustrate the effectiveness of our proposed approach in insect modeling and achieve State-of-the-Art performance on standard benchmarks of insect-related tasks.
In addition we introduce Description Consistency loss to improve micro-feature modeling via insect descriptions. Through our experiments we illustrate the effectiveness of our proposed approach in insect modeling and achieve State-of-the-Art performance on standard benchmarks of insect-related tasks. Our Insect Foundation Model and Dataset promise to empower the next generation of insect-related vision models bringing them closer to the ultimate goal of precision agriculture.
The goal of multimodal alignment is to learn a single latent space that is shared between multimodal inputs. The most powerful models in this space have been trained using massive datasets of paired inputs and large-scale computational resources making them prohibitively expensive to train in many practical scenarios. We surmise that existing unimodal encoders pre-trained on large amounts of unimodal data should provide an effective bootstrap to create multimodal models from unimodal ones at much lower costs. We therefore propose FuseMix a multimodal augmentation scheme that operates on the latent spaces of arbitrary pre-trained unimodal encoders. Using FuseMix for multimodal alignment we achieve competitive performance - and in certain cases outperform state-of-the art methods - in both image-text and audio-text retrieval with orders of magnitude less compute and data: for example we outperform CLIP on the Flickr30K text-to-image retrieval task with ?600x fewer GPU days and ?80x fewer image-text pairs. Additionally we show how our method can be applied to convert pre-trained text-to-image generative models into audio-to-image ones. Code is available at: https://github.com/layer6ai-labs/fusemix.
Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions. Existing PFL methods usually decouple global updates in deep neural networks by performing personalization on particular layers (i.e. classifier heads) and global aggregation for the rest of the network. However preselecting network layers for personalization may result in suboptimal storage of global knowledge. In this work we propose FedSelect a novel PFL algorithm inspired by the iterative subnetwork discovery procedure used for the Lottery Ticket Hypothesis. FedSelect incrementally expands subnetworks to personalize client parameters concurrently conducting global aggregations on the remaining parameters. This approach enables the personalization of both client parameters and subnetwork structure during the training process. Finally we show that FedSelect outperforms recent state-of-the-art PFL algorithms under challenging client data heterogeneity settings and demonstrates robustness to various real-world distributional shifts.
3D facial landmark localization has proven to be of particular use for applications such as face tracking 3D face modeling and image-based 3D face reconstruction. In the supervised learning case such methods usually rely on 3D landmark datasets derived from 3DMM-based registration that often lack spatial definition alignment as compared with that chosen by hand-labeled human consensus e.g. how are eyebrow landmarks defined? This creates a gap between landmark datasets generated via high-quality 2D human labels and 3DMMs and it ultimately limits their effectiveness. To address this issue we introduce a novel semi-supervised learning approach that learns 3D landmarks by directly lifting (visible) hand-labeled 2D landmarks and ensures better definition alignment without the need for 3D landmark datasets. To lift 2D landmarks to 3D we leverage 3D-aware GANs for better multi-view consistency learning and in-the-wild multi-frame videos for robust cross-generalization. Empirical experiments demonstrate that our method not only achieves better definition alignment between 2D-3D landmarks but also outperforms other supervised learning 3D landmark localization methods on both 3DMM labeled and photogrammetric ground truth evaluation datasets.
Empirical experiments demonstrate that our method not only achieves better definition alignment between 2D-3D landmarks but also outperforms other supervised learning 3D landmark localization methods on both 3DMM labeled and photogrammetric ground truth evaluation datasets. Project Page: https://davidcferman.github.io/FaceLift
Image-level Weakly Supervised Semantic Segmentation (WSSS) has received increasing attention due to its low annotation cost. Class Activation Mapping (CAM) generated through classifier weights in WSSS inevitably ignores certain useful cues while the CAM generated through class prototypes can alleviate that. However because of the different goals of image classification and semantic segmentation the class prototypes still focus on activating primary discriminative pixels learned from classification loss leading to incomplete CAM. In this paper we propose a plugand-play Prototype-based Secondary Discriminative Pixels Mining (PSDPM) framework for enabling class prototypes to activate more secondary discriminative pixels thus generating a more complete CAM. Specifically we introduce a Foreground Pixel Estimation Module (FPEM) for estimating potential foreground pixels based on the correlations between primary and secondary discriminative pixels and the semantic segmentation results of baseline methods. Then we enable WSSS model to learn discriminative features from secondary discriminative pixels through a consistency loss calculated between FPEM result and class-prototype CAM. Experimental results show that our PSDPM improves various baseline methods significantly and achieves new state-of-the-art performances on WSSS benchmarks. Codes are available at https://github.com/xinqiaozhao/PSDPM.
How to effectively explore multi-scale representations of rain streaks is important for image deraining. In contrast to existing Transformer-based methods that depend mostly on single-scale rain appearance we develop an end-to-end multi-scale Transformer that leverages the potentially useful features in various scales to facilitate high-quality image reconstruction. To better explore the common degradation representations from spatially-varying rain streaks we incorporate intra-scale implicit neural representations based on pixel coordinates with the degraded inputs in a closed-loop design enabling the learned features to facilitate rain removal and improve the robustness of the model in complex scenarios. To ensure richer collaborative representation from different scales we embed a simple yet effective inter-scale bidirectional feedback operation into our multi-scale Transformer by performing coarse-to-fine and fine-to-coarse information communication. Extensive experiments demonstrate that our approach named as NeRD-Rain performs favorably against the state-of-the-art ones on both synthetic and real-world benchmark datasets. The source code and trained models are available at https://github.com/cschenxiang/NeRD-Rain.
Weakly supervised semantic segmentation has witnessed great achievements with image-level labels. Several recent approaches use the CLIP model to generate pseudo labels for training an individual segmentation model while there is no attempt to apply the CLIP model as the backbone to directly segment objects with image-level labels. In this paper we propose WeCLIP a CLIP-based single-stage pipeline for weakly supervised semantic segmentation. Specifically the frozen CLIP model is applied as the backbone for semantic feature extraction and a new decoder is designed to interpret extracted semantic features for final prediction. Meanwhile we utilize the above frozen backbone to generate pseudo labels for training the decoder. Such labels cannot be optimized during training. We then propose a refinement module (RFM) to rectify them dynamically. Our architecture enforces the proposed decoder and RFM to benefit from each other to boost the final performance. Extensive experiments show that our approach significantly outperforms other approaches with less training cost. Additionally our WeCLIP also obtains promising results for fully supervised settings. The code is available at https://github.com/zbf1991/WeCLIP.
Personalized Federated Learning (PFL) is primarily designed to provide customized models for each client to better fit the non-iid distributed client data which is a inherent challenge in Federated Learning. However current PFL methods suffer from inconsistencies in both intra-client and inter-client levels: 1) The intra-client inconsistency stems from the asynchronous update strategy for personalized and shared parameters. In PFL clients update their shared parameters to communicate and learn from others while keeping personalized parts unchanged leading to poor coordination between these two components. 2) The Inter-client inconsistency arises from "stragglers" - inactive clients that communicate and train with the server less frequently. This results in their under-trained personalized models and impedes the collaborative training stage for other clients. In this paper we present a novel PFL framework named FedAS which uses Federated Parameter-Alignment and Client-Synchronization to overcome above challenges. Initially we enhance the localization of global parameters by infusing them with local insights. We make the shared parts learn from previous model thereby increasing their local relevance and reducing the impact of parameter inconsistency. Furthermore we design a robust aggregation method to mitigate the impact of stragglers by preventing the incorporation of their under-trained knowledge into aggregated model.
We make the shared parts learn from previous model thereby increasing their local relevance and reducing the impact of parameter inconsistency. Furthermore we design a robust aggregation method to mitigate the impact of stragglers by preventing the incorporation of their under-trained knowledge into aggregated model. Experimental results on Cifar10 and Cifar100 validate the effectiveness of our FedAS in achieving better performance and robustness against data heterogeneity.
In this work we focus on learning facial representations that can be adapted to train effective face recognition models particularly in the absence of labels. Firstly compared with existing labelled face datasets a vastly larger magnitude of unlabeled faces exists in the real world. We explore the learning strategy of these unlabeled facial images through self-supervised pretraining to transfer generalized face recognition performance. Moreover motivated by one recent finding that is the face saliency area is critical for face recognition in contrast to utilizing random cropped blocks of images for constructing augmentations in pretraining we utilize patches localized by extracted facial landmarks. This enables our method - namely Landmark-based Facial Self-supervised learning (LAFS) to learn key representation that is more critical for face recognition. We also incorporate two landmark-specific augmentations which introduce more diversity of landmark information to further regularize the learning. With learned landmark-based facial representations we further adapt the representation for face recognition with regularization mitigating variations in landmark positions. Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks especially on more challenging few-shot scenarios. The code is available at https://github.com/szlbiubiubiu/LAFS_CVPR2024
Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-trained vision-language models in which the key is to adopt the image-level model for pixel-level segmentation task. In this paper we propose a simple encoder-decoder named SED for open-vocabulary semantic segmentation which comprises a hierarchical encoder-based cost map generation and a gradual fusion decoder with category early rejection. The hierarchical encoder-based cost map generation employs hierarchical backbone instead of plain transformer to predict pixel-level image-text cost map. Compared to plain transformer hierarchical backbone better captures local spatial information and has linear computational complexity with respect to input size. Our gradual fusion decoder employs a top-down structure to combine cost map and the feature maps of different backbone levels for segmentation. To accelerate inference speed we introduce a category early rejection scheme in the decoder that rejects many no-existing categories at the early layer of decoder resulting in at most 4.7 times acceleration without accuracy degradation. Experiments are performed on multiple open-vocabulary semantic segmentation datasets which demonstrates the efficacy of our SED method.
To accelerate inference speed we introduce a category early rejection scheme in the decoder that rejects many no-existing categories at the early layer of decoder resulting in at most 4.7 times acceleration without accuracy degradation. Experiments are performed on multiple open-vocabulary semantic segmentation datasets which demonstrates the efficacy of our SED method. When using ConvNeXt-B our SED method achieves mIoU score of 31.6% on ADE20K with 150 categories at 82 millisecond (ms) per image on a single A6000. Our source code is available at https://github.com/xb534/SED.