CVPR 2024 论文列表

Quantization for model compression can efficiently reduce the network complexity and storage requirement but the original training data is necessary to remedy the performance loss caused by quantization. The Data-Free Quantization (DFQ) methods have been proposed to handle the absence of original training data with synthetic data. However there are differences between the synthetic and original training data which affects the performance of the quantized network but none of the existing methods considers the differences. In this paper we propose an efficient data-free quantization via pseudo-label filtering which is the first to evaluate the synthetic data before quantization. We design a new metric for evaluating synthetic data using self-entropy which indicates the reliability of synthetic data. The synthetic data can be categorized with the metric into high- and low-reliable datasets for the following training process. Besides the multiple pseudo-labels are designed to label the synthetic data with different reliability which can provide valuable supervision information and avoid misleading training by low-reliable samples. Extensive experiments are implemented on several datasets including CIFAR-10 CIFAR-100 and ImageNet with various models. The experimental results show that our method can perform excellently and outperform existing methods in accuracy.

4D medical images which represent 3D images with temporal information are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression. However acquiring 4D medical images poses challenges due to factors such as radiation exposure and imaging duration necessitating a balance between achieving high temporal resolution and minimizing adverse effects. Given these circumstances not only is data acquisition challenging but increasing the frame rate for each dataset also proves difficult. To address this challenge this paper proposes a simple yet effective Unsupervised Volumetric Interpolation framework UVI-Net. This framework facilitates temporal interpolation without the need for any intermediate frames distinguishing it from the majority of other existing unsupervised methods. Experiments on benchmark datasets demonstrate significant improvements across diverse evaluation metrics compared to unsupervised and supervised baselines. Remarkably our approach achieves this superior performance even when trained with a dataset as small as one highlighting its exceptional robustness and efficiency in scenarios with sparse supervision. This positions UVI-Net as a compelling alternative for 4D medical imaging particularly in settings where data availability is limited. The source code is available at https://github.com/jungeun122333/UVI-Net.

Federated Learning (FL) enables collaborative model training while preserving the privacy of raw data. A challenge in this framework is the fair and efficient valuation of data which is crucial for incentivizing clients to contribute high-quality data in the FL task. In scenarios involving numerous data clients within FL it is often the case that only a subset of clients and datasets are pertinent to a specific learning task while others might have either a negative or negligible impact on the model training process. This paper introduces a novel privacy-preserving method for evaluating client contributions and selecting relevant datasets without a pre-specified training algorithm in an FL task. Our proposed approach \texttt FedBary utilizes Wasserstein distance within the federated context offering a new solution for data valuation in the FL framework. This method ensures transparent data valuation and efficient computation of the Wasserstein barycenter and reduces the dependence on validation datasets. Through extensive empirical experiments and theoretical analyses we demonstrate the advantages of this data valuation method as a promising avenue for FL research.

Contrastive learning (CL) pre-trains general-purpose encoders using an unlabeled pre-training dataset which consists of images or image-text pairs. CL is vulnerable to data poisoning based backdoor attacks (DPBAs) in which an attacker injects poisoned inputs into the pre-training dataset so the encoder is backdoored. However existing DPBAs achieve limited effectiveness. In this work we take the first step to analyze the limitations of existing backdoor attacks and propose new DPBAs called CorruptEncoder to CL. CorruptEncoder introduces a new attack strategy to create poisoned inputs and uses a theory-guided method to maximize attack effectiveness. Our experiments show that CorruptEncoder substantially outperforms existing DPBAs. In particular CorruptEncoder is the first DPBA that achieves more than 90% attack success rates with only a few (3) reference images and a small poisoning ratio (0.5%). Moreover we also propose a defense called localized cropping to defend against DPBAs. Our results show that our defense can reduce the effectiveness of DPBAs but it sacrifices the utility of the encoder highlighting the need for new defenses.

Recently dataset distillation has paved the way towards efficient machine learning especially for image datasets. However the distillation for videos characterized by an exclusive temporal dimension remains an underexplored domain. In this work we provide the first systematic study of video distillation and introduce a taxonomy to categorize temporal compression. Our investigation reveals that the temporal information is usually not well learned during distillation and the temporal dimension of synthetic data contributes little. The observations motivate our unified framework of disentangling the dynamic and static information in the videos. It first distills the videos into still images as static memory and then compensates the dynamic and motion information with a learnable dynamic memory block. Our method achieves state-of-the-art on video datasets at different scales with notably smaller memory storage budget. Our code is available at https://github.com/yuz1wan/video_distillation.

