CVPR 2024 论文列表

Diffusion models have recently gained unprecedented attention in the field of image synthesis due to their remarkable generative capabilities. Notwithstanding their prowess these models often incur substantial computational costs primarily attributed to the sequential denoising process and cumbersome model size. Traditional methods for compressing diffusion models typically involve extensive retraining presenting cost and feasibility challenges. In this paper we introduce DeepCache a novel training-free paradigm that accelerates diffusion models from the perspective of model architecture. DeepCache capitalizes on the inherent temporal redundancy observed in the sequential denoising steps of diffusion models which caches and retrieves features across adjacent denoising stages thereby curtailing redundant computations. Utilizing the property of the U-Net we reuse the high-level features while updating the low-level features in a very cheap way. This innovative strategy in turn enables a speedup factor of 2.3xfor Stable Diffusion v1.5 with only a 0.05 decline in CLIP Score and 4.1xfor LDM-4-G with a slight decrease of 0.22 in FID on ImageNet. Our experiments also demonstrate DeepCache's superiority over existing pruning and distillation methods that necessitate retraining and its compatibility with current sampling techniques. Furthermore we find that under the same throughput DeepCache effectively achieves comparable or even marginally improved results with DDIM or PLMS.

To insert Trojan into a Deep Neural Network (DNN) the existing attack assumes the attacker can access the victim's training facilities. However a realistic threat model was recently developed by leveraging memory fault to inject Trojans at the inference stage. In this work we develop a novel Trojan attack by adopting a unique memory fault injection technique that can inject bit-flip into the page table of the main memory. In the main memory each weight block consists of a group of weights located at a specific address of a DRAM row. A bit-flip in the page frame number replaces a target weight block of a DNN model with another replacement weight block. To develop a successful Trojan attack leveraging this unique fault model the attacker must solve three key challenges: i) how to identify a minimum set of target weight blocks to be modified? ii) how to identify the corresponding optimal replacement weight block? iii) how to optimize the trigger to maximize the attacker's objective given a target and replacement weight block set? We address them by proposing a novel Deep-TROJ attack algorithm that can identify a minimum set of vulnerable target and corresponding replacement weight blocks while optimizing the trigger at the same time. We evaluate the performance of our proposed Deep-TROJ on CIFAR-10 CIFAR-100 and ImageNet dataset for sixteen different DNN architectures including vision transformers. Proposed Deep-TROJ is the most successful one to date that does not require access to training facilities while successfully bypassing the existing defenses. Our code is available at https://github.com/ML-Security-Research-LAB/Deep-TROJ.

Inverse tone mapping (ITM) aims to reconstruct high dynamic range (HDR) radiance from low dynamic range (LDR) content. Although many deep image ITM methods can generate impressive results the field of video ITM is still to be explored. Processing video sequences by image ITM methods may cause temporal inconsistency. Besides they aren't able to exploit the potentially useful information in the temporal domain. In this paper we analyze the process of video filming and then propose a Global Sample and Local Propagate strategy to better find and utilize temporal clues. To better realize the proposed strategy we design a two-stage pipeline which includes modules named Incremental Clue Aggregation Module and Feature and Clue Propagation Module. They can align and fuse frames effectively under the condition of brightness changes and propagate features and temporal clues to all frames efficiently. Our temporal clues based video ITM method can recover realistic and temporal consistent results with high fidelity in over-exposed regions. Qualitative and quantitative experiments on public datasets show that the proposed method has significant advantages over existing methods.

A Manhattan world lying along cuboid buildings is useful for camera angle estimation. However accurate and robust angle estimation from fisheye images in the Manhattan world has remained an open challenge because general scene images tend to lack constraints such as lines arcs and vanishing points. To achieve higher accuracy and robustness we propose a learning-based calibration method that uses heatmap regression which is similar to pose estimation using keypoints to detect the directions of labeled image coordinates. Simultaneously our two estimators recover the rotation and remove fisheye distortion by remapping from a general scene image. Without considering vanishing-point constraints we find that additional points for learning-based methods can be defined. To compensate for the lack of vanishing points in images we introduce auxiliary diagonal points that have the optimal 3D arrangement of spatial uniformity. Extensive experiments demonstrated that our method outperforms conventional methods on large-scale datasets and with off-the-shelf cameras.

