CVPR 2024 Papers

Periocular and face are complementary biometrics for identity management albeit with inherent limitations notably in scenarios involving occlusion due to sunglasses or masks. In response to these challenges we introduce Flexible Biometric Recognition (FBR) a novel framework designed to advance conventional face periocular and multimodal face-periocular biometrics across both intra- and cross-modality recognition tasks. FBR strategically utilizes the Multimodal Fusion Attention (MFA) and Multimodal Prompt Tuning (MPT) mechanisms within the Vision Transformer architecture. MFA facilitates the fusion of modalities ensuring cohesive alignment between facial and periocular embeddings while incorporating soft-biometrics to enhance the model's ability to discriminate between individuals. The fusion of three modalities is pivotal in exploring interrelationships between different modalities. Additionally MPT serves as a unifying bridge intertwining inputs and promoting cross-modality interactions while preserving their distinctive characteristics. The collaborative synergy of MFA and MPT enhances the shared features of the face and periocular with a specific emphasis on the ocular region yielding exceptional performance in both intra- and cross-modality recognition tasks. Rigorous experimentation across four benchmark datasets validates the noteworthy performance of the FBR model. The source code is available at https://github.com/MIS-DevWorks/FBR.

Hierarchy is a natural representation of semantic taxonomies including the ones routinely used in image segmentation. Indeed recent work on semantic segmentation reports improved accuracy from supervised training leveraging hierarchical label structures. Encouraged by these results we revisit the fundamental assumptions behind that work. We postulate and then empirically verify that the reasons for the observed improvement in segmentation accuracy may be entirely unrelated to the use of the semantic hierarchy. To demonstrate this we design a range of cross-domain experiments with a representative hierarchical approach. We find that on the new testing domains a flat (non-hierarchical) segmentation network in which the parents are inferred from the children has superior segmentation accuracy to the hierarchical approach across the board. Complementing these findings and inspired by the intrinsic properties of hyperbolic spaces we study a more principled approach to hierarchical segmentation using the Poincare ball model. The hyperbolic representation largely outperforms the previous (Euclidean) hierarchical approach as well and is on par with our flat Euclidean baseline in terms of segmentation accuracy. However it additionally exhibits surprisingly strong calibration quality of the parent nodes in the semantic hierarchy especially on the more challenging domains. Our combined analysis suggests that the established practice of hierarchical segmentation may be limited to in-domain settings whereas flat classifiers generalize substantially better especially if they are modeled in the hyperbolic space.

Cross-domain few-shot learning (CDFSL) aims to acquire knowledge from limited training data in the target domain by leveraging prior knowledge transferred from source domains with abundant training samples. CDFSL faces challenges in transferring knowledge across dissimilar domains and fine-tuning models with limited training data. To address these challenges we initially extend the analysis of loss landscapes from the parameter space to the representation space which allows us to simultaneously interpret the transferring and fine-tuning difficulties of CDFSL models. We observe that sharp minima in the loss landscapes of the representation space result in representations that are hard to transfer and fine-tune. Moreover existing flatness-based methods have limited generalization ability due to their short-range flatness. To enhance the transferability and facilitate fine-tuning we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape. This approach considers representations that are differently normalized as minima in the loss landscape and flattens the high-loss region in the middle by randomly sampling interpolated representations. We implement this method as a new normalization layer that replaces the original one in both CNNs and ViTs. This layer is simple and lightweight introducing only a minimal number of additional parameters. Experimental results on 8 datasets demonstrate that our approach outperforms state-of-the-art methods in terms of average accuracy. Moreover our method achieves performance improvements of up to 9% compared to the current best approaches on individual datasets. Our code will be released.

In recent years there has been significant progress in the development of text-to-image generative models. Evaluating the quality of the generative models is one essential step in the development process. Unfortunately the evaluation process could consume a significant amount of computational resources making the required periodic evaluation of model performance (e.g. monitoring training progress) impractical. Therefore we seek to improve the evaluation efficiency by selecting the representative subset of the text-image dataset. We systematically investigate the design choices including the selection criteria (textural features or imagebased metrics) and the selection granularity (prompt-level or set-level). We find that the insights from prior work on subset selection for training data do not generalize to this problem and we propose FlashEval an iterative search algorithm tailored to evaluation data selection. We demonstrate the effectiveness of FlashEval on ranking diffusion models with various configurations including architectures quantization levels and sampler schedules on COCO and DiffusionDB datasets. Our searched 50-item subset could achieve comparable evaluation quality to the randomly sampled 500-item subset for COCO annotations on unseen models achieving a 10x evaluation speedup. We release the condensed subset of these commonly used datasets to help facilitate diffusion algorithm design and evaluation and open-source FlashEval as a tool for condensing future datasets accessible at https://github.com/thu-nics/FlashEval.

