CVPR 2024 Papers

We propose a simple strategy for masking image patches during visual-language contrastive learning that improves the quality of the learned representations and the training speed. During each iteration of training we randomly mask clusters of visually similar image patches as measured by their raw pixel intensities. This provides an extra learning signal beyond the contrastive training itself since it forces a model to predict words for masked visual structures solely from context. It also speeds up training by reducing the amount of data used in each image. We evaluate the effectiveness of our model by pre-training on a number of benchmarks finding that it outperforms other masking strategies such as FLIP on the quality of the learned representation.

Test-time adaptation with pre-trained vision-language models has attracted increasing attention for tackling distribution shifts during the test time. Though prior studies have achieved very promising performance they involve intensive computation which is severely unaligned with test-time adaptation. We design TDA a training-free dynamic adapter that enables effective and efficient test-time adaptation with vision-language models. TDA works with a lightweight key-value cache that maintains a dynamic queue with few-shot pseudo labels as values and the corresponding test-sample features as keys. Leveraging the key-value cache TDA allows adapting to test data gradually via progressive pseudo label refinement which is super-efficient without incurring any backpropagation. In addition we introduce negative pseudo labeling that alleviates the adverse impact of pseudo label noises by assigning pseudo labels to certain negative classes when the model is uncertain about its pseudo label predictions. Extensive experiments over two benchmarks demonstrate TDA's superior effectiveness and efficiency as compared with the state-of-the-art. The code has been released in https://kdiaaa.github.io/tda/.

In the era of AI-generated content (AIGC) malicious tampering poses imminent threats to copyright integrity and information security. Current deep image watermarking while widely accepted for safeguarding visual content can only protect copyright and ensure traceability. They fall short in localizing increasingly realistic image tampering potentially leading to trust crises privacy violations and legal disputes. To solve this challenge we propose an innovative proactive forensics framework EditGuard to unify copyright protection and tamper-agnostic localization especially for AIGC-based editing methods. It can offer a meticulous embedding of imperceptible watermarks and precise decoding of tampered areas and copyright information. Leveraging our observed fragility and locality of image-into-image steganography the realization of EditGuard can be converted into a united image-bit steganography issue thus completely decoupling the training process from the tampering types. Extensive experiments verify that our EditGuard balances the tamper localization accuracy copyright recovery precision and generalizability to various AIGC-based tampering methods especially for image forgery that is difficult for the naked eye to detect.

In this paper we tackle the task of category-agnostic pose estimation (CAPE) which aims to predict poses for objects of any category with few annotated samples. Previous works either rely on local matching between features of support and query samples or require support keypoint identifier. The former is prone to overfitting due to its sensitivity to sparse samples while the latter is impractical for the open-world nature of the task. To overcome these limitations we propose ESCAPE - a Bayesian framework that learns a prior over the features of keypoints. The prior can be expressed as a mixture of super-keypoints each being a high-level abstract keypoint that captures the statistics of semantically related keypoints from different categories. We estimate the super-keypoints from base categories and use them in adaptation to novel categories. The adaptation to an unseen category involves two steps: first we match each novel keypoint to a related super-keypoint; and second we transfer the knowledge encoded in the matched super-keypoints to the novel keypoints. For the first step we propose a learnable matching network to capture the relationship between the novel keypoints and the super-keypoints resulting in a more reliable matching. ESCAPE mitigates overfitting by directly transferring learned knowledge to novel categories while it does not use keypoint identifiers. We achieve state-of-the-art performance on the standard MP-100 benchmark.

Head avatars animated by visual signals have gained popularity particularly in cross-driving synthesis where the driver differs from the animated character a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model with a particular focus on its latent space for facial expression descriptors and uncover several limitations with its ability to express intense face motions. Head avatars animated by visual signals have gained popularity particularly in cross-driving synthesis where the driver differs from the animated character a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model with a particular focus on its latent space for facial expression descriptors and uncover several limitations with its ability to express intense face motions. To address these limitations we propose substantial changes in both training pipeline and model architecture to introduce our EMOPortraits model where we: Enhance the model's capability to faithfully support intense asymmetric face expressions setting a new state-of-the-art result in the emotion transfer task surpassing previous methods in both metrics and quality. Incorporate speech-driven mode to our model achieving top-tier performance in audio-driven facial animation making it possible to drive source identity through diverse modalities including visual signal audio or a blend of both.Furthermore we propose a novel multi-view video dataset featuring a wide range of intense and asymmetric facial expressions filling the gap with absence of such data in existing datasets.