Regression tasks in computer vision such as age estimation or counting are often formulated into classification by quantizing the target space into classes. Yet real-world data is often imbalanced -- the majority of training samples lie in a head range of target values while a minority of samples span a usually larger tail range. By selecting the class quantization one can adjust imbalanced regression targets into balanced classification outputs though there are trade-offs in balancing classification accuracy and quantization error. To improve regression performance over the entire range of data we propose to construct hierarchical classifiers for solving imbalanced regression tasks. The fine-grained classifiers limit the quantization error while being modulated by the coarse predictions to ensure high accuracy. Standard hierarchical classification approaches when applied to the regression problem fail to ensure that predicted ranges remain consistent across the hierarchy. As such we propose a range-preserving distillation process that effectively learns a single classifier from the set of hierarchical classifiers. Our novel hierarchical classification adjustment (HCA) for imbalanced regression shows superior results on three diverse tasks: age estimation crowd counting and depth estimation. Code is available at https://github.com/xhp-hust-2018-2011/HCA.

In this paper we propose Image Downscaling Assessment by Rate-Distortion (IDA-RD) a novel measure to quantitatively evaluate image downscaling algorithms. In contrast to image-based methods that measure the quality of downscaled images ours is process-based that draws ideas from rate-distortion theory to measure the distortion incurred during downscaling. Our main idea is that downscaling and super-resolution (SR) can be viewed as the encoding and decoding processes in the rate-distortion model respectively and that a downscaling algorithm that preserves more details in the resulting low-resolution (LR) images should lead to less distorted high-resolution (HR) images in SR. In other words the distortion should increase as the downscaling algorithm deteriorates. However it is non-trivial to measure this distortion as it requires the SR algorithm to be blind and stochastic. Our key insight is that such requirements can be met by recent SR algorithms based on deep generative models that can find all matching HR images for a given LR image on their learned image manifolds. Extensive experimental results show the effectiveness of our IDA-RD measure.

Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images achieving promising performance. Due to the inherent property of diffusion models most existing methods need long serial sampling chains to restore HQ images step-by-step resulting in expensive sampling time and high computation costs. Moreover such long sampling chains hinder understanding the relationship between inputs and restoration results since it is hard to compute the gradients in the whole chains. In this work we aim to rethink the diffusion model-based IR models through a different perspective i.e. a deep equilibrium (DEQ) fixed point system called DeqIR. Specifically we derive an analytical solution by modeling the entire sampling chain in these IR models as a joint multivariate fixed point system. Based on the analytical solution we can conduct parallel sampling and restore HQ images without training. Furthermore we compute fast gradients via DEQ inversion and found that initialization optimization can boost image quality and control the generation direction. Extensive experiments on benchmarks demonstrate the effectiveness of our method on typical IR tasks and real-world settings.

Referring video segmentation relies on natural language expressions to identify and segment objects often emphasizing motion clues. Previous works treat a sentence as a whole and directly perform identification at the video-level mixing up static image-level cues with temporal motion cues. However image-level features cannot well comprehend motion cues in sentences and static cues are not crucial for temporal perception. In fact static cues can sometimes interfere with temporal perception by overshadowing motion cues. In this work we propose to decouple video-level referring expression understanding into static and motion perception with a specific emphasis on enhancing temporal comprehension. Firstly we introduce an expression-decoupling module to make static cues and motion cues perform their distinct role alleviating the issue of sentence embeddings overlooking motion cues. Secondly we propose a hierarchical motion perception module to capture temporal information effectively across varying timescales. Furthermore we employ contrastive learning to distinguish the motions of visually similar objects. These contributions yield state-of-the-art performance across five datasets including a remarkable 9.2% J&F improvement on the challenging MeViS dataset.

We delve into pseudo-labeling for semi-supervised monocular 3D object detection (SSM3OD) and discover two primary issues: a misalignment between the prediction quality of 3D and 2D attributes and the tendency of depth supervision derived from pseudo-labels to be noisy leading to significant optimization conflicts with other reliable forms of supervision. To tackle these issues we introduce a novel decoupled pseudo-labeling (DPL) approach for SSM3OD. Our approach features a Decoupled Pseudo-label Generation (DPG) module designed to efficiently generate pseudo-labels by separately processing 2D and 3D attributes. This module incorporates a unique homography-based method for identifying dependable pseudo-labels in Bird's Eye View (BEV) space specifically for 3D attributes. Additionally we present a Depth Gradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels effectively decoupling the depth gradient and removing conflicting gradients. This dual decoupling strategy--at both the pseudo-label generation and gradient levels--significantly improves the utilization of pseudo-labels in SSM3OD. Our comprehensive experiments on the KITTI benchmark demonstrate the superiority of our method over existing approaches.