Choreographers determine what the dances look like while cameramen determine the final presentation of dances. Recently various methods and datasets have showcased the feasibility of dance synthesis. However camera movement synthesis with music and dance remains an unsolved challenging problem due to the scarcity of paired data. Thus we present DCM a new multi-modal 3D dataset which for the first time combines camera movement with dance motion and music audio. This dataset encompasses 108 dance sequences (3.2 hours) of paired dance-camera-music data from the anime community covering 4 music genres. With this dataset we uncover that dance camera movement is multifaceted and human-centric and possesses multiple influencing factors making dance camera synthesis a more challenging task compared to camera or dance synthesis alone. To overcome these difficulties we propose DanceCamera3D a transformer-based diffusion model that incorporates a novel body attention loss and a condition separation strategy. For evaluation we devise new metrics measuring camera movement quality diversity and dancer fidelity. Utilizing these metrics we conduct extensive experiments on our DCM dataset providing both quantitative and qualitative evidence showcasing the effectiveness of our DanceCamera3D model. Code and video demos are available at https://github.com/ Carmenw1203/DanceCamera3D-Official.

Addressing the intricate challenge of modeling and re-rendering dynamic scenes most recent approaches have sought to simplify these complexities using plane-based explicit representations overcoming the slow training time issues associated with methods like Neural Radiance Fields (NeRF) and implicit representations. However the straightforward decomposition of 4D dynamic scenes into multiple 2D plane-based representations proves insufficient for re-rendering high-fidelity scenes with complex motions. In response we present a novel direction-aware representation (DaRe) approach that captures scene dynamics from six different directions. This learned representation undergoes an inverse dual-tree complex wavelet transformation (DTCWT) to recover plane-based information. DaReNeRF computes features for each space-time point by fusing vectors from these recovered planes. Combining DaReNeRF with a tiny MLP for color regression and leveraging volume rendering in training yield state-of-the-art performance in novel view synthesis for complex dynamic scenes. Notably to address redundancy introduced by the six real and six imaginary direction-aware wavelet coefficients we introduce a trainable masking approach mitigating storage issues without significant performance decline. Moreover DaReNeRF maintains a 2x reduction in training time compared to prior art while delivering superior performance.

Dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures.With empirical observations we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this we introduce Dataset Distillation via Disentangled Diffusion Model (D^4M) an efficient framework for dataset distillation. Compared to architecture-dependent methods D^4M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments D^4M demonstrates superior performance and robust generalization surpassing the SOTA methods across most aspects.

In this paper we focus on a challenging Online Task-Free Class Incremental Learning (OTFCIL) problem. Different from the existing methods that continuously learn the feature space from data streams we propose a novel compute-and-align paradigm for the OTFCIL. It first computes an optimal geometry i.e. the class prototype distribution for classifying existing classes and updates it when new classes emerge and then trains a DNN model by aligning its feature space to the optimal geometry. To this end we develop a novel Dynamic Neural Collapse (DNC) algorithm to compute and update the optimal geometry. The DNC expands the geometry when new classes emerge without loss of the geometry optimality and guarantees the drift distance of old class prototypes with an explicit upper bound. Then we propose a novel Dynamic feature space Self-Organization (DYSON) method containing three major components including 1) a feature extractor 2) a Dynamic Feature-Geometry Alignment (DFGA) module aligning the feature space to the optimal geometry computed by DNC and 3) a training-free class-incremental classifier derived from the DNC geometry. Experimental comparison results on four benchmark datasets including CIFAR10 CIFAR100 CUB200 and CoRe50 demonstrate the efficiency and superiority of the DYSON method. The source code is provided in the supplementary material.

Determining the relative pose of an object between two images is pivotal to the success of generalizable object pose estimation. Existing approaches typically approximate the continuous pose representation with a large number of discrete pose hypotheses which incurs a computationally expensive process of scoring each hypothesis at test time. By contrast we present a Deep Voxel Matching Network (DVMNet) that eliminates the need for pose hypotheses and computes the relative object pose in a single pass. To this end we map the two input RGB images reference and query to their respective voxelized 3D representations. We then pass the resulting voxels through a pose estimation module where the voxels are aligned and the pose is computed in an end-to-end fashion by solving a least-squares problem. To enhance robustness we introduce a weighted closest voxel algorithm capable of mitigating the impact of noisy voxels. We conduct extensive experiments on the CO3D LINEMOD and Objaverse datasets demonstrating that our method delivers more accurate relative pose estimates for novel objects at a lower computational cost compared to state-of-the-art methods. Our code is released at: https://github.com/sailor-z/DVMNet.