We propose FlashAvatar a novel and lightweight 3D animatable avatar representation that could reconstruct a digital avatar from a short monocular video sequence in minutes and render high-fidelity photo-realistic images at 300FPS on a consumer-grade GPU. To achieve this we maintain a uniform 3D Gaussian field embedded in the surface of a parametric face model and learn extra spatial offset to model non-surface regions and subtle facial details. While full use of geometric priors can capture high-frequency facial details and preserve exaggerated expressions proper initialization can help reduce the number of Gaussians thus enabling super-fast rendering speed. Extensive experimental results demonstrate that FlashAvatar outperforms existing works regarding visual quality and personalized details and is almost an order of magnitude faster in rendering speed. Project page: https://ustc3dv.github.io/FlashAvatar/

We introduce the Fixed Point Diffusion Model (FPDM) a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based generative modeling. Our approach embeds an implicit fixed point solving layer into the denoising network of a diffusion model transforming the diffusion process into a sequence of closely-related fixed point problems. Combined with a new stochastic training method this approach significantly reduces model size reduces memory usage and accelerates training. Moreover it enables the development of two new techniques to improve sampling efficiency: reallocating computation across timesteps and reusing fixed point solutions between timesteps. We conduct extensive experiments with state-of-the-art models on ImageNet FFHQ CelebA-HQ and LSUN-Church demonstrating substantial improvements in performance and efficiency. Compared to the state-of-the-art DiT model FPDM contains 87% fewer parameters consumes 60% less memory during training and improves image generation quality in situations where sampling computation or time is limited.

Affine subspaces of Euclidean spaces are also referred to as flats. A standard task in computer vision or more generally in engineering and applied sciences is fitting a flat to a set of points which is commonly solved using the PCA. We generalize this technique to enable fitting a flat to a set of other flats possibly of varying dimensions based on representing the flats as squared distance fields. Compared to previous approaches such as Riemannian centers of mass in the manifold of affine Grassmannians our approach is conceptually much simpler and computationally more efficient yet offers desirable properties such as respecting symmetries and being equivariant to rigid transformations leading to more intuitive and useful results in practice. We demonstrate these claims in a number of synthetic experiments and a multi-view reconstruction task of line-like objects.

The Laplace-Beltrami operator (LBO) emerges from studying manifolds equipped with a Riemannian metric. It is often called the swiss army knife of geometry processing as it allows to capture intrinsic shape information and gives rise to heat diffusion geodesic distances and a multitude of shape descriptors. It also plays a central role in geometric deep learning. In this work we explore Finsler manifolds as a generalization of Riemannian manifolds. We revisit the Finsler heat equation and derive a Finsler heat kernel and a Finsler-Laplace-Beltrami Operator (FLBO): a novel theoretically justified anisotropic Laplace-Beltrami operator (ALBO). In experimental evaluations we demonstrate that the proposed FLBO is a valuable alternative to the traditional Riemannian-based LBO and ALBOs for spatial filtering and shape correspondence estimation. We hope that the proposed Finsler heat kernel and the FLBO will inspire further exploration of Finsler geometry in the computer vision community.

Fine-grained action analysis in multi-person sports is complex due to athletes' quick movements and intense physical confrontations which result in severe visual obstructions in most scenes. In addition accessible multi-person sports video datasets lack fine-grained action annotations in both space and time adding to the difficulty in fine-grained action analysis. To this end we construct a new multi-person basketball sports video dataset named FineSports which contains fine-grained semantic and spatial-temporal annotations on 10000 NBA game videos covering 52 fine-grained action types 16000 action instances and 123000 spatial-temporal bounding boxes. We also propose a new prompt-driven spatial-temporal action location approach called PoSTAL composed of a prompt-driven target action encoder (PTA) and an action tube-specific detector (ATD) to directly generate target action tubes with fine-grained action types without any off-line proposal generation. Extensive experiments on the FineSports dataset demonstrate that PoSTAL outperforms state-of-the-art methods. Data and code are available at https://github.com/PKU-ICST-MIPL/FineSports_CVPR2024.