Accurate data association is crucial in reducing confusion such as ID switches and assignment errors in multi-object tracking (MOT). However existing advanced methods often overlook the diversity among trajectories and the ambiguity and conflicts present in motion and appearance cues leading to confusion among detections trajectories and associations when performing simple global data association. To address this issue we propose a simple versatile and highly interpretable data association approach called Decomposed Data Association (DDA). DDA decomposes the traditional association problem into multiple sub-problems using a series of non-learning-based modules and selectively addresses the confusion in each sub-problem by incorporating targeted exploitation of new cues. Additionally we introduce Occlusion-aware Non-Maximum Suppression (ONMS) to retain more occluded detections thereby increasing opportunities for association with trajectories and indirectly reducing the confusion caused by missed detections. Finally based on DDA and ONMS we design a powerful multi-object tracker named DeconfuseTrack specifically focused on resolving confusion in MOT. Extensive experiments conducted on the MOT17 and MOT20 datasets demonstrate that our proposed DDA and ONMS significantly enhance the performance of several popular trackers. Moreover DeconfuseTrack achieves state-of-the-art performance on the MOT17 and MOT20 test sets significantly outperforms the baseline tracker ByteTrack in metrics such as HOTA IDF1 AssA. This validates that our tracking design effectively reduces confusion caused by simple global association.

Medical vision language pre-training (VLP) has emerged as a frontier of research enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease. Due to the complex semantics of biomedical texts current methods struggle to align medical images with key pathological findings in unstructured reports. This leads to the misalignment with the target disease's textual representation. In this paper we introduce a novel VLP framework designed to dissect disease descriptions into their fundamental aspects leveraging prior knowledge about the visual manifestations of pathologies. This is achieved by consulting a large language model and medical experts. Integrating a Transformer module our approach aligns an input image with the diverse elements of a disease generating aspect-centric image representations. By consolidating the matches from each aspect we improve the compatibility between an image and its associated disease. Additionally capitalizing on the aspect-oriented representations we present a dual-head Transformer tailored to process known and unknown diseases optimizing the comprehensive detection efficacy. Conducting experiments on seven downstream datasets ours improves the accuracy of recent methods by up to 8.56% and 17.26% for seen and unseen categories respectively. Our code is released at https://github.com/HieuPhan33/MAVL.

While standard Empirical Risk Minimization (ERM) training is proven effective for image classification on in-distribution data it fails to perform well on out-of-distribution samples. One of the main sources of distribution shift for image classification is the compositional nature of images. Specifically in addition to the main object or component(s) determining the label some other image components usually exist which may lead to the shift of input distribution between train and test environments. More importantly these components may have spurious correlations with the label. To address this issue we propose Decompose-and-Compose (DaC) which improves robustness to correlation shift by a compositional approach based on combining elements of images. Based on our observations models trained with ERM usually highly attend to either the causal components or the components having a high spurious correlation with the label (especially in datapoints on which models have a high confidence). In fact according to the amount of spurious correlation and the easiness of classification based on the causal or non-causal components the model usually attends to one of these more (on samples with high confidence). Following this we first try to identify the causal components of images using class activation maps of models trained with ERM. Afterward we intervene on images by combining them and retraining the model on the augmented data including the counterfactual ones. This work proposes a group-balancing method by intervening on images without requiring group labels or information regarding the spurious features during training. The method has an overall better worst group accuracy compared to previous methods with the same amount of supervision on the group labels in correlation shift. Our code is available at https://github.com/fhn98/DaC.

Personalized Federated Learning (PFL) is proposed to find the greatest personalized models for each client. To avoid the central failure and communication bottleneck in the server-based FL we concentrate on the Decentralized Personalized Federated Learning (DPFL) that performs distributed model training in a Peer-to-Peer (P2P) manner. Most personalized works in DPFL are based on undirected and symmetric topologies however the data computation and communication resources heterogeneity result in large variances in the personalized models which lead the undirected aggregation to suboptimal personalized performance and unguaranteed convergence. To address these issues we propose a directed collaboration DPFL framework by incorporating stochastic gradient push and partial model personalized called Decentralized Federated Partial Gradient Push (DFedPGP). It personalizes the linear classifier in the modern deep model to customize the local solution and learns a consensus representation in a fully decentralized manner. Clients only share gradients with a subset of neighbors based on the directed and asymmetric topologies which guarantees flexible choices for resource efficiency and better convergence. Theoretically we show that the proposed DFedPGP achieves a superior convergence rate of O(1/?T) in the general non-convex setting and tighter connectivity among clients will speed up the convergence. The proposed method achieves state-of-the-art (SOTA) accuracy in both data and computation heterogeneity scenarios demonstrating the efficiency of the directed collaboration and partial gradient push.

