CVPR 2024 Papers

We present Bayesian Diffusion Models (BDM) a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes. We demonstrate the application of BDM on the 3D shape reconstruction task. Compared to standard deep learning data-driven approaches relying on supervised data our BDM can bring in rich prior information trained in an unsupervised manner to improve the bottom-up 3D reconstruction. As opposed to the traditional Bayesian frameworks where explicitly learned prior and data-driven distributions are required for gradient computation and combination BDM performs a seamless fusion of the two via coupled diffusion processes with learned gradient computation networks. The specialty of our Bayesian Diffusion Models (BDM) lies in its capability to engage the active and effective information exchange and fusion of the top-down and bottom-up processes where each itself is a diffusion process. We demonstrate state-of-the-art results on both synthetic and real-world benchmarks for 3D shape reconstruction. Project link: https://mlpc-ucsd.github.io/BDM

We propose a new method for cloth digitalization. Deviating from existing methods which learn from data captured under relatively casual settings we propose to learn from data captured in strictly tested measuring protocols and find plausible physical parameters of the cloths. However such data is currently absent so we first propose a new dataset with accurate cloth measurements. Further the data size is considerably smaller than the ones in current deep learning due to the nature of the data capture process. To learn from small data we propose a new Bayesian differentiable cloth model to estimate the complex material heterogeneity of real cloths. It can provide highly accurate digitalization from very limited data samples. Through exhaustive evaluation and comparison we show our method is accurate in cloth digitalization efficient in learning from limited data samples and general in capturing material variations. Code and data are available in: https://github.com/realcrane/Bayesian-Differentiable-Physics-for-Cloth-Digitalization

Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation but learning from multiview images faces inherent uncertainties. Current methods to quantify them are either heuristic or computationally demanding. We introduce BayesRays a post-hoc framework to evaluate uncertainty in any pretrained NeRF without modifying the training process. Our method establishes a volumetric uncertainty field using spatial perturbations and a Bayesian Laplace approximation. We derive our algorithm statistically and show its superior performance in key metrics and applications. Additional results available at: https://bayesrays.github.io/

Representing signals using coordinate networks dominates the area of inverse problems recently and is widely applied in various scientific computing tasks. Still there exists an issue of spectral bias in coordinate networks limiting the capacity to learn high-frequency components. This problem is caused by the pathological distribution of the neural tangent kernel's (NTK's) eigenvalues of coordinate networks. We find that this pathological distribution could be improved using the classical batch normalization (BN) which is a common deep learning technique but rarely used in coordinate networks. BN greatly reduces the maximum and variance of NTK's eigenvalues while slightly modifies the mean value considering the max eigenvalue is much larger than the most this variance change results in a shift of eigenvalues' distribution from a lower one to a higher one therefore the spectral bias could be alleviated (see Fig. 1). This observation is substantiated by the significant improvements of applying BN-based coordinate networks to various tasks including the image compression computed tomography reconstruction shape representation magnetic resonance imaging and novel view synthesis.

Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. These models are widely used for data augmentation and creative applications. However DMs reflect the biases present in the training datasets. This is especially concerning in the context of faces where the DM prefers one demographic subgroup vs others (eg. female vs male). In this work we present a method for debiasing DMs without relying on additional reference data or model retraining. Specifically we propose Distribution Guidance which enforces the generated images to follow the prescribed attribute distribution. To realize this we build on the key insight that the latent features of denoising UNet hold rich demographic semantics and the same can be leveraged to guide debiased generation. We train Attribute Distribution Predictor (ADP) - a small mlp that maps the latent features to the distribution of attributes. ADP is trained with pseudo labels generated from existing attribute classifiers. The proposed Distribution Guidance with ADP enables us to do fair generation. Our method reduces bias across single/multiple attributes and outperforms the baseline by a significant margin for unconditional and text-conditional diffusion models. Further we present a downstream task of training a fair attribute classifier by augmenting the training set with our generated data.