Existing action quality assessment (AQA) methods mainly learn deep representations at the video level for scoring diverse actions. Due to the lack of a fine-grained understanding of actions in videos they harshly suffer from low credibility and interpretability thus insufficient for stringent applications such as Olympic diving events. We argue that a fine-grained understanding of actions requires the model to perceive and parse actions in both time and space which is also the key to the credibility and interpretability of the AQA technique. Based on this insight we propose a new fine-grained spatial-temporal action parser named FineParser. It learns human-centric foreground action representations by focusing on target action regions within each frame and exploiting their fine-grained alignments in time and space to minimize the impact of invalid backgrounds during the assessment. In addition we construct fine-grained annotations of human-centric foreground action masks for the FineDiving dataset called FineDiving-HM. With refined annotations on diverse target action procedures FineDiving-HM can promote the development of real-world AQA systems. Through extensive experiments we demonstrate the effectiveness of FineParser which outperforms state-of-the-art methods while supporting more tasks of fine-grained action understanding. Data and code are available at https://github.com/PKU-ICST-MIPL/FineParser_CVPR2024.

The 3D Human Pose Estimation (3D HPE) task uses 2D images or videos to predict human joint coordinates in 3D space. Despite recent advancements in deep learning-based methods they mostly ignore the capability of coupling accessible texts and naturally feasible knowledge of humans missing out on valuable implicit supervision to guide the 3D HPE task. Moreover previous efforts often study this task from the perspective of the whole human body neglecting fine-grained guidance hidden in different body parts. To this end we present a new Fine-Grained Prompt-Driven Denoiser based on a diffusion model for 3D HPE named FinePOSE. It consists of three core blocks enhancing the reverse process of the diffusion model: (1) Fine-grained Part-aware Prompt learning (FPP) block constructs fine-grained part-aware prompts via coupling accessible texts and naturally feasible knowledge of body parts with learnable prompts to model implicit guidance. (2) Fine-grained Prompt-pose Communication (FPC) block establishes fine-grained communications between learned part-aware prompts and poses to improve the denoising quality. (3) Prompt-driven Timestamp Stylization (PTS) block integrates learned prompt embedding and temporal information related to the noise level to enable adaptive adjustment at each denoising step. Extensive experiments on public single-human pose estimation datasets show that FinePOSE outperforms state-of-the-art methods. We further extend FinePOSE to multi-human pose estimation. Achieving 34.3mm average MPJPE on the EgoHumans dataset demonstrates the potential of FinePOSE to deal with complex multi-human scenarios. Code is available at https://github.com/PKU-ICST-MIPL/FinePOSE_CVPR2024.

In this paper we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph which provides comprehensive and complementary neighbor information of the data. The factor matrices are learned with bipartite graph partitioning which exploits explicit cluster structure of the data and is more geared towards clustering application. We develop an effective and efficient optimization algorithm for our method and provide elegant theoretical results about the convergence. Extensive experimental results confirm the effectiveness of the proposed method.

Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on this framework and propose a new pruning at initialization algorithm that leverages the Neural Tangent Kernel (NTK) theory to align the training dynamics of the sparse network with that of the dense one. Specifically we show how the usually neglected data-dependent component in the NTK's spectrum can be taken into account by providing an analytical upper bound to the NTK's trace obtained by decomposing neural networks into individual paths. This leads to our Path eXclusion (PX) a foresight pruning method designed to preserve the parameters that mostly influence the NTK's trace. PX is able to find lottery tickets (i.e. good paths) even at high sparsity levels and largely reduces the need for additional training. When applied to pre-trained models it extracts subnetworks directly usable for several downstream tasks resulting in performance comparable to those of the dense counterpart but with substantial cost and computational savings.

