CVPR 2024 论文列表

Comprehensive modeling of the surrounding 3D world is crucial for the success of autonomous driving. However existing perception tasks like object detection road structure segmentation depth & elevation estimation and open-set object localization each only focus on a small facet of the holistic 3D scene understanding task. This divide-and-conquer strategy simplifies the algorithm development process but comes at the cost of losing an end-to-end unified solution to the problem. In this work we address this limitation by studying camera-based 3D panoptic segmentation aiming to achieve a unified occupancy representation for camera-only 3D scene understanding. To achieve this we introduce a novel method called PanoOcc which utilizes voxel queries to aggregate spatiotemporal information from multi-frame and multi-view images in a coarse-to-fine scheme integrating feature learning and scene representation into a unified occupancy representation. We have conducted extensive ablation studies to validate the effectiveness and efficiency of the proposed method. Our approach achieves new state-of-the-art results for camera-based semantic segmentation and panoptic segmentation on the nuScenes dataset. Furthermore our method can be easily extended to dense occupancy prediction and has demonstrated promising performance on the Occ3D benchmark. The code will be made available at https://github.com/Robertwyq/PanoOcc.

Panoramic images enable deeper understanding and more holistic perception of 360 surrounding environment which can naturally encode enriched scene context information compared to standard perspective image. Previous work has made lots of effort to solve the scene understanding task in a hybrid solution based on 2D-3D geometric reasoning thus each sub-task is processed separately and few correlations are explored in this procedure. In this paper we propose a fully 3D method for holistic indoor scene understanding which recovers the objects' shapes oriented bounding boxes and the 3D room layout simultaneously from a single panorama. To maximize the exploration of the rich context information we design a transformer-based context module to predict the representation and relationship among each component of the scene. In addition we introduce a new dataset for scene understanding including photo-realistic panoramas high-fidelity depth images accurately annotated room layouts oriented object bounding boxes and shapes. Experiments on the synthetic and new datasets demonstrate that our method outperforms previous panoramic scene understanding methods in terms of both layout estimation and 3D object detection.

The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs high-quality video-text data is much harder to collect. First of all manual labeling is more time-consuming as it requires an annotator to watch an entire video. Second videos have a temporal dimension consist of a number of scenes stacked together and show multiple actions. Accordingly to establish a video dataset with high-quality captions we propose an automatic approach leveraging multimodal inputs such as textual video description subtitles and individual video frames. Specifically we curate 3.8M high-resolution videos from the publicly available HD-VILA-100M dataset. We then split them into semantically consistent video clips and apply multiple cross-modality teacher models to obtain captions for each video. Next we finetune a retrieval model on a small subset where the best caption of each video is manually selected and then employ the model in the whole dataset to select the best caption as the annotation. In this way we get 70M videos paired with high-quality text captions. We dub the dataset as Panda-70M. We show the value of the proposed dataset on three downstream tasks: video captioning video and text retrieval and text-driven video generation. The models trained on the proposed data score substantially better on the majority of metrics across all the tasks.

The field of autonomous driving increasingly demands high-quality annotated training data. In this paper we propose Panacea an innovative approach to generate panoramic and controllable videos in driving scenarios capable of yielding an unlimited numbers of diverse annotated samples pivotal for autonomous driving advancements. Panacea addresses two critical challenges: 'Consistency' and 'Controllability.' Consistency ensures temporal and cross-view coherence while Controllability ensures the alignment of generated content with corresponding annotations. Our approach integrates a novel 4D attention and a two-stage generation pipeline to maintain coherence supplemented by the ControlNet framework for meticulous control by the Bird's-Eye-View (BEV) layouts. Extensive qualitative and quantitative evaluations of Panacea on the nuScenes dataset prove its effectiveness in generating high-quality multi-view driving-scene videos. This work notably propels the field of autonomous driving by effectively augmenting the training dataset used for advanced BEV perception techniques.

Image and video analysis requires not only accurate object but also the understanding of relationships among detected objects. Common solutions to relation modeling typically resort to stand-alone object detectors followed by non-differentiable post-processing techniques. Recently introduced detection transformers (DETR) perform end-to-end object detection based on a bipartite matching loss. Such methods however lack the ability to jointly detect objects and resolve object associations. In this paper we build on the DETR approach and extend it to the joint detection of objects and their relationships by introducing an approximated bipartite matching. While our method can generalize to an arbitrary number of objects we here focus on the modeling of object pairs and their relations. In particular we apply our method PairDETR to the problem of detecting human bodies and faces and associating them for the same person. Our approach not only eliminates the need for hand-designed post-processing but also achieves excellent results for body-face associations. We evaluate PairDETR on the challenging CrowdHuman and CityPersons datasets and demonstrate a large improvement over the state of the art. Our training code and pre-trained models are available online.

Current vision-language pre-training (VLP) methodologies predominantly depend on paired image-text datasets a resource that is challenging to acquire in radiology due to privacy considerations and labelling complexities. Data augmentation provides a practical solution to overcome the issue of data scarcity however most augmentation methods exhibit a limited focus prioritising either image or text augmentation exclusively. Acknowledging this limitation our objective is to devise a framework capable of concurrently augmenting medical image and text data. We design a Pairwise Augmentation (PairAug) approach that contains an Inter-patient Augmentation (InterAug) branch and an Intra-patient Augmentation (IntraAug) branch. Specifically the InterAug branch of our approach generates radiology images using synthesised yet plausible reports derived from a Large Language Model (LLM). The generated pairs can be considered a collection of new patient cases since they are artificially created and may not exist in the original dataset. In contrast the IntraAug branch uses newly generated reports to manipulate images. This process allows us to create new paired data for each individual with diverse medical conditions. Our extensive experiments on various downstream tasks covering medical image classification zero-shot and fine-tuning analysis demonstrate that our PairAug concurrently expanding both image and text data substantially outperforms image-/text-only expansion baselines and advanced medical VLP baselines. Our code is released at https://github.com/YtongXie/PairAug.

This paper presents Paint3D a novel coarse-to-fine generative framework that is capable of producing high-resolution lighting-less and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs. The key challenge addressed is generating high-quality textures without embedded illumination information which allows the textures to be re-lighted or re-edited within modern graphics pipelines. To achieve this our method first leverages a pre-trained depth-aware 2D diffusion model to generate view-conditional images and perform multi-view texture fusion producing an initial coarse texture map. However as 2D models cannot fully represent 3D shapes and disable lighting effects the coarse texture map exhibits incomplete areas and illumination artifacts. To resolve this we train separate UV Inpainting and UVHD diffusion models specialized for the shape-aware refinement of incomplete areas and the removal of illumination artifacts. Through this coarse-to-fine process Paint3D can produce high-quality 2K UV textures that maintain semantic consistency while being lighting-less significantly advancing the state-of-the-art in texturing 3D objects.

We present Paint-it a text-driven high-fidelity texture map synthesis method for 3D meshes via neural re-parameterized texture optimization. Paint-it synthesizes texture maps from a text description by synthesis-through-optimization exploiting the Score-Distillation Sampling (SDS). We observe that directly applying SDS yields undesirable texture quality due to its noisy gradients. We reveal the importance of texture parameterization when using SDS. Specifically we propose Deep Convolutional Physically-Based Rendering (DC-PBR) parameterization which re-parameterizes the physically-based rendering (PBR) texture maps with randomly initialized convolution-based neural kernels instead of a standard pixel-based parameterization. We show that DC-PBR inherently schedules the optimization curriculum according to texture frequency and naturally filters out the noisy signals from SDS. In experiments Paint-it obtains remarkable quality PBR texture maps within 15 min. given only a text description. We demonstrate the generalizability and practicality of Paint-it by synthesizing high-quality texture maps for large-scale mesh datasets and showing test-time applications such as relighting and material control using a popular graphics engine.

We propose the task of Panoptic Scene Completion (PSC) which extends the recently popular Semantic Scene Completion (SSC) task with instance-level information to produce a richer understanding of the 3D scene. Our PSC proposal utilizes a hybrid mask-based technique on the nonempty voxels from sparse multi-scale completions. Whereas the SSC literature overlooks uncertainty which is critical for robotics applications we instead propose an efficient ensembling to estimate both voxel-wise and instance-wise uncertainties along PSC. This is achieved by building on a multi-input multi-output (MIMO) strategy while improving performance and yielding better uncertainty for little additional compute. Additionally we introduce a technