CVPR 2024 Papers

Fine-tuning pre-trained vision-language models like CLIP has yielded success on diverse downstream tasks. However several pain points persist for this paradigm: (i) directly tuning entire pre-trained models becomes both time-intensive and computationally costly. Additionally these tuned models tend to become highly specialized limiting their practicality for real-world deployment; (ii) recent studies indicate that pre-trained vision-language classifiers may overly depend on spurious features -- patterns that correlate with the target in training data but are not related to the true labeling function; and (iii) existing studies on mitigating the reliance on spurious features largely based on the assumption that we can identify such features does not provide definitive assurance for real-world applications. As a piloting study this work focuses on exploring mitigating the reliance on spurious features for CLIP without using any group annotation. To this end we systematically study the existence of spurious correlation on CLIP and CILP+ERM. We first following recent work on Deep Feature Reweighting (DFR) verify that last-layer retraining can greatly improve group robustness on pretrained CLIP. In view of them we advocate a lightweight representation calibration method for fine-tuning CLIP by first generating a calibration set using the pretrained CLIP and then calibrating representations of samples within this set through contrastive learning all without the need for group labels. Extensive experiments and in-depth visualizations on several benchmarks validate the effectiveness of our proposals largely reducing reliance and significantly boosting the model generalization.

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).

Knowledge distillation (KD) possesses immense potential to accelerate the deep neural networks (DNNs) for LiDAR-based 3D detection. However in most of prevailing approaches the suboptimal teacher models and insufficient student architecture investigations limit the performance gains. To address these issues we propose a simple yet effective Category-aware Knowledge Distillation and Pruning (CaKDP) framework for compressing 3D detectors. Firstly CaKDP transfers the knowledge of two-stage detector to one-stage student one mitigating the impact of inadequate teacher models. To bridge the gap between the heterogeneous detectors we investigate their differences and then introduce the student-motivated category-aware KD to align the category prediction between distillation pairs. Secondly we propose a category-aware pruning scheme to obtain the customizable architecture of compact student model. The method calculates the category prediction gap before and after removing each filter to evaluate the importance of filters and retains the important filters. Finally to further improve the student performance a modified IOU-aware refinement module with negligible computations is leveraged to remove the redundant false positive predictions. Experiments demonstrate that CaKDP achieves the compact detector with high performance. For example on WOD CaKDP accelerates CenterPoint by half while boosting L2 mAPH by 1.61%. The code is available at https://github.com/zhnxjtu/CaKDP.

For safe motion planning in real-world autonomous vehicles require behavior prediction models that are reliable and robust to distribution shifts. The recent studies suggest that the existing learning-based trajectory prediction models do not posses such characteristics and are susceptible to small perturbations that are not present in the training data largely due to overfitting to spurious correlations while learning. In this paper we propose a causal disentanglement representation learning approach aiming to separate invariant (causal) and variant (spurious) features for more robust learning. Our method benefits from a novel intervention mechanism in the latent space that estimates potential distribution shifts resulted from spurious correlations using uncertain feature statistics hence maintaining the realism of interventions. To facilitate learning we propose a novel invariance objective based on the variances of the distributions over uncertain statistics to induce the model to focus on invariant representations during training. We conduct extensive experiments on two large-scale autonomous driving datasets and show that besides achieving state-of-the-art performance our method can significantly improve prediction robustness to various distribution shifts in driving scenes. We further conduct ablative studies to evaluate the design choices in our proposed framework.

Cone beam computed tomography (CBCT) is an important imaging technology widely used in medical scenarios such as diagnosis and preoperative planning. Using fewer projection views to reconstruct CT also known as sparse-view reconstruction can reduce ionizing radiation and further benefit interventional radiology. Compared with sparse-view reconstruction for traditional parallel/fan-beam CT CBCT reconstruction is more challenging due to the increased dimensionality caused by the measurement process based on cone-shaped X-ray beams. As a 2D-to-3D reconstruction problem although implicit neural representations have been introduced to enable efficient training only local features are considered and different views are processed equally in previous works resulting in spatial inconsistency and poor performance on complicated anatomies. To this end we propose C^2RV by leveraging explicit multi-scale volumetric representations to enable cross-regional learning in the 3D space. Additionally the scale-view cross-attention module is introduced to adaptively aggregate multi-scale and multi-view features. Extensive experiments demonstrate that our C^2RV achieves consistent and significant improvement over previous state-of-the-art methods on datasets with diverse anatomy. Code is available at https://github.com/xmed-lab/C2RV-CBCT.

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.