Multi-view stereo reconstruction (MVS) in the wild requires to first estimate the camera intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain yet they are mandatory to triangulate corresponding pixels in 3D space which is at the core of all best performing MVS algorithms. In this work we take an opposite stance and introduce DUSt3R a radically novel paradigm for Dense and Unconstrained Stereo 3D Reconstruction of arbitrary image collections operating without prior information about camera calibration nor viewpoint poses. We cast the pairwise reconstruction problem as a regression of pointmaps relaxing the hard constraints of usual projective camera models. We show that this formulation smoothly unifies the monocular and binocular reconstruction cases. In the case where more than two images are provided we further propose a simple yet effective global alignment strategy that expresses all pairwise pointmaps in a common reference frame. We base our network architecture on standard Transformer encoders and decoders allowing us to leverage powerful pretrained models. Our formulation directly provides a 3D model of the scene as well as depth information but interestingly we can seamlessly recover from it pixel matches focal lengths relative and absolute cameras. Extensive experiments on all these tasks showcase how DUSt3R effectively unifies various 3D vision tasks setting new performance records on monocular & multi-view depth estimation as well as relative pose estimation. In summary DUSt3R makes many geometric 3D vision tasks easy. Code and models at https://github.com/naver/dust3r

In recent years there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. However UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients generally resulting in fragmented and discontinuous surfaces. In this paper we propose to learn a hyperbolic scaling of the unsigned distance field which defines a new Eikonal problem with distinct boundary conditions. This allows our formulation to integrate seamlessly with state-of-the-art continuously differentiable implicit neural representation networks largely applied in the literature to represent signed distance fields. Our approach not only addresses the challenge of open surface representation but also demonstrates significant improvement in reconstruction quality and training performance. Moreover the unlocked field's differentiability allows the accurate computation of essential topological properties such as normal directions and curvatures pervasive in downstream tasks such as rendering. Through extensive experiments we validate our approach across various data sets and against competitive baselines. The results demonstrate enhanced accuracy and up to an order of magnitude increase in speed compared to previous methods.

Generic Face Image Quality Assessment (GFIQA) evaluates the perceptual quality of facial images which is crucial in improving image restoration algorithms and selecting high-quality face images for downstream tasks. We present a novel transformer-based method for GFIQA which is aided by two unique mechanisms. First a novel Dual-Set Degradation Representation Learning (DSL) mechanism uses facial images with both synthetic and real degradations to decouple degradation from content ensuring generalizability to real-world scenarios. This self-supervised method learns degradation features on a global scale providing a robust alternative to conventional methods that use local patch information in degradation learning. Second our transformer leverages facial landmarks to emphasize visually salient parts of a face image in evaluating its perceptual quality. We also introduce a balanced and diverse Comprehensive Generic Face IQA (CGFIQA-40k) dataset of 40K images carefully designed to overcome the biases in particular the imbalances in skin tone and gender representation in existing datasets. Extensive analysis and evaluation demonstrate the robustness of our method marking a significant improvement over prior methods.

Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image which is challenging due to incomplete labeling long-tailed relationship categories and relational semantic overlap. Existing Transformer-based methods either employ distinct queries for objects and predicates or utilize holistic queries for relation triplets and hence often suffer from limited capacity in learning low-frequency relationships. In this paper we present a new Transformer-based method called DSGG that views scene graph detection as a direct graph prediction problem based on a unique set of graph-aware queries. In particular each graph-aware query encodes a compact representation of both the node and all of its relations in the graph acquired through the utilization of a relaxed sub-graph matching during the training process. Moreover to address the problem of relational semantic overlap we utilize a strategy for relation distillation aiming to efficiently learn multiple instances of semantic relationships. Extensive experiments on the VG and the PSG datasets show that our model achieves state-of-the-art results showing a significant improvement of 3.5% and 6.7% in mR@50 and mR@100 for the scene-graph generation task and achieves an even more substantial improvement of 8.5% and 10.3% in mR@50 and mR@100 for the panoptic scene graph generation task. Code is available at https://github.com/zeeshanhayder/DSGG.

Implicit neural representations for video (NeRV) have recently become a novel way for high-quality video representation. However existing works employ a single network to represent the entire video which implicitly confuse static and dynamic information. This leads to an inability to effectively compress the redundant static information and lack the explicitly modeling of global temporal-coherent dynamic details. To solve above problems we propose DS-NeRV which decomposes videos into sparse learnable static codes and dynamic codes without the need for explicit optical flow or residual supervision. By setting different sampling rates for two codes and applying weighted sum and interpolation sampling methods DS-NeRV efficiently utilizes redundant static information while maintaining high-frequency details. Additionally we design a cross-channel attention-based (CCA) fusion module to efficiently fuse these two codes for frame decoding. Our approach achieves a high quality reconstruction of 31.2 PSNR with only 0.35M parameters thanks to separate static and dynamic codes representation and outperforms existing NeRV methods in many downstream tasks. Our project website is at https://haoyan14.github.io/DS-NeRV.

We present DRESS a large vision language model (LVLM) that innovatively exploits Natural Language feedback (NLF) from Large Language Models to enhance its alignment and interactions by addressing two key limitations in the state-of-the-art LVLMs. First prior LVLMs generally rely only on the instruction finetuning stage to enhance alignment with human preferences. Without incorporating extra feedback they are still prone to generate unhelpful hallucinated or harmful responses. Second while the visual instruction tuning data is generally structured in a multi-turn dialogue format the connections and dependencies among consecutive conversational turns are weak. This reduces the capacity for effective multi-turn interactions. To tackle these we propose a novel categorization of the NLF into two key types: critique and refinement. The critique NLF identifies the strengths and weaknesses of the responses and is used to align the LVLMs with human preferences. The refinement NLF offers concrete suggestions for improvement and is adopted to improve the interaction ability of the LVLMs-- which focuses on LVLMs' ability to refine responses by incorporating feedback in multi-turn interactions. To address the non-differentiable nature of NLF we generalize conditional reinforcement learning for training. Our experimental results demonstrate that DRESS can generate more helpful (9.76%) honest (11.52%) and harmless (21.03%) responses and more effectively learn from feedback during multi-turn interactions compared to SOTA LVLMs.

We present DREAM a novel training framework representing Diffusion Rectification and Estimation-Adaptive Models requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models. DREAM features two components: diffusion rectification which adjusts training to reflect the sampling process and estimation adaptation which balances perception against distortion. When applied to image super-resolution (SR) DREAM adeptly navigates the tradeoff between minimizing distortion and preserving high image quality. Experiments demonstrate DREAM's superiority over standard diffusion-based SR methods showing a to faster training convergence and a to reduction in necessary sampling steps to achieve comparable or superior results. We hope DREAM will inspire a rethinking of diffusion model training paradigms.

The recovery of occluded human meshes poses challenges for current methods due to the difficulty in extracting effective image features under severe occlusion. In this paper we introduce DPMesh an innovative framework for occluded human mesh recovery that capitalizes on the profound knowledge about object structure and spatial relationships embedded in a pre-trained text-to-image diffusion model. Unlike previous methods reliant on conventional backbones for vanilla feature extraction DPMesh seamlessly integrates the pre-trained denoising U-Net with potent priors as its image backbone and performs a single-step inference to provide occlusion-aware information. To enhance the perception capability for occluded poses DPMesh incorporates judicious guidance via condition injection which produces effective controls from 2D observations for the denoising U-Net. Furthermore we explore a dedicated noisy key-point reasoning approach to mitigate disturbances arising from occlusion and crowded scenarios. This strategy fully unleashes the perceptual capability of the diffusion prior thereby enhancing accuracy. Extensive quantitative and qualitative experiments affirm the efficacy of our framework as we outperform state-of-the-art methods on both occlusion-specific and standard datasets underscoring its ability to achieve precise and robust 3D human mesh recovery particularly in challenging scenarios involving occlusion and crowded scenes. Code is available at https://github.com/EternalEvan/DPMesh.

We introduce Diffusion Parametric Head Models (DPHMs) a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences. While recent volumetric head models such as NPHMs can now excel in representing high-fidelity head geometries tracking and reconstructing heads from real-world single-view depth sequences remains very challenging as the fitting to partial and noisy observations is underconstrained. To tackle these challenges we propose a latent diffusion-based prior to regularize volumetric head reconstruction and tracking. This prior-based regularizer effectively constrains the identity and expression codes to lie on the underlying latent manifold which represents plausible head shapes. To evaluate the effectiveness of the diffusion-based prior we collect a dataset of monocular Kinect sequences consisting of various complex facial expression motions and rapid transitions. We compare our method to state-of-the-art tracking methods and demonstrate improved head identity reconstruction as well as robust expression tracking.