CVPR 2024 论文列表

Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using the semi-supervised student-teacher framework. However a fundamental issue arises from the class imbalance in the labelled training set which can result in inaccurate pseudo-labels. The relationship between classes especially where one class is a majority and the other minority has a large impact on class bias. We propose Class-Aware Teacher (CAT) to address the class bias issue in the domain adaptation setting. In our work we approximate the class relationships with our Inter-Class Relation module (ICRm) and exploit it to reduce the bias within the model. In this way we are able to apply augmentations to highly related classes both inter- and intra-domain to boost the performance of minority classes while having minimal impact on majority classes. We further reduce the bias by implementing a class-relation weight to our classification loss. Experiments conducted on various datasets and ablation studies show that our method is able to address the class bias in the domain adaptation setting. On the Cityscapes ? Foggy Cityscapes dataset we attained a 52.5 mAP a substantial improvement over the 51.2 mAP achieved by the state-of-the-art method.

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions. In this work we introduce a novel cost-based approach to adapt vision-language foundation models notably CLIP for the intricate task of semantic segmentation. Through aggregating the cosine similarity score i.e. the cost volume between image and text embeddings our method potently adapts CLIP for segmenting seen and unseen classes by fine-tuning its encoders addressing the challenges faced by existing methods in handling unseen classes. Building upon this we explore methods to effectively aggregate the cost volume considering its multi-modal nature of being established between image and text embeddings. Furthermore we examine various methods for efficiently fine-tuning CLIP.

Generative Adversarial Networks (GANs) dominate the research field in image-based virtual try-on but have not resolved problems such as unnatural deformation of garments and the blurry generation quality. While the generative quality of diffusion models is impressive achieving controllability poses a significant challenge when applying it to virtual try-on and multiple denoising iterations limit its potential for real-time applications. In this paper we propose Controllable Accelerated virtual Try-on with Diffusion Model (CAT-DM). To enhance the controllability a basic diffusion-based virtual try-on network is designed which utilizes ControlNet to introduce additional control conditions and improves the feature extraction of garment images. In terms of acceleration CAT-DM initiates a reverse denoising process with an implicit distribution generated by a pre-trained GAN-based model. Compared with previous try-on methods based on diffusion models CAT-DM not only retains the pattern and texture details of the in-shop garment but also reduces the sampling steps without compromising generation quality. Extensive experiments demonstrate the superiority of CAT-DM against both GAN-based and diffusion-based methods in producing more realistic images and accurately reproducing garment patterns.

The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs focusing on image-text alignment. However existing methods primarily rely on cosine similarity for alignment which may not fully capture the complex relationship between medical images and reports. To address this gap we introduce a novel approach called Cross-Attention Alignment for Radiology Zero-Shot Classification (CARZero). Our approach innovatively leverages cross-attention mechanisms to process image and report features creating a Similarity Representation that more accurately reflects the intricate relationships in medical semantics. This representation is then linearly projected to form an image-text similarity matrix for cross-modality alignment. Additionally recognizing the pivotal role of prompt selection in zero-shot learning CARZero incorporates a Large Language Model-based prompt alignment strategy. This strategy standardizes diverse diagnostic expressions into a unified format for both training and inference phases overcoming the challenges of manual prompt design. Our approach is simple yet effective demonstrating state-of-the-art performance in zero-shot classification on five official chest radiograph diagnostic test sets including remarkable results on datasets with long-tail distributions of rare diseases. This achievement is attributed to our new image-text alignment strategy which effectively addresses the complex relationship between medical images and reports. Code and models are available at https://github.com/laihaoran/CARZero.

Deep Neural Networks (DNNs) are widely used for visual classification tasks but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways to visually explain the DNN decision-making process by displaying 'attention' heatmaps of the DNNs. Nevertheless the CAM explanation only offers relative attention information that is on an attention heatmap we can interpret which image region is more or less important than the others. However these regions cannot be meaningfully compared across classes and the contribution of each region to the model's class prediction is not revealed. To address these challenges that ultimately lead to better DNN Interpretation in this paper we propose CAPE a novel reformulation of CAM that provides a unified and probabilistically meaningful assessment of the contributions of image regions. We quantitatively and qualitatively compare CAPE with state-of-the-art CAM methods on CUB and ImageNet benchmark datasets to demonstrate enhanced interpretability. We also test on a cytology imaging dataset depicting a challenging Chronic Myelomonocytic Leukemia (CMML) diagnosis problem. Code is available at:https://github.com/AIML-MED/CAPE.

To satisfy the rapidly increasing demands on the large image (2K-8K) super-resolution (SR) prevailing methods follow two independent tracks: 1) accelerate existing networks by content-aware routing and 2) design better super-resolution networks via token mixer refining. Despite directness they encounter unavoidable defects (e.g. inflexible route or non-discriminative processing) limiting further improvements of quality-complexity trade-off. To erase the drawbacks we integrate these schemes by proposing a content-aware mixer (CAMixer) which assigns convolution for simple contexts and additional deformable window-attention for sparse textures. Specifically the CAMixer uses a learnable predictor to generate multiple bootstraps including offsets for windows warping a mask for classifying windows and convolutional attentions for endowing convolution with the dynamic property which modulates attention to include more useful textures self-adaptively and improves the representation capability of convolution. We further introduce a global classification loss to improve the accuracy of predictors. By simply stacking CAMixers we obtain CAMixerSR which achieves superior performance on large-image SR lightweight SR and omnidirectional-image SR.

Text-driven video editing poses significant challenges in exhibiting flicker-free visual continuity while preserving the inherent motion patterns of original videos. Existing methods operate under a paradigm where motion and appearance are intricately intertwined. This coupling leads to the network either over-fitting appearance content -- failing to capture motion patterns -- or focusing on motion patterns at the expense of content generalization to diverse textual scenarios. Inspired by the pivotal role of wavelet transform in dissecting video sequences we propose CAusal Motion Enhancement tailored for Lifting text-driven video editing (CAMEL) a novel technique with two core designs. First we introduce motion prompts designed to summarize motion concepts from video templates through direct optimization. The optimized prompts are purposefully integrated into latent representations of diffusion models to enhance the motion fidelity of generated results. Second to enhance motion coherence and extend the generalization of appearance content to creative textual prompts we propose the causal motion-enhanced attention mechanism. This mechanism is implemented in tandem with a novel causal motion filter synergistically enhancing the motion coherence of disentangled high-frequency components and concurrently preserving the generalization of appearance content across various textual scenarios. Extensive experimental results show the superior performance of CAMEL.

Recently convolutional neural networks (CNNs) with large size kernels have attracted much attention in the computer vision field following the success of the Vision Transformers. Large kernel CNNs have been reported to perform well in downstream vision tasks as well as in classification performance. The reason for the high-performance of large kernel CNNs in downstream tasks has been attributed to the large effective receptive field (ERF) produced by large size kernels but this view has not been fully tested. We therefore revisit the performance of large kernel CNNs in downstream task focusing on the weakly supervised object localization (WSOL) task. WSOL a difficult downstream task that is not fully supervised provides a new angle to explore the capabilities of the large kernel CNNs. Our study compares the modern large kernel CNNs ConvNeXt RepLKNet and SLaK to test the validity of the naive expectation that ERF size is important for improving downstream task performance. Our analysis of the factors contributing to high performance provides a different perspective in which the main factor is feature map improvement. Furthermore we find that modern CNNs are robust to the CAM problems of local regions of objects being activated which has long been discussed in WSOL. CAM is the most classic WSOL method but because of the above-mentioned problems it is often used as a baseline method for comparison. However experiments on the CUB-200-2011 dataset show that simply combining a large kernel CNN CAM and simple data augmentation methods can achieve performance (90.99% MaxBoxAcc) comparable to the latest WSOL method which is CNN-based and requires special training or complex post-processing.

We address the challenge of generating 3D articulated objects in a controllable fashion. Currently modeling articulated 3D objects is either achieved through laborious manual authoring or using methods from prior work that are hard to scale and control directly. We leverage the interplay between part shape connectivity and motion using a denoising diffusion-based method with attention modules designed to extract correlations between part attributes. Our method takes an object category label and a part connectivity graph as input and generates an object's geometry and motion parameters. The generated objects conform to user-specified constraints on the object category part shape and part articulation. Our experiments show that our method outperforms the state-of-the-art in articulated object generation producing more realistic objects while conforming better to user constraints.

CAD programs are a popular way to compactly encode shapes as a sequence of operations that are easy to parametrically modify. However without sufficient semantic comments and structure such programs can be challenging to understand let alone modify. We introduce the problem of semantic commenting CAD programs wherein the goal is to segment the input program into code blocks corresponding to semantically meaningful shape parts and assign a semantic label to each block. We solve the problem by combining program parsing with visual-semantic analysis afforded by recent advances in foundational language and vision models. Specifically by executing the input programs we create shapes which we use to generate conditional photorealistic images to make use of semantic annotators for such images. We then distill the information across the images and link back to the original programs to semantically comment on them. Additionally we collected and annotated a benchmark dataset CADTalk consisting of 5288 machine-made programs and 45 human-made programs with ground truth semantic comments. We extensively evaluated our approach compared it to a GPT-based baseline and an open-set shape segmentation baseline and reported an 83.24% accuracy on the new CADTalk dataset. Code and data: https://enigma-li.github.io/CADTalk/.

The increased demand for 3D data in AR/VR robotics and gaming applications gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these models rely on the Score Distillation Sampling (SDS) algorithm to optimize a 3D representation such that the rendered image maintains a high likelihood as evaluated by a pre-trained diffusion model. However this distillation process involves finding a correct mode in the high-dimensional and large-variance distribution produced by the diffusion model. This task is challenging and often leads to issues such as over-saturation over-smoothing and Janus-like artifacts in the 3D generation. In this paper we propose a novel learning paradigm for 3D synthesis that utilizes pre-trained diffusion models. Instead of focusing on mode-seeking our method directly models the distribution discrepancy between multi-view renderings and diffusion priors in an adversarial manner which unlocks the generation of high-fidelity and photorealistic 3D content conditioned on a single image and prompt. Moreover by harnessing the latent space of GANs and expressive diffusion model priors our method enables a wide variety of 3D applications including single-view reconstruction high diversity generation and continuous 3D interpolation in open domain. Our experiments demonstrate the superiority of our pipeline compared to previous works in terms of generation quality and diversity.

Reverse engineering in the realm of Computer-Aided Design (CAD) has been a longstanding aspiration though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD-SIGNet an end-to-end trainable and auto-regressive architecture to recover the design history of a CAD model represented as a sequence of sketch- and-extrusion from an input point cloud. Our model learns CAD visual-language representations by layer-wise cross-attention between point cloud and CAD language embedding. In particular a new Sketch instance Guided Attention (SGA) module is proposed in order to reconstruct the fine- grained details of the sketches. Thanks to its auto-regressive nature CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an in- put point cloud but also provides multiple plausible design choices. This allows for an interactive reverse engineering scenario by providing designers with multiple next step choices along with the design process. Extensive experiments on publicly available CAD datasets showcase the effectiveness of our approach against existing baseline models in two settings namely full design history recovery and conditional auto-completion from point clouds.

Person re-identification (re-ID) is a challenging task that aims to learn discriminative features for person retrieval. In person re-ID Jaccard distance is a widely used distance metric especially in re-ranking and clustering scenarios. However we discover that camera variation has a significant negative impact on the reliability of Jaccard distance. In particular Jaccard distance calculates the distance based on the overlap of relevant neighbors. Due to camera variation intra-camera samples dominate the relevant neighbors which reduces the reliability of the neighbors by introducing intra-camera negative samples and excluding inter-camera positive samples. To overcome this problem we propose a novel camera-aware Jaccard (CA-Jaccard) distance that leverages camera information to enhance the reliability of Jaccard distance. Specifically we design camera-aware k-reciprocal nearest neighbors (CKRNNs) to find k-reciprocal nearest neighbors on the intra-camera and inter-camera ranking lists which improves the reliability of relevant neighbors and guarantees the contribution of inter-camera samples in the overlap. Moreover we propose a camera-aware local query expansion (CLQE) to mine reliable samples in relevant neighbors by exploiting camera variation as a strong constraint and assign these samples higher weights in overlap further improving the reliability. Our CA-Jaccard distance is simple yet effective and can serve as a general distance metric for person re-ID methods with high reliability and low computational cost. Extensive experiments demonstrate the effectiveness of our method. Code is available at https://github.com/chen960/CA-Jaccard/.

We present Compound Conditioned ControlNet C3Net a novel generative neural architecture taking conditions from multiple modalities and synthesizing multimodal contents simultaneously (e.g. image text audio). C3Net adapts the ControlNet architecture to jointly train and make inferences on a production-ready diffusion model and its trainable copies. Specifically C3Net first aligns the conditions from multi-modalities to the same semantic latent space using modality-specific encoders based on contrastive training. Then it generates multimodal outputs based on the aligned latent space whose semantic information is combined using a ControlNet-like architecture called Control C3-UNet. Correspondingly with this system design our model offers an improved solution for joint-modality generation through learning and explaining multimodal conditions involving more than just linear interpolation within the latent space. Meanwhile as we align conditions to a unified latent space C3Net only requires one trainable Control C3-UNet to work on multimodal semantic information. Furthermore our model employs unimodal pretraining on the condition alignment stage outperforming the non-pretrained alignment even on relatively scarce training data and thus demonstrating high-quality compound condition generation. We contribute the first high-quality tri-modal validation set to validate quantitatively that C3Net outperforms or is on par with the first and contemporary state-of-the-art multimodal generation. Our codes and tri-modal dataset will be released.

Most neural compression models are trained on large datasets of images or videos in order to generalize to unseen data. Such generalization typically requires large and expressive architectures with a high decoding complexity. Here we introduce C3 a neural compression method with strong rate-distortion (RD) performance that instead overfits a small model to each image or video separately. The resulting decoding complexity of C3 can be an order of magnitude lower than neural baselines with similar RD performance. C3 builds on COOL-CHIC [Ladune et al 2023] and makes several simple and effective improvements for images. We further develop new methodology to apply C3 to videos. On the CLIC2020 image benchmark we match the RD performance of VTM the reference implementation of the H.266 codec with less than3k MACs/pixel for decoding. On the UVG video benchmark we match the RD performance of the Video Compression Transformer [Mentzer er al 2022] a well-established neural video codec with less than 5k MACs/pixel for decoding.

Existing Knowledge Distillation (KD) methods typically focus on transferring knowledge from a large-capacity teacher to a low-capacity student model achieving substantial success in unimodal knowledge transfer. However existing methods can hardly be extended to Cross-Modal Knowledge Distillation (CMKD) where the knowledge is transferred from a teacher modality to a different student modality with inference only on the distilled student modality. We empirically reveal that the modality gap i.e. modality imbalance and soft label misalignment incurs the ineffectiveness of traditional KD in CMKD. As a solution we propose a novel \underline C ustomized \underline C rossmodal \underline K nowledge \underline D istillation (C^2KD). Specifically to alleviate the modality gap the pre-trained teacher performs bidirectional distillation with the student to provide customized knowledge. The On-the-Fly Selection Distillation(OFSD) strategy is applied to selectively filter out the samples with misaligned soft labels where we distill cross-modal knowledge from non-target classes to avoid the modality imbalance issue. To further provide receptive cross-modal knowledge proxy student and teacher inheriting unimodal and cross-modal knowledge is formulated to progressively transfer cross-modal knowledge through bidirectional distillation. Experimental results on audio-visual image-text and RGB-depth datasets demonstrate that our method can effectively transfer knowledge across modalities achieving superior performance against traditional KD by a large margin.

Decentralized federated learning (DFL) facilitates collaborative model training across multiple connected clients without a central coordination server thereby avoiding the single point of failure in traditional centralized federated learning (CFL). However DFL exhibits heightened susceptibility to Byzantine attacks owing to the lack of a responsible central server. Furthermore a benign client in DFL may be dominated by Byzantine clients (more than half of its neighbors are malicious) posing significant challenges for robust model training. In this work we propose DFL-Dual a novel Byzantine-robust DFL method through dual-domain client clustering and trust bootstrapping. Specifically we first propose to leverage both data-domain and model-domain distance metrics to identify client discrepancies. Then we design a trust evaluation mechanism centered on benign clients which enables them to evaluate their neighbors. Building upon the dual-domain distance metric and trust evaluation mechanism we further develop a two-stage clustering and trust bootstrapping technique to exclude Byzantine clients from local model aggregation. We extensively evaluate the proposed DFL-Dual method through rigorous experimentation demonstrating its remarkable performance superiority over existing robust CFL and DFL schemes.

An effective pre-training framework with universal 3D representations is extremely desired in perceiving large-scale dynamic scenes. However establishing such an ideal framework that is both task-generic and label-efficient poses a challenge in unifying the representation of the same primitive across diverse scenes. The current contrastive 3D pre-training methods typically follow a frame-level consistency which focuses on the 2D-3D relationships in each detached image. Such inconsiderate consistency greatly hampers the promising path of reaching an universal pre-training framework: (1) The cross-scene semantic self-conflict \textit i.e. the intense collision between primitive segments of the same semantics from different scenes; (2) Lacking a globally unified bond that pushes the cross-scene semantic consistency into 3D representation learning. To address above challenges we propose a CSC framework that puts a scene-level semantic consistency in the heart bridging the connection of the similar semantic segments across various scenes. To achieve this goal we combine the coherent semantic cues provided by the vision foundation model and the knowledge-rich cross-scene prototypes derived from the complementary multi-modality information. These allow us to train a universal 3D pre-training model that facilitates various downstream tasks with less fine-tuning efforts. Empirically we achieve consistent improvements over SOTA pre-training approaches in semantic segmentation (+1.4% mIoU) object detection (+1.0% mAP) and panoptic segmentation (+3.0% PQ) using their task-specific 3D network on nuScenes. Code is released at \href https://github.com/chenhaomingbob/CSC https://github.com/chenhaomingbob/CSC hoping to inspire future research.

Recent advancements in Vision-Language Models (VLMs) have marked a significant leap in bridging the gap between computer vision and natural language processing. However traditional VLMs trained through contrastive learning on limited and noisy image-text pairs often lack the spatial and linguistic understanding to generalize well to dense vision tasks or less common languages. Our approach Solid Foundation CLIP (SF-CLIP) circumvents this issue by implicitly building on the solid visual and language understanding of foundational models trained on vast amounts of unimodal data. SF-CLIP integrates contrastive image-text pretraining with a masked knowledge distillation from large foundational text and vision models. This methodology guides our VLM in developing robust text and image representations. As a result SF-CLIP shows exceptional zero-shot classification accuracy and enhanced image and text retrieval capabilities setting a new state of the art for ViT-B/16 trained on YFCC15M and CC12M. Moreover the dense per-patch supervision enhances our zero-shot and linear probe performance in semantic segmentation tasks. A remarkable aspect of our model is its multilingual proficiency evidenced by strong retrieval results in multiple languages despite being trained predominantly on English data. We achieve all of these improvements without sacrificing the training efficiency through our selective application of masked distillation and the inheritance of teacher word embeddings.