An efficient and effective decoding mechanism is crucial in medical image segmentation especially in scenarios with limited computational resources. However these decoding mechanisms usually come with high computational costs. To address this concern we introduce EMCAD a new efficient multi-scale convolutional attention decoder designed to optimize both performance and computational efficiency. EMCAD leverages a unique multi-scale depth-wise convolution block significantly enhancing feature maps through multi-scale convolutions. EMCAD also employs channel spatial and grouped (large-kernel) gated attention mechanisms which are highly effective at capturing intricate spatial relationships while focusing on salient regions. By employing group and depth-wise convolution EMCAD is very efficient and scales well (e.g. only 1.91M parameters and 0.381G FLOPs are needed when using a standard encoder). Our rigorous evaluations across 12 datasets that belong to six medical image segmentation tasks reveal that EMCAD achieves state-of-the-art (SOTA) performance with 79.4% and 80.3% reduction in #Params and #FLOPs respectively. Moreover EMCAD's adaptability to different encoders and versatility across segmentation tasks further establish EMCAD as a promising tool advancing the field towards more efficient and accurate medical image analysis. Our implementation is available at https://github.com/SLDGroup/EMCAD.

Text-to-image (T2I) diffusion models notably the unCLIP models (e.g. DALL-E-2) achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks at the cost of significant computational resources. The unCLIP stack comprises T2I prior and diffusion image decoder. The T2I prior model alone adds a billion parameters compared to the Latent Diffusion Models which increases the computational and high-quality data requirements. We introduce ECLIPSE a novel contrastive learning method that is both parameter and data-efficient. ECLIPSE leverages pre-trained vision-language models (e.g. CLIP) to distill the knowledge into the prior model. We demonstrate that the ECLIPSE trained prior with only 3.3% of the parameters and trained on a mere 2.8% of the data surpasses the baseline T2I priors with an average of 71.6% preference score under resource-limited setting. It also attains performance on par with SOTA big models achieving an average of 63.36% preference score in terms of the ability to follow the text compositions. Extensive experiments on two unCLIP diffusion image decoders Karlo and Kandinsky affirm that ECLIPSE priors consistently deliver high performance while significantly reducing resource dependency. Project page: https://eclipse-t2i.vercel.app/

Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches involve learning WSIs as instance-bag representations emphasizing significant instances but struggling to capture the interactions between instances. Additionally conventional graph representation methods utilize explicit spatial positions to construct topological structures but restrict the flexible interaction capabilities between instances at arbitrary locations particularly when spatially distant. In response we propose a novel dynamic graph representation algorithm that conceptualizes WSIs as a form of the knowledge graph structure. Specifically we dynamically construct neighbors and directed edge embeddings based on the head and tail relationships between instances. Then we devise a knowledge-aware attention mechanism that can update the head node features by learning the joint attention score of each neighbor and edge. Finally we obtain a graph-level embedding through the global pooling process of the updated head serving as an implicit representation for the WSI classification. Our end-to-end graph representation learning approach has outperformed the state-of-the-art WSI analysis methods on three TCGA benchmark datasets and in-house test sets. Our code is available at https://github.com/WonderLandxD/WiKG.

Non-exemplar class incremental learning (NECIL) aims to continuously assimilate new knowledge without forgetting previously acquired ones when historical data are unavailable. One of the generative NECIL methods is to invert the images of old classes for joint training. However these synthetic images suffer significant domain shifts compared with real data hampering the recognition of old classes. In this paper we present a novel method termed Dual-Consistency Model Inversion (DCMI) to generate better synthetic samples of old classes through two pivotal consistency alignments: (1) the semantic consistency between the synthetic images and the corresponding prototypes and (2) domain consistency between synthetic and real images of new classes. Besides we introduce Prototypical Routing (PR) to provide task-prior information and generate unbiased and accurate predictions. Our comprehensive experiments across diverse datasets consistently showcase the superiority of our method over previous state-of-the-art approaches.

Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos. The primary techniques used in unsupervised VOS are 1) the collaboration of appearance and motion information; and 2) temporal fusion between different frames. This paper proposes two novel prototype-based attention mechanisms inter-modality attention (IMA) and inter-frame attention (IFA) to incorporate these techniques via dense propagation across different modalities and frames. IMA densely integrates context information from different modalities based on a mutual refinement. IFA injects global context of a video to the query frame enabling a full utilization of useful properties from multiple frames. Experimental results on public benchmark datasets demonstrate that our proposed approach outperforms all existing methods by a substantial margin. The proposed two components are also thoroughly validated via ablative study.

Recently deep unfolding methods have achieved remarkable success in the realm of Snapshot Compressive Imaging (SCI) reconstruction. However the existing methods all follow the iterative framework of a single image prior which limits the efficiency of the unfolding methods and makes it a problem to use other priors simply and effectively. To break out of the box we derive an effective Dual Prior Unfolding (DPU) which achieves the joint utilization of multiple deep priors and greatly improves iteration efficiency. Our unfolding method is implemented through two parts i.e. Dual Prior Framework (DPF) and Focused Attention (FA). In brief in addition to the normal image prior DPF introduces a residual into the iteration formula and constructs a degraded prior for the residual by considering various degradations to establish the unfolding framework. To improve the effectiveness of the image prior based on self-attention FA adopts a novel mechanism inspired by PCA denoising to scale and filter attention which lets the attention focus more on effective features with little computation cost. Besides an asymmetric backbone is proposed to further improve the efficiency of hierarchical self-attention. Remarkably our 5-stage DPU achieves state-of-the-art (SOTA) performance with the least FLOPs and parameters compared to previous methods while our 9-stage DPU significantly outperforms other unfolding methods with less computational requirement.

With the emergence of pre-trained vision-language models like CLIP how to adapt them to various downstream classification tasks has garnered significant attention in recent research. The adaptation strategies can be typically categorized into three paradigms: zero-shot adaptation few-shot adaptation and the recently-proposed training-free few-shot adaptation. Most existing approaches are tailored for a specific setting and can only cater to one or two of these paradigms. In this paper we introduce a versatile adaptation approach that can effectively work under all three settings. Specifically we propose the dual memory networks that comprise dynamic and static memory components. The static memory caches training data knowledge enabling training-free few-shot adaptation while the dynamic memory preserves historical test features online during the testing process allowing for the exploration of additional data insights beyond the training set. This novel capability enhances model performance in the few-shot setting and enables model usability in the absence of training data. The two memory networks employ the same flexible memory interactive strategy which can operate in a training-free mode and can be further enhanced by incorporating learnable projection layers. Our approach is tested across 11 datasets under the three task settings. Remarkably in the zero-shot scenario it outperforms existing methods by over 3% and even shows superior results against methods utilizing external training data. Additionally our method exhibits robust performance against natural distribution shifts.

Real driving-video dehazing poses a significant challenge due to the inherent difficulty in acquiring precisely aligned hazy/clear video pairs for effective model training especially in dynamic driving scenarios with unpredictable weather conditions. In this paper we propose a pioneering approach that addresses this challenge through a nonaligned regularization strategy. Our core concept involves identifying clear frames that closely match hazy frames serving as references to supervise a video dehazing network. Our approach comprises two key components: reference matching and video dehazing. Firstly we introduce a non-aligned reference frame matching module leveraging an adaptive sliding window to match high-quality reference frames from clear videos. Video dehazing incorporates flow-guided cosine attention sampler and deformable cosine attention fusion modules to enhance spatial multiframe alignment and fuse their improved information. To validate our approach we collect a GoProHazy dataset captured effortlessly with GoPro cameras in diverse rural and urban road environments. Extensive experiments demonstrate the superiority of the proposed method over current state-of-the-art methods in the challenging task of real driving-video dehazing. Project page.

Reconstructing CAD construction sequences from raw 3D geometry serves as an interface between real-world objects and digital designs. In this paper we propose CAD-Diffuser a multimodal diffusion scheme aiming at integrating top-down design paradigm into generative reconstruction. In particular we unify CAD point clouds and CAD construction sequences at the token level guiding our proposed multimodal diffusion strategy to understand and link between the geometry and the design intent concentrated in construction sequences. Leveraging the strong decoding abilities of language models the forward process is modeled as a random walk between the original token and the [MASK] token while the reverse process naturally fits the masked token modeling scheme. A volume-based noise schedule is designed to encourage outline-first generation decomposing the top-down design methodology into a machine-understandable procedure. For tokenizing CAD data of multiple modalities we introduce a tokenizer with a self-supervised face segmentation task to compress local and global geometric information for CAD point clouds and the CAD construction sequence is transformed into a primitive token string. Experimental results show that our CAD-Diffuser can perceive geometric details and the results are more likely to be reused by human designers.

