CVPR 2024 Papers

Vector-Quantized Image Modeling (VQIM) is a fundamental research problem in image synthesis which aims to represent an image with a discrete token sequence. Existing studies effectively address this problem by learning a discrete codebook from scratch and in a code-independent manner to quantize continuous representations into discrete tokens. However learning a codebook from scratch and in a code-independent manner is highly challenging which may be a key reason causing codebook collapse i.e. some code vectors can rarely be optimized without regard to the relationship between codes and good codebook priors such that die off finally. In this paper inspired by pretrained language models we find that these language models have actually pretrained a superior codebook via a large number of text corpus but such information is rarely exploited in VQIM. To this end we propose a novel codebook transfer framework with part-of-speech called VQCT which aims to transfer a well-trained codebook from pretrained language models to VQIM for robust codebook learning. Specifically we first introduce a pretrained codebook from language models and part-of-speech knowledge as priors. Then we construct a vision-related codebook with these priors for achieving codebook transfer. Finally a novel codebook transfer network is designed to exploit abundant semantic relationships between codes contained in pretrained codebooks for robust VQIM codebook learning. Experimental results on four datasets show that our VQCT method achieves superior VQIM performance over previous state-of-the-art methods.

Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing methods simply align the person appearance to the target pose they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image. In this paper we propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for PGPIS. In the absence of image-caption pairs and textual prompts we develop a novel training paradigm purely based on images to control the generation process of a pre-trained text-to-image diffusion model. A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt. This allows for the decoupling of fine-grained appearance and pose information controls at different stages and thus circumventing the potential overfitting problem. To generate more realistic texture details a hybrid-granularity attention module is proposed to encode multi-scale fine-grained appearance features as bias terms to augment the coarse-grained prompt. Both quantitative and qualitative experimental results on the DeepFashion benchmark demonstrate the superiority of our method over the state of the arts for PGPIS. Code is available at https://github.com/YanzuoLu/CFLD.

Existing super-resolution (SR) models primarily focus on restoring local texture details often neglecting the global semantic information within the scene. This oversight can lead to the omission of crucial semantic details or the introduction of inaccurate textures during the recovery process. In our work we introduce the Cognitive Super-Resolution (CoSeR) framework empowering SR models with the capacity to comprehend low-resolution images. We achieve this by marrying image appearance and language understanding to generate a cognitive embedding which not only activates prior information from large text-to-image diffusion models but also facilitates the generation of high-quality reference images to optimize the SR process. To further improve image fidelity we propose a novel condition injection scheme called "All-in-Attention" consolidating all conditional information into a single module. Consequently our method successfully restores semantically correct and photorealistic details demonstrating state-of-the-art performance across multiple benchmarks. Project page: https://coser-main.github.io/

Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks such as image enhancement restoration editing and compositing. However their widespread adoption is hindered by the high computational cost which limits their real-time application. To address this challenge we introduce a novel method dubbed CoDi that adapts a pre-trained latent diffusion model to accept additional image conditioning inputs while significantly reducing the sampling steps required to achieve high-quality results. Our method can leverage architectures such as ControlNet to incorporate conditioning inputs without compromising the model's prior knowledge gained during large scale pre-training. Additionally a conditional consistency loss enforces consistent predictions across diffusion steps effectively compelling the model to generate high-quality images with conditions in a few steps. Our conditional-task learning and distillation approach outperforms previous distillation methods achieving a new state-of-the-art in producing high-quality images with very few steps (e.g. 1-4) across multiple tasks including super-resolution text-guided image editing and depth-to-image generation.

We present CoDi-2 a Multimodal Large Language Model (MLLM) for learning in-context interleaved multimodal representations. By aligning modalities with language for both encoding and generation CoDi-2 empowers Large Language Models (LLMs) to understand modality-interleaved instructions and in-context examples and autoregressively generate grounded and coherent multimodal outputs in an any-to-any input-output modality paradigm. To train CoDi-2 we build a large-scale generation dataset encompassing in-context multimodal instructions across text vision and audio. CoDi-2 demonstrates a wide range of zero-shot and few-shot capabilities for tasks like editing exemplar learning composition reasoning etc. CoDi-2 surpasses previous domain-specific models on tasks such as subject-driven image generation vision transformation and audio editing and showcases a significant advancement for integrating diverse multimodal tasks with sequential generation.