Two-view correspondence learning has recently focused on considering the coherence and smoothness of the motion field between an image pair. Dominant schemes include controlling the complexity of the field function with regularization or smoothing the field with local filters but the former suffers from heavy computational burden and the latter fails to accommodate discontinuities in the case of large scene disparities. In this paper inspired by Fourier expansion we propose a novel network called DeMatch which decomposes the motion field to retain its main "low-frequency" and smooth part. This achieves implicit regularization with lower computational cost and generates piecewise smoothness naturally. Specifically we first decompose the rough motion field that is contaminated by false matches into several different sub-fields which are highly smooth and contain the main energy of the original field. Then with these smooth sub-fields we recover a cleaner motion field from which correct motion vectors are subsequently derived. We also design a special masked decomposition strategy to further mitigate the negative influence of false matches. All the mentioned processes are finally implemented in a discrete and learnable manner avoiding the difficulty of calculating real dense fields. Extensive experiments reveal that DeMatch outperforms state-of-the-art methods in multiple tasks and shows promising low computational usage and piecewise smoothness property. The code and trained models are publicly available at https://github.com/SuhZhang/DeMatch.

Open-World Few-Shot Learning (OFSL) is a critical field of research concentrating on the precise identification of target samples in environments with scarce data and unreliable labels thus possessing substantial practical significance. Recently the evolution of foundation models like CLIP has revealed their strong capacity for representation even in settings with restricted resources and data. This development has led to a significant shift in focus transitioning from the traditional method of "building models from scratch" to a strategy centered on "efficiently utilizing the capabilities of foundation models to extract relevant prior knowledge tailored for OFSL and apply it judiciously". Amidst this backdrop we unveil the Direct-and-Inverse CLIP (DeIL) an innovative method leveraging our proposed "Direct-and-Inverse" concept to activate CLIP-based methods for addressing OFSL. This concept transforms conventional single-step classification into a nuanced two-stage process: initially filtering out less probable categories followed by accurately determining the specific category of samples. DeIL comprises two key components: a pre-trainer (frozen) for data denoising and an adapter (tunable) for achieving precise final classification. In experiments DeIL achieves SOTA performance on 11 datasets.

In this paper we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. Specifically we first enhance a baseline convolutional depth completion model by applying attention to 2D features in the bottleneck and skip connections. This effectively improves the performance of this simple network and sets it on par with the latest complex transformer-based models. Leveraging the initial depths and features from this network we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it allowing the model to explicitly learn and exploit 3D geometric features. In addition we propose normalization techniques to process the point cloud which improves learning and leads to better accuracy than directly using point transformers off the shelf. Furthermore we incorporate global attention on downsampled point cloud features which enables long-range context while still being computationally feasible. We evaluate our method DeCoTR on established depth completion benchmarks including NYU Depth V2 and KITTI showcasing that it sets new state-of-the-art performance. We further conduct zero-shot evaluations on ScanNet and DDAD benchmarks and demonstrate that DeCoTR has superior generalizability compared to existing approaches.

Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Existing methods commonly avoid relying on private data by utilizing synthetic or sampled data. However a long-overlooked issue is that the severe distribution shifts between their substitution and original data which manifests as huge differences in the quality of images and class proportions. The harmful shifts are essentially the confounder that significantly causes performance bottlenecks. To tackle the issue this paper proposes a novel perspective with causal inference to disentangle the student models from the impact of such shifts. By designing a customized causal graph we first reveal the causalities among the variables in the DFKD task. Subsequently we propose a Knowledge Distillation Causal Intervention (KDCI) framework based on the backdoor adjustment to de-confound the confounder. KDCI can be flexibly combined with most existing state-of-the-art baselines. Experiments in combination with six representative DFKD methods demonstrate the effectiveness of our KDCI which can obviously help existing methods under almost all settings e.g. improving the baseline by up to 15.54% accuracy on the CIFAR-100 dataset.

We demonstrate text as a strong cross-modal interface. Rather than relying on deep embeddings to connect image and language as the interface representation our approach represents an image as text from which we enjoy the interpretability and flexibility inherent to natural language. We employ an autoencoder that uses a pre-trained text-to-image diffusion model for decoding. The encoder is trained to transform an input image into text which is then fed into the fixed text-to-image diffusion decoder to reconstruct the original input a process we term De-Diffusion. Experiments validate both the precision and comprehensiveness of De-Diffusion text representing images such that it can be readily ingested by off-the-shelf text-to-image tools and LLMs for diverse multi-modal tasks. For example a single De-Diffusion model can generalize to provide transferable prompts for different text-to-image tools and also achieves a new state of the art on open-ended vision-language tasks by simply prompting large language models with few-shot examples. Project page: https://dediffusion.github.io/