Contrastive Vision-Language Pre-training known as CLIP has shown promising effectiveness in addressing downstream image recognition tasks. However recent works revealed that the CLIP model can be implanted with a downstream-oriented backdoor. On downstream tasks one victim model performs well on clean samples but predicts a specific target class whenever a specific trigger is present. For injecting a backdoor existing attacks depend on a large amount of additional data to maliciously fine-tune the entire pre-trained CLIP model which makes them inapplicable to data-limited scenarios. In this work motivated by the recent success of learnable prompts we address this problem by injecting a backdoor into the CLIP model in the prompt learning stage. Our method named BadCLIP is built on a novel and effective mechanism in backdoor attacks on CLIP i.e. influencing both the image and text encoders with the trigger. It consists of a learnable trigger applied to images and a trigger-aware context generator such that the trigger can change text features via trigger-aware prompts resulting in a powerful and generalizable attack. Extensive experiments conducted on 11 datasets verify that the clean accuracy of BadCLIP is similar to those of advanced prompt learning methods and the attack success rate is higher than 99% in most cases. BadCLIP is also generalizable to unseen classes and shows a strong generalization capability under cross-dataset and cross-domain settings. The code is available at https://github.com/jiawangbai/BadCLIP.

While existing backdoor attacks have successfully infected multimodal contrastive learning models such as CLIP they can be easily countered by specialized backdoor defenses for MCL models. This paper reveals the threats in this practical scenario and introduces the BadCLIP attack which is resistant to backdoor detection and model fine-tuning defenses. To achieve this we draw motivations from the perspective of the Bayesian rule and propose a dual-embedding guided framework for backdoor attacks. Specifically we ensure that visual trigger patterns approximate the textual target semantics in the embedding space making it challenging to detect the subtle parameter variations induced by backdoor learning on such natural trigger patterns. Additionally we optimize the visual trigger patterns to align the poisoned samples with target vision features in order to hinder backdoor unlearning through clean fine-tuning. Our experiments show a significant improvement in attack success rate (+45.3 % ASR) over current leading methods even against state-of-the-art backdoor defenses highlighting our attack's effectiveness in various scenarios including downstream tasks. Our codes can be found at https://github.com/LiangSiyuan21/BadCLIP.

Real-world systems often encounter new data over time which leads to experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally heavy and memory-intensive backpropagation-based approaches to handle this. Here we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data. Our model uses a two-stream architecture to maintain knowledge about the source domain as well as complementary target-domain-specific information. The backpropagation-free property of our model helps address the well-known forgetting problem and mitigates the error accumulation issue. The proposed method also eliminates the need for the usually noisy process of pseudo-labeling and reliance on costly self-supervised training. Moreover our method leverages subspace learning effectively reducing the distribution variance between the two domains. Furthermore the source-domain-specific and the target-domain-specific streams are aligned using a novel entropy-based adaptive fusion strategy. Extensive experiments on popular benchmarks demonstrate the effectiveness of our method. The code will be available at https://github.com/abie-e/BFTT3D.

Deep neural networks have played a crucial part in many critical domains such as autonomous driving face recognition and medical diagnosis. However deep neural networks are facing security threats from backdoor attacks and can be manipulated into attacker-decided behaviors by the backdoor attacker. To defend the backdoor prior research has focused on using clean data to remove backdoor attacks before model deployment. In this paper we investigate the possibility of defending against backdoor attacks by utilizing test-time partially poisoned data to remove the backdoor from the model. To address the problem a two-stage method TTBD is proposed. In the first stage we propose a backdoor sample detection method DDP to identify poisoned samples from a batch of mixed partially poisoned samples. Once the poisoned samples are detected we employ Shapley estimation to calculate the contribution of each neuron's significance in the network locate the poisoned neurons and prune them to remove backdoor in the models. Our experiments demonstrate that TTBD removes the backdoor successfully with only a batch of partially poisoned data across different model architectures and datasets against different types of backdoor attacks.

With the immense growth of dataset sizes and computing resources in recent years so-called foundation models have become popular in NLP and vision tasks. In this work we propose to explore foundation models for the task of keypoint detection on 3D shapes. A unique characteristic of keypoint detection is that it requires semantic and geometric awareness while demanding high localization accuracy. To address this problem we propose first to back-project features from large pre-trained 2D vision models onto 3D shapes and employ them for this task. We show that we obtain robust 3D features that contain rich semantic information and analyze multiple candidate features stemming from different 2D foundation models. Second we employ a keypoint candidate optimization module which aims to match the average observed distribution of keypoints on the shape and is guided by the back-projected features. The resulting approach achieves a new state of the art for few-shot keypoint detection on the KeyPointNet dataset almost doubling the performance of the previous best methods.

The recent progress in Large Language Models (LLM) has spurred various advancements in image-language conversation agents while how to build a proficient video-based dialogue system is still under exploration. Considering the extensive scale of LLM and visual backbone minimal GPU memory is left for facilitating effective temporal modeling which is crucial for comprehending and providing feedback on videos. To this end we propose Branching Temporal Adapter (BT-Adapter) a novel method for extending image-language pretrained models into the video domain. Specifically BT-Adapter serves as a plug-and-use temporal modeling branch alongside the pretrained visual encoder which is tuned while keeping the backbone frozen. Just pretrained once BT-Adapter can be seamlessly integrated into all image conversation models using this version of CLIP enabling video conversations without the need for video instructions. Besides we develop a unique asymmetric token masking strategy inside the branch with tailor-made training tasks for BT-Adapter facilitating faster convergence and better results. Thanks to BT-Adapter we are able to empower existing multimodal dialogue models with strong video understanding capabilities without incurring excessive GPU costs. Without bells and whistles BT-Adapter achieves (1) state-of-the-art zero-shot results on various video tasks using thousands of fewer GPU hours. (2) better performance than current video chatbots without any video instruction tuning. (3) state-of-the-art results of video chatting using video instruction tuning outperforming previous SOTAs by a large margin. The code has been available at https://github.com/farewellthree/BT-Adapter.

3D instance segmentation (3DIS) is a crucial task but point-level annotations are tedious in fully supervised settings. Thus using bounding boxes (bboxes) as annotations has shown great potential. The current mainstream approach is a two-step process involving the generation of pseudo-labels from box annotations and the training of a 3DIS network with the pseudo-labels. However due to the presence of intersections among bboxes not every point has a determined instance label especially in overlapping areas. To generate higher quality pseudo-labels and achieve more precise weakly supervised 3DIS results we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (BSNet) which devises a novel pseudo-labeler called Simulation-assisted Transformer. The labeler consists of two main components. The first is Simulation-assisted Mean Teacher which introduces Mean Teacher for the first time in this task and constructs simulated samples to assist the labeler in acquiring prior knowledge about overlapping areas. To better model local-global structure we also propose Local-Global Aware Attention as the decoder for teacher and student labelers. Extensive experiments conducted on the ScanNetV2 and S3DIS datasets verify the superiority of our designs.

The recently emerging text-to-motion advances have spired numerous attempts for convenient and interactive human motion generation. Yet existing methods are largely limited to generating body motions only without considering the rich two-hand motions let alone handling various conditions like body dynamics or texts. To break the data bottleneck we propose BOTH57M a novel multi-modal dataset for two-hand motion generation. Our dataset includes accurate motion tracking for the human body and hands and provides pair-wised finger-level hand annotations and body descriptions. We further provide a strong baseline method BOTH2Hands for the novel task: generating vivid two-hand motions from both implicit body dynamics and explicit text prompts. We first warm up two parallel body-to-hand and text-to-hand diffusion models and then utilize the cross-attention transformer for motion blending. Extensive experiments and cross-validations demonstrate the effectiveness of our approach and dataset for generating convincing two-hand motions from the hybrid body-and-textual conditions. Our dataset and code will be disseminated to the community for future research which can be found at https://github.com/Godheritage/BOTH2Hands.

Diffusion models have made tremendous progress in text-driven image and video generation. Now text-to-image foundation models are widely applied to various downstream image synthesis tasks such as controllable image generation and image editing while downstream video synthesis tasks are less explored for several reasons. First it requires huge memory and computation overhead to train a video generation foundation model. Even with video foundation models additional costly training is still required for downstream video synthesis tasks. Second although some works extend image diffusion models into videos in a training-free manner temporal consistency cannot be well preserved. Finally these adaption methods are specifically designed for one task and fail to generalize to different tasks. To mitigate these issues we propose a training-free general-purpose video synthesis framework coined as BIVDiff via bridging specific image diffusion models and general text-to-video foundation diffusion models. Specifically we first use a specific image diffusion model (e.g. ControlNet and Instruct Pix2Pix) for frame-wise video generation then perform Mixed Inversion on the generated video and finally input the inverted latents into the video diffusion models (e.g. VidRD and ZeroScope) for temporal smoothing. This decoupled framework enables flexible image model selection for different purposes with strong task generalization and high efficiency. To validate the effectiveness and general use of BIVDiff we perform a wide range of video synthesis tasks including controllable video generation video editing video inpainting and outpainting.

Vision-based roadside 3D object detection has attracted rising attention in autonomous driving domain since it encompasses inherent advantages in reducing blind spots and expanding perception range. While previous work mainly focuses on accurately estimating depth or height for 2D-to-3D mapping ignoring the position approximation error in the voxel pooling process. Inspired by this insight we propose a novel voxel pooling strategy to reduce such error dubbed BEVSpread. Specifically instead of bringing the image features contained in a frustum point to a single BEV grid BEVSpread considers each frustum point as a source and spreads the image features to the surrounding BEV grids with adaptive weights. To achieve superior propagation performance a specific weight function is designed to dynamically control the decay speed of the weights according to distance and depth. Aided by customized CUDA parallel acceleration BEVSpread achieves comparable inference time as the original voxel pooling. Extensive experiments on two large-scale roadside benchmarks demonstrate that as a plug-in BEVSpread can significantly improve the performance of existing frustum-based BEV methods by a large margin of (1.12 5.26 3.01) AP in vehicle pedestrian and cyclist.

Recently the rise of query-based Transformer decoders is reshaping camera-based 3D object detection. These query-based decoders are surpassing the traditional dense BEV (Bird's Eye View)-based methods. However we argue that dense BEV frameworks remain important due to their outstanding abilities in depth estimation and object localization depicting 3D scenes accurately and comprehensively. This paper aims to address the drawbacks of the existing dense BEV-based 3D object detectors by introducing our proposed enhanced components including a CRF-modulated depth estimation module enforcing object-level consistencies a long-term temporal aggregation module with extended receptive fields and a two-stage object decoder combining perspective techniques with CRF-modulated depth embedding. These enhancements lead to a "modernized" dense BEV framework dubbed BEVNeXt. On the nuScenes benchmark BEVNeXt outperforms both BEV-based and query-based frameworks under various settings achieving a state-of-the-art result of 64.2 NDS on the nuScenes test set.

Data mixing methods play a crucial role in semi-supervised learning (SSL) but their application is unexplored in long-tailed semi-supervised learning (LTSSL). The primary reason is that the in-batch mixing manner fails to address class imbalance. Furthermore existing LTSSL methods mainly focus on re-balancing data quantity but ignore class-wise uncertainty which is also vital for class balance. For instance some classes with sufficient samples might still exhibit high uncertainty due to indistinguishable features. To this end this paper introduces the Balanced and Entropy-based Mix (BEM) a pioneering mixing approach to re-balance the class distribution of both data quantity and uncertainty. Specifically we first propose a class balanced mix bank to store data of each class for mixing. This bank samples data based on the estimated quantity distribution thus re-balancing data quantity. Then we present an entropy-based learning approach to re-balance class-wise uncertainty including entropy-based sampling strategy entropy-based selection module and entropy-based class balanced loss. Our BEM first leverages data mixing for improving LTSSL and it can also serve as a complement to the existing re-balancing methods. Experimental results show that BEM significantly enhances various LTSSL frameworks and achieves state-of-the-art performances across multiple benchmarks.

The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative particularly for embodied AI tasks they often fall short for computer vision tasks due to low asset and rendering quality limited diversity and unrealistic physical properties. We introduce the BEHAVIOR Vision Suite (BVS) a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models based on the newly developed embodied AI benchmark BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g. lighting object placement) the object level (e.g. joint configuration attributes such as "filled" and "folded") and the camera level (e.g. field of view focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift evaluating scene understanding models on the same set of images and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://behavior-vision-suite.github.io/

Largely due to their implicit nature neural fields lack a direct mechanism for filtering as Fourier analysis from discrete signal processing is not directly applicable to these representations. Effective filtering of neural fields is critical to enable level-of-detail processing in downstream applications and support operations that involve sampling the field on regular grids (e.g. marching cubes). Existing methods that attempt to decompose neural fields in the frequency domain either resort to heuristics or require extensive modifications to the neural field architecture. We show that via a simple modification one can obtain neural fields that are low-pass filtered and in turn show how this can be exploited to obtain a frequency decomposition of the entire signal. We demonstrate the validity of our technique by investigating level-of-detail reconstruction and showing how coarser representations can be computed effectively.