CVPR 2024 Papers

Recent years have witnessed a trend of the deep integration of the generation and reconstruction paradigms. In this paper we extend the ability of controllable generative models for a more comprehensive hand mesh recovery task: direct hand mesh generation inpainting reconstruction and fitting in a single framework which we name as Holistic Hand Mesh Recovery (HHMR). Our key observation is that different kinds of hand mesh recovery tasks can be achieved by a single generative model with strong multimodal controllability and in such a framework realizing different tasks only requires giving different signals as conditions. To achieve this goal we propose an all-in-one diffusion framework based on graph convolution and attention mechanisms for holistic hand mesh recovery. In order to achieve strong control generation capability while ensuring the decoupling of multimodal control signals we map different modalities to a share feature space and apply cross-scale random masking in both modality and feature levels. In this way the correlation between different modalities can be fully exploited during the learning of hand priors. Furthermore we propose Condition-aligned Gradient Guidance to enhance the alignment of the generated model with the control signals which significantly improves the accuracy of the hand mesh reconstruction and fitting. Experiments show that our novel framework can realize multiple hand mesh recovery tasks simultaneously and outperform the existing methods in different tasks which provides more possibilities for subsequent downstream applications including gesture recognition pose generation mesh editing and so on.

Vision graph neural networks (ViG) offer a new avenue for exploration in computer vision. A major bottleneck in ViGs is the inefficient k-nearest neighbor (KNN) operation used for graph construction. To solve this issue we propose a new method for designing ViGs Dynamic Axial Graph Construction (DAGC) which is more efficient than KNN as it limits the number of considered graph connections made within an image. Additionally we propose a novel CNN-GNN architecture GreedyViG which uses DAGC. Extensive experiments show that GreedyViG beats existing ViG CNN and ViT architectures in terms of accuracy GMACs and parameters on image classification object detection instance segmentation and semantic segmentation tasks. Our smallest model GreedyViG-S achieves 81.1% top-1 accuracy on ImageNet-1K 2.9% higher than Vision GNN and 2.2% higher than Vision HyperGraph Neural Network (ViHGNN) with less GMACs and a similar number of parameters. Our largest model GreedyViG-B obtains 83.9% top-1 accuracy 0.2% higher than Vision GNN with a 66.6% decrease in parameters and a 69% decrease in GMACs. GreedyViG-B also obtains the same accuracy as ViHGNN with a 67.3% decrease in parameters and a 71.3% decrease in GMACs. Our work shows that hybrid CNN-GNN architectures not only provide a new avenue for designing efficient models but that they can also exceed the performance of current state-of-the-art models.

The success of a specific neural network architecture is closely tied to the dataset and task it tackles; there is no one-size-fits-all solution. Thus considerable efforts have been made to quickly and accurately estimate the performances of neural architectures without full training or evaluation for given tasks and datasets. Neural architecture encoding has played a crucial role in the estimation and graphbased methods which treat an architecture as a graph have shown prominent performance. For enhanced representation learning of neural architectures we introduce FlowerFormer a powerful graph transformer that incorporates the information flows within a neural architecture. FlowerFormer consists of two key components: (a) bidirectional asynchronous message passing inspired by the flows; (b) global attention built on flow-based masking. Our extensive experiments demonstrate the superiority of FlowerFormer over existing neural encoding methods and its effectiveness extends beyond computer vision models to include graph neural networks and auto speech recognition models. Our code is available at http://github.com/y0ngjaenius/CVPR2024_FLOWERFormer.

Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context we posit that a general unified model can effectively address them all irrespective of the input data type (image 3D etc.). We introduce DiffAssemble a Graph Neural Network (GNN)-based architecture that learns to solve reassembly tasks using a diffusion model formulation. Our method treats the elements of a set whether pieces of 2D patch or 3D object fragments as nodes of a spatial graph. Training is performed by introducing noise into the position and rotation of the elements and iteratively denoising them to reconstruct the coherent initial pose. DiffAssemble achieves state-of-the-art (SOTA) results in most 2D and 3D reassembly tasks and is the first learning-based approach that solves 2D puzzles for both rotation and translation. Furthermore we highlight its remarkable reduction in run-time performing 11 times faster than the quickest optimization-based method for puzzle solving.

In Federated Learning (FL) the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' data vary in modalities. In response to these issues of statistical heterogeneity and modality incompatibility we propose the Adaptive Hyper-graph Aggregation framework a novel solution for Modality-Agnostic Federated Learning. We design a Modular Architecture for Local Model with single modality setting the stage for efficient intra-modality sharing and inter-modality complementarity. An innovative Global Consensus Prototype Enhancer is crafted to assimilate and broadcast global consensus knowledge within the network. At the core of our approach lies the Adaptive Hyper-graph Learning Strategy which effectively tackles the inherent challenges of modality incompatibility and statistical heterogeneity within federated learning environments accomplishing this adaptively even without the server being aware of the clients' modalities. Our approach tested on three multimodal benchmark datasets demonstrated strong performance across diverse data distributions affirming its effectiveness in multimodal federated learning.