To achieve greater accuracy hypergraph matching algorithms require exponential increases in computational resources. Recent kd-tree-based approximate nearest neighbor (ANN) methods despite the sparsity of their compatibility tensor still require exhaustive calculations for large-scale graph matching. This work utilizes CUR tensor decomposition and introduces a novel cascaded second and third-order hypergraph matching framework (CURSOR) for efficient hypergraph matching. A CUR-based second-order graph matching algorithm is used to provide a rough match and then the core of CURSOR a fiber-CUR-based tensor generation method directly calculates entries of the compatibility tensor by leveraging the initial second-order match result. This significantly decreases the time complexity and tensor density. A probability relaxation labeling (PRL)-based matching algorithm especially suitable for sparse tensors is developed. Experiment results on large-scale synthetic datasets and widely-adopted benchmark sets demonstrate the superiority of CURSOR over existing methods. The tensor generation method in CURSOR can be integrated seamlessly into existing hypergraph matching methods to improve their performance and lower their computational costs.

Video summarization aims to generate a concise representation of a video capturing its essential content and key moments while reducing its overall length. Although several methods employ attention mechanisms to handle long-term dependencies they often fail to capture the visual significance inherent in frames. To address this limitation we propose a CNN-based SpatioTemporal Attention (CSTA) method that stacks each feature of frames from a single video to form image-like frame representations and applies 2D CNN to these frame features. Our methodology relies on CNN to comprehend the inter and intra-frame relations and to find crucial attributes in videos by exploiting its ability to learn absolute positions within images. In contrast to previous work compromising efficiency by designing additional modules to focus on spatial importance CSTA requires minimal computational overhead as it uses CNN as a sliding window. Extensive experiments on two benchmark datasets (SumMe and TVSum) demonstrate that our proposed approach achieves state-of-the-art performance with fewer MACs compared to previous methods. Codes are available at https://github.com/thswodnjs3/CSTA.

In the field of 3D object detection for autonomous driving LiDAR-Camera (LC) fusion is the top-performing sensor configuration. Still LiDAR is relatively high cost which hinders adoption of this technology for consumer automobiles. Alternatively camera and radar are commonly deployed on vehicles already on the road today but performance of Camera-Radar (CR) fusion falls behind LC fusion. In this work we propose Camera-Radar Knowledge Distillation (CRKD) to bridge the performance gap between LC and CR detectors with a novel cross-modality KD framework. We use the Bird's-Eye-View (BEV) representation as the shared feature space to enable effective knowledge distillation. To accommodate the unique cross-modality KD path we propose four distillation losses to help the student learn crucial features from the teacher model. We present extensive evaluations on the nuScenes dataset to demonstrate the effectiveness of the proposed CRKD framework. The project page for CRKD is https://song-jingyu.github.io/CRKD.

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.

Fine-grained medical action analysis plays a vital role in improving medical skill training efficiency but it faces the problems of data and algorithm shortage. Cardiopulmonary Resuscitation (CPR) is an essential skill in emergency treatment. Currently the assessment of CPR skills mainly depends on dummies and trainers leading to high training costs and low efficiency. For the first time this paper constructs a vision-based system to complete error action recognition and skill assessment in CPR. Specifically we define 13 types of single-error actions and 74 types of composite error actions during external cardiac compression and then develop a video dataset named CPR-Coach. By taking the CPR-Coach as a benchmark this paper investigates and compares the performance of existing action recognition models based on different data modalities. To solve the unavoidable "Single-class Training & Multi-class Testing" problem we propose a human-cognition-inspired framework named ImagineNet to improve the model's multi-error recognition performance under restricted supervision. Extensive comparison and actual deployment experiments verify the effectiveness of the framework. We hope this work could bring new inspiration to the computer vision and medical skills training communities simultaneously. The dataset and the code are publicly available on https://github.com/Shunli-Wang/CPR-Coach.

In the domain of compressive sensing (CS) deep unfolding networks (DUNs) have garnered attention for their good performance and certain degree of interpretability rooted in CS domain achieved by marrying traditional optimization solvers with deep networks. However current DUNs are ill-suited for the intricate task of capturing fine-grained image details leading to perceptible distortions and blurriness in reconstructed images particularly at low CS ratios e.g. 0.10 and below. In this paper we propose CPP-Net a novel deep unfolding CS framework inspired by the primal-dual hybrid strategy of the Chambolle and Pock Proximal Point Algorithm (CP-PPA). First we derive three iteration submodules Xk Vk and Yk by incorporating customized deep learning modules to solve the sparse basis related proximal operator within CP-PPA. Second we design the Dual Path Fusion Block (DPFB) to adeptly extract and fuse multi-scale feature information enhancing sensitivity to feature information at different scales and improving detail reconstruction. Third we introduce the Iteration Fusion Strategy (IFS) to effectively weight the fusion of outputs from diverse reconstruction stages maximizing the utilization of feature information and mitigating the information loss during reconstruction stages. Extensive experiments demonstrate that CPP-Net effectively reduces distortion and blurriness while preserving richer image details outperforming current state-of-the-art methods. Codes are available at https://github.com/ICSResearch/CPP-Net.