The performance of image restoration (IR) is highly dependent on the reconstruction quality of diverse contents with varying complexity. However most IR approaches model the mapping between various complexity contents of inputs and outputs through the repeated feature calculation propagation mechanism in a unified pipeline which leads to unsatisfactory results. To address this issue we propose an explicit Content Decoupling framework for IR dubbed CoDe to end-to-end model the restoration process by utilizing decoupled content components in a divide-and-conquer-like architecture. Specifically a Content Decoupling Module is first designed to decouple content components of inputs and outputs according to the frequency spectra adaptively generated from the transform domain. In addition in order to harness the divide-and-conquer strategy for reconstructing decoupled content components we propose an IR Network Container. It contains an optimized version which is a streamlining of an arbitrary IR network comprising the cascaded modulated subnets and a Reconstruction Layers Pool. Finally a Content Consistency Loss is designed from the transform domain perspective to supervise the restoration process of each content component and further guide the feature fusion process. Extensive experiments on several IR tasks such as image super-resolution image denoising and image blurring covering both real and synthetic settings demonstrate that the proposed paradigm can effectively take the performance of the original network to a new state-of-the-art level in multiple benchmark datasets (e.g. 0.34dB@Set5 x4 over DAT).

Prominent solutions for medical image segmentation are typically tailored for automatic or interactive setups posing challenges in facilitating progress achieved in one task to another. This also necessitates separate models for each task duplicating both training time and parameters. To address above issues we introduce S2VNet a universal framework that leverages Slice-to-Volume propagation to unify automatic/interactive segmentation within a single model and one training session. Inspired by clustering-based segmentation techniques S2VNet makes full use of the slice-wise structure of volumetric data by initializing cluster centers from the cluster results of previous slice. This enables knowledge acquired from prior slices to assist in the segmentation of the current slice further efficiently bridging the communication between remote slices using mere 2D networks. Moreover such a framework readily accommodates inter- active segmentation with no architectural change simply by initializing centroids from user inputs. S2VNet distinguishes itself by swift inference speeds and reduced memory consumption compared to prevailing 3D solutions. It can also handle multi-class interactions with each of them serving to initialize different centroids. Experiments on three benchmarks demonstrate S2VNet surpasses task-specified solutions on both automatic/interactive setups.

This work aims to improve the efficiency of text-to-image diffusion models. While diffusion models use computationally expensive UNet-based denoising operations in every generation step we identify that not all operations are equally relevant for the final output quality. In particular we observe that UNet layers operating on high-res feature maps are relatively sensitive to small perturbations. In contrast low-res feature maps influence the semantic layout of the final image and can often be perturbed with no noticeable change in the output. Based on this observation we propose Clockwork Diffusion a method that periodically reuses computation from preceding denoising steps to approximate low-res feature maps at one or more subsequent steps. For multiple base- lines and for both text-to-image generation and image editing we demonstrate that Clockwork leads to comparable or improved perceptual scores with drastically reduced computational complexity. As an example for Stable Diffusion v1.5 with 8 DPM++ steps we save 32% of FLOPs with negligible FID and CLIP change. We re- lease code at https://github.com/Qualcomm-AI-research/clockwork-diffusion

In this paper we present an empirical study on image recognition unfairness i.e. extreme class accuracy disparity on balanced data like ImageNet. We demonstrate that classes are not equal and unfairness is prevalent for image classification models across various datasets network architectures and model capacities. Moreover several intriguing properties of fairness are identified. First the unfairness lies in problematic representation rather than classifier bias distinguished from long-tailed recognition. Second with the proposed concept of Model Prediction Bias we investigate the origins of problematic representation during training optimization. Our findings reveal that models tend to exhibit greater prediction biases for classes that are more challenging to recognize. It means that more other classes will be confused with harder classes. Then the False Positives (FPs) will dominate the learning in optimization thus leading to their poor accuracy. Further we conclude that data augmentation and representation learning algorithms improve overall performance by promoting fairness to some degree in image classification.

Diffusion models have recently achieved remarkable progress in generating realistic images. However challenges remain in accurately understanding and synthesizing the layout requirements in the textual prompts. To align the generated image with layout instructions we present a training-free layout calibration system SimM that intervenes in the generative process on the fly during inference time. Specifically following a "check-locate-rectify" pipeline the system first analyses the prompt to generate the target layout and compares it with the intermediate outputs to automatically detect errors. Then by moving the located activations and making intra- and inter-map adjustments the rectification process can be performed with negligible computational overhead. To evaluate SimM over a range of layout requirements we present a benchmark SimMBench that compensates for the lack of superlative spatial relations in existing datasets. And both quantitative and qualitative results demonstrate the effectiveness of the proposed SimM in calibrating the layout inconsistencies. Our project page is at https://simm-t2i.github.io/SimM.

Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversations. However existing methods encounter challenges in effectively handling both image and video understanding particularly with limited visual tokens. In this work we introduce Chat-UniVi a Unified Vision-language model capable of comprehending and engaging in conversations involving images and videos through a unified visual representation. Specifically we employ a set of dynamic visual tokens to uniformly represent images and videos. This representation framework empowers the model to efficiently utilize a limited number of visual tokens to simultaneously capture the spatial details necessary for images and the comprehensive temporal relationship required for videos. Moreover we leverage a multi-scale representation enabling the model to perceive both high-level semantic concepts and low-level visual details. Notably Chat-UniVi is trained on a mixed dataset containing both images and videos allowing direct application to tasks involving both mediums without requiring any modifications. Extensive experimental results demonstrate that Chat-UniVi consistently outperforms even existing methods exclusively designed for either images or videos. Code is available at https://github.com/PKU-YuanGroup/Chat-UniVi.

The rapidly growing scale of data in practice poses demands on the efficiency of retrieval models. However for fine-grained image retrieval task there are inherent contradictions in the design of hashing based efficient models. Firstly the limited information embedding capacity of low-dimensional binary hash codes coupled with the detailed information required to describe fine-grained categories results in a contradiction in feature learning. Secondly there is also a contradiction between the complexity of fine-grained feature extraction models and retrieval efficiency. To address these issues in this paper we propose the characteristics matching based hash codes generation method. Coupled with the cross-layer semantic information transfer module and the multi-region feature embedding module the proposed method can generate hash codes that effectively capture fine-grained differences among samples while ensuring efficient inference. Extensive experiments on widely used datasets demonstrate that our method can significantly outperform state-of-the-art methods.

While Multi-modal Language Models (MLMs) demon strate impressive multimodal ability they still struggle on providing factual and precise responses for tasks like vi sual question answering (VQA). In this paper we address this challenge from the perspective of contextual informa tion. We propose Causal Context Generation Causal-CoG which is a prompting strategy that engages contextual infor mation to enhance precise VQA during inference. Specifi cally we prompt MLMs to generate contexts i.e text de scription of an image and engage the generated contexts for question answering. Moreover we investigate the ad vantage of contexts on VQA from a causality perspective introducing causality filtering to select samples for which contextual information is helpful. To show the effective ness of Causal-CoG we run extensive experiments on 10 multimodal benchmarks and showconsistent improvements e.g. +6.30% on POPE +13.69% on Vizwiz and +6.43% on VQAv2 compared to direct decoding surpassing exist ing methods. We hope Casual-CoG inspires explorations of context knowledge in multimodal models and serves as a plug-and-play strategy for MLM decoding.

Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success but suffer from excessive noise. Recent studies use alternative captions synthesized by captioning models and have achieved notable benchmark performance. However our experiments reveal significant Scalability Deficiency and World Knowledge Loss issues in models trained with synthetic captions which have been largely obscured by their initial benchmark success. Upon closer examination we identify the root cause as the overly-simplified language structure and lack of knowledge details in existing synthetic captions. To provide higher-quality and more scalable multimodal pretraining data we propose CapsFusion an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions. Extensive experiments show that CapsFusion captions exhibit remarkable all-round superiority over existing captions in terms of model performance (e.g. 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps) sample efficiency (requiring 11-16 times less computation than baselines) world knowledge depth and scalability. These effectiveness efficiency and scalability advantages position CapsFusion as a promising candidate for future scaling of LMM training.

We concentrate on a novel human-centric image synthesis task that is given only one reference facial photograph it is expected to generate specific individual images with diverse head positions poses facial expressions and illuminations in different contexts. To accomplish this goal we argue that our generative model should be capable of the following favorable characteristics: (1) a strong visual and semantic understanding of our world and human society for basic object and human image generation. (2) generalizable identity preservation ability. (3) flexible and fine-grained head control. Recently large pre-trained text-to-image diffusion models have shown remarkable results serving as a powerful generative foundation. As a basis we aim to unleash the above two capabilities of the pre-trained model. In this work we present a new framework named CapHuman. We embrace the "encode then learn to align" paradigm which enables generalizable identity preservation for new individuals without cumbersome tuning at inference. CapHuman encodes identity features and then learns to align them into the latent space. Moreover we introduce the 3D facial prior to equip our model with control over the human head in a flexible and 3D-consistent manner. Extensive qualitative and quantitative analyses demonstrate our CapHuman can produce well-identity-preserved photo-realistic and high-fidelity portraits with content-rich representations and various head renditions superior to established baselines. Code and checkpoint will be released at https://github.com/VamosC/CapHuman.

Diffusion models have recently revolutionized the field of image synthesis due to their ability to generate photorealistic images. However one of the major drawbacks of diffusion models is that the image generation process is costly. A large image-to-image network has to be applied many times to iteratively refine an image from random noise. While many recent works propose techniques to reduce the number of required steps they generally treat the underlying denoising network as a black box. In this work we investigate the behavior of the layers within the network and find that 1) the layers' output changes smoothly over time 2) the layers show distinct patterns of change and 3) the change from step to step is often very small. We hypothesize that many layer computations in the denoising network are redundant. Leveraging this we introduce Block Caching in which we reuse outputs from layer blocks of previous steps to speed up inference. Furthermore we propose a technique to automatically determine caching schedules based on each block's changes over timesteps. In our experiments we show through FID human evaluation and qualitative analysis that Block Caching allows to generate images with higher visual quality at the same computational cost. We demonstrate this for different state-of-the-art models (LDM and EMU) and solvers (DDIM and DPM).

Neural Radiance Fields (NeRF) have shown impressive capabilities for photorealistic novel view synthesis when trained on dense inputs. However when trained on sparse inputs NeRF typically encounters issues of incorrect density or color predictions mainly due to insufficient coverage of the scene causing partial and sparse supervision thus leading to significant performance degradation. While existing works mainly consider ray-level consistency to construct 2D learning regularization based on rendered color depth or semantics on image planes in this paper we propose a novel approach that models 3D spatial field consistency to improve NeRF's performance with sparse inputs. Specifically we first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray which are then incorporated into the volume rendering. By backpropagating through the rendering loss we enhance the consistency among neighboring points. Additionally we propose to use a contrastive loss on the encoder output of the Transformer to further improve consistency within each voxel. Experiments demonstrate that our method yields significant improvement over different radiance fields in the sparse inputs setting and achieves comparable performance with current works. The project page for this paper is available at https://zhongyingji.github.io/CVT-xRF.

Retrieval Augmented Generation (RAG) is emerging as a flexible and robust technique to adapt models to private users data without training to handle credit attribution and to allow efficient machine unlearning at scale. However RAG techniques for image generation may lead to parts of the retrieved samples being copied in the model's output. To reduce risks of leaking private information contained in the retrieved set we introduce Copy-Protected generation with Retrieval (CPR) a new method for RAG with strong copyright protection guarantees in a mixed-private setting for diffusion models. CPR allows to condition the output of diffusion models on a set of retrieved images while also guaranteeing that unique identifiable information about those example is not exposed in the generated outputs. In particular it does so by sampling from a mixture of public (safe) distribution and private (user) distribution by merging their diffusion scores at inference. We prove that CPR satisfies Near Access Freeness (NAF) which bounds the amount of information an attacker may be able to extract from the generated images. We provide two algorithms for copyright protection CPR-KL and CPR-Choose. Unlike previously proposed rejection-sampling-based NAF methods our methods enable efficient copyright-protected sampling with a single run of backward diffusion. We show that our method can be applied to any pre-trained conditional diffusion model such as Stable Diffusion or unCLIP. In particular we empirically show that applying CPR on top of un- CLIP improves quality and text-to-image alignment of the generated results (81.4 to 83.17 on TIFA benchmark) while enabling credit attribution copy-right protection and deterministic constant time unlearning.

Novel view synthesis is attractive for social media but it often contains unwanted details such as personal information that needs to be edited out for a better experience. Multiplane image (MPI) is desirable for social media because of its generality but it is complex and computationally expensive making object removal challenging. To address these challenges we propose CORE-MPI which employs embedding images to improve the consistency and accessibility of MPI object removal. CORE-MPI allows for real-time transmission and interaction with embedding images on social media facilitating object removal with a single mask. However recovering the geometric information hidden in the embedding images is a significant challenge. Therefore we propose a dual-network approach where one network focuses on color restoration and the other on inpainting the embedding image including geometric information. For the training of CORE-MPI we introduce a pseudo-reference loss aimed at proficient color recovery even in complex scenes or with large masks. Furthermore we present a disparity consistency loss to preserve the geometric consistency of the inpainted region. We demonstrate the effectiveness of CORE-MPI on RealEstate10K and UCSD datasets.