We study text-based image editing (TBIE) of a single image by counterfactual inference because it is an elegant formulation to precisely address the requirement: the edited image should retain the fidelity of the original one. Through the lens of the formulation we find that the crux of TBIE is that existing techniques hardly achieve a good trade-off between editability and fidelity mainly due to the overfitting of the single-image fine-tuning. To this end we propose a Doubly Abductive Counterfactual inference framework (DAC). We first parameterize an exogenous variable as a UNet LoRA whose abduction can encode all the image details. Second we abduct another exogenous variable parameterized by a text encoder LoRA which recovers the lost editability caused by the overfitted first abduction. Thanks to the second abduction which exclusively encodes the visual transition from post-edit to pre-edit its inversion---subtracting the LoRA---effectively reverts pre-edit back to post-edit thereby accomplishing the edit. Through extensive experiments our DAC achieves a good trade-off between editability and fidelity. Thus we can support a wide spectrum of user editing intents including addition removal manipulation replacement style transfer and facial change which are extensively validated in both qualitative and quantitative evaluations. Codes are in https://github.com/xuesong39/DAC.

We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes. Our method learns latent priors discretized as tokens by only performing computations at the visible locations of the image. This is realized by a restrictive partial encoder that predicts the token label for each visible block a bidirectional transformer that infers the missing labels by only looking at these tokens and a dedicated synthesis network that couples the tokens with the partial image priors to generate coherent and pluralistic complete image even under extreme mask settings. Experiments on public benchmarks validate our design choices as the proposed method outperforms strong baselines in both visual quality and diversity metrics.

Conventional Unsupervised Domain Adaptation (UDA) strives to minimize distribution discrepancy between domains which neglects to harness rich semantics from data and struggles to handle complex domain shifts. A promising technique is to leverage the knowledge of large-scale pre-trained vision-language models for more guided adaptation. Despite some endeavors current methods often learn textual prompts to embed domain semantics for source and target domains separately and perform classification within each domain limiting cross-domain knowledge transfer. Moreover prompting only the language branch lacks flexibility to adapt both modalities dynamically. To bridge this gap we propose Domain-Agnostic Mutual Prompting (DAMP) to exploit domain-invariant semantics by mutually aligning visual and textual embeddings. Specifically the image contextual information is utilized to prompt the language branch in a domain-agnostic and instance-conditioned way. Meanwhile visual prompts are imposed based on the domain-agnostic textual prompt to elicit domain-invariant visual embeddings. These two branches of prompts are learned mutually with a cross-attention module and regularized with a semantic-consistency loss and an instance-discrimination contrastive loss. Experiments on three UDA benchmarks demonstrate the superiority of DAMP over state-of-the-art approaches.

Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However when adapting VLMs to specialized domains such as remote sensing and medical imaging domain prompt learning remains underexplored. While large-scale domain-specific foundation models can help tackle this challenge their concentration on a single vision level makes it challenging to prompt both vision and language modalities. To overcome this we propose to leverage domain-specific knowledge from domain-specific foundation models to transfer the robust recognition ability of VLMs from generalized to specialized domains using quaternion networks. Specifically the proposed method involves using domain-specific vision features from domain-specific foundation models to guide the transformation of generalized contextual embeddings from the language branch into a specialized space within the quaternion networks. Moreover we present a hierarchical approach that generates vision prompt features by analyzing intermodal relationships between hierarchical language prompt features and domain-specific vision features. In this way quaternion networks can effectively mine the intermodal relationships in the specific domain facilitating domain-specific vision-language contrastive learning. Extensive experiments on domain-specific datasets show that our proposed method achieves new state-of-the-art results in prompt learning.

Aligned text-image encoders such as CLIP have become the de-facto model for vision-language tasks. Furthermore modality-specific encoders achieve impressive performances in their respective domains. This raises a central question: does an alignment exist between uni-modal vision and language encoders since they fundamentally represent the same physical world? Analyzing the latent spaces structure of vision and language models on image-caption benchmarks using the Centered Kernel Alignment (CKA) we find that the representation spaces of unaligned and aligned encoders are semantically similar. In the absence of statistical similarity in aligned encoders like CLIP we show that a possible matching of unaligned encoders exists without any training. We frame this as a seeded graph-matching problem exploiting the semantic similarity between graphs and propose two methods - a Fast Quadratic Assignment Problem optimization and a novel localized CKA metric-based matching/retrieval. We demonstrate the effectiveness of this on several downstream tasks including cross-lingual cross-domain caption matching and image classification. Code available at github.com/mayug/0-shot-llm-vision.