This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP) a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology enriches vision language models by leveraging extensive data without needing ground truth annotations. CPLIP involves constructing a pathology-specific dictionary generating textual descriptions for images using language models and retrieving relevant images for each text snippet via a pre-trained model. The model is then fine-tuned using a many-to-many contrastive learning method to align complex interrelated concepts across both modalities. Evaluated across multiple histopathology tasks CPLIP shows notable improvements in zero-shot learning scenarios outperforming existing methods in both interpretability and robustness and setting a higher benchmark for the application of vision-language models in the field. To encourage further research and replication the code for CPLIP is available on GitHubat https://cplip.github.io/

Recently numerous approaches have achieved notable success in compressed video quality enhancement (VQE). However these methods usually ignore the utilization of valuable coding priors inherently embedded in compressed videos such as motion vectors and residual frames which carry abundant temporal and spatial information. To remedy this problem we propose the Coding Priors-Guided Aggregation (CPGA) network to utilize temporal and spatial information from coding priors. The CPGA mainly consists of an inter-frame temporal aggregation (ITA) module and a multi-scale non-local aggregation (MNA) module. Specifically the ITA module aggregates temporal information from consecutive frames and coding priors while the MNA module globally captures spatial information guided by residual frames. In addition to facilitate research in VQE task we newly construct the Video Coding Priors (VCP) dataset comprising 300 videos with various coding priors extracted from corresponding bitstreams. It remedies the shortage of previous datasets on the lack of coding information. Experimental results demonstrate the superiority of our method compared to existing state-of-the-art methods. The code and dataset will be released at https://github.com/VQE-CPGA/CPGA.

The autonomous driving community has shown significant interest in 3D occupancy prediction driven by its exceptional geometric perception and general object recognition capabilities. To achieve this current works try to construct a Tri-Perspective View (TPV) or Occupancy (OCC) representation extending from the Bird-Eye-View perception. However compressed views like TPV representation lose 3D geometry information while raw and sparse OCC representation requires heavy but redundant computational costs. To address the above limitations we propose Compact Occupancy TRansformer (COTR) with a geometry-aware occupancy encoder and a semantic-aware group decoder to reconstruct a compact 3D OCC representation. The occupancy encoder first generates a compact geometrical OCC feature through efficient explicit-implicit view transformation. Then the occupancy decoder further enhances the semantic discriminability of the compact OCC representation by a coarse-to-fine semantic grouping strategy. Empirical experiments show that there are evident performance gains across multiple baselines e.g. COTR outperforms baselines with a relative improvement of 8%-15% demonstrating the superiority of our method.

Deep neural networks (DNNs) often display overconfidence when encountering out-of-distribution (OOD) samples posing significant challenges in real-world applications. Capitalizing on the observation that responses on convolutional kernels are generally more pronounced for in-distribution (ID) samples than for OOD ones this paper proposes the COnvolutional REsponse-based Score (CORES) to exploit these discrepancies for OOD detection. Initially CORES delves into the extremities of convolutional responses by considering both their magnitude and the frequency of significant values. Moreover through backtracking from the most prominent predictions CORES effectively pinpoints sample-relevant kernels across different layers. These kernels which exhibit a strong correlation to input samples are integral to CORES's OOD detection capability. Comprehensive experiments across various ID and OOD settings demonstrate CORES's effectiveness in OOD detection and its superiority to the state-of-the-art methods.

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.

Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt where the model might overlook or entirely fail to produce certain objects. While recent studies propose various solutions they often require customly tailored functions for each of these problems leading to sub-optimal results especially for complex prompts. Our work introduces a novel perspective by tackling this challenge in a contrastive context. Our approach intuitively promotes the segregation of objects in attention maps while also maintaining that pairs of related attributes are kept close to each other. We conducted extensive experiments across a wide variety of scenarios each involving unique combinations of objects attributes and scenes. These experiments effectively showcase the versatility efficiency and flexibility of our method in working with both latent and pixel-based diffusion models including Stable Diffusion and Imagen. Moreover we publicly share our source code to facilitate further research.

While neural rendering has led to impressive advances in scene reconstruction and novel view synthesis it relies heavily on accurately pre-computed camera poses. To relax this constraint multiple efforts have been made to train Neural Radiance Fields (NeRFs) without pre-processed camera poses. However the implicit representations of NeRFs provide extra challenges to optimize the 3D structure and camera poses at the same time. On the other hand the recently proposed 3D Gaussian Splatting provides new opportunities given its explicit point cloud representations. This paper leverages both the explicit geometric representation and the continuity of the input video stream to perform novel view synthesis without any SfM preprocessing. We process the input frames in a sequential manner and progressively grow the 3D Gaussians set by taking one input frame at a time without the need to pre-compute the camera poses. Our method significantly improves over previous approaches in view synthesis and camera pose estimation under large motion changes. Our project page is: https: //oasisyang.github.io/colmap-free-3dgs.