Even when using large multi-modal foundation models few-shot learning is still challenging -- if there is no proper inductive bias it is nearly impossible to keep the nuanced class attributes while removing the visually prominent attributes that spuriously correlate with class labels. To this end we find an inductive bias that the time-steps of a Diffusion Model (DM) can isolate the nuanced class attributes i.e. as the forward diffusion adds noise to an image at each time-step nuanced attributes are usually lost at an earlier time-step than the spurious attributes that are visually prominent. Building on this we propose Time-step Few-shot (TiF) learner. We train class-specific low-rank adapters for a text-conditioned DM to make up for the lost attributes such that images can be accurately reconstructed from their noisy ones given a prompt. Hence at a small time-step the adapter and prompt are essentially a parameterization of only the nuanced class attributes. For a test image we can use the parameterization to only extract the nuanced class attributes for classification. TiF learner significantly outperforms OpenCLIP and its adapters on a variety of fine-grained and customized few-shot learning tasks. Codes are in https://github.com/yue-zhongqi/tif.

Few-shot object detection (FSOD) aims to detect objects with only a few training examples. Visual feature extraction and query-support similarity learning are the two critical components. Existing works are usually developed based on ImageNet pre-trained vision backbones and design sophisticated metric-learning networks for few-shot learning but still have inferior accuracy. In this work we study few-shot object detection using modern foundation models. First vision-only contrastive pre-trained DINOv2 model is used for the vision backbone which shows strong transferable performance without tuning the parameters. Second Large Language Model (LLM) is employed for contextualized few-shot learning with the input of all classes and query image proposals. Language instructions are carefully designed to prompt the LLM to classify each proposal in context. The contextual information include proposal-proposal relations proposal-class relations and class-class relations which can largely promote few-shot learning. We comprehensively evaluate the proposed model (FM-FSOD) in multiple FSOD benchmarks achieving state-of-the-arts performance.

While behavior cloning has recently emerged as a highly successful paradigm for autonomous driving humans rarely learn to perform complex tasks such as driving via imitation or behavior cloning alone. In contrast learning in humans often involves additional detailed guidance throughout the interactive learning process i.e. where feedback often via language provides detailed information as to which part of their trial was performed incorrectly or suboptimally and why. Motivated by this observation we introduce an efficient feedback-based framework for improving behavior-cloning-based training of sensorimotor driving agents. Our key insight is to leverage recent advances in Large Language Models (LLMs) to provide corrective fine-grained feedback regarding the underlying reason behind driving prediction failures. Moreover our introduced network architecture is efficient enabling the first sensorimotor end-to-end training and evaluation of LLM-based driving models. The resulting agent achieves state-of-the-art performance in open-loop evaluation on nuScenes outperforming prior state-of-the-art by over 8.1% and 57.1% in accuracy and collision rate respectively. In CARLA our camera-based agent improves by 16.6% in driving score over prior LIDAR-based approaches.

We introduce a novel approach for adapting deep stereo networks in a collaborative manner. By building over principles of federated learning we develop a distributed framework allowing for demanding the optimization process to a number of clients deployed in different environments. This makes it possible for a deep stereo network running on resourced-constrained devices to capitalize on the adaptation process carried out by other instances of the same architecture and thus improve its accuracy in challenging environments even when it cannot carry out adaptation on its own. Experimental results show how federated adaptation performs equivalently to on-device adaptation and even better when dealing with challenging environments.

Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes given labeled data of known classes. To meet the recent decentralization trend in the community we introduce a practical yet challenging task Federated GCD (Fed-GCD) where the training data are distributed in local clients and cannot be shared among clients. Fed-GCD aims to train a generic GCD model by client collaboration under the privacy-protected constraint. The Fed-GCD leads to two challenges: 1) representation degradation caused by training each client model with fewer data than centralized GCD learning and 2) highly heterogeneous label spaces across different clients. To this end we propose a novel Associated Gaussian Contrastive Learning (AGCL) framework based on learnable GMMs which consists of a Client Semantics Association (CSA) and a global-local GMM Contrastive Learning (GCL). On the server CSA aggregates the heterogeneous categories of local-client GMMs to generate a global GMM containing more comprehensive category knowledge. On each client GCL builds class-level contrastive learning with both local and global GMMs. The local GCL learns robust representation with limited local data. The global GCL encourages the model to produce more discriminative representation with the comprehensive category relationships that may not exist in local data. We build a benchmark based on six visual datasets to facilitate the study of Fed-GCD. Extensive experiments show that our AGCL outperforms multiple baselines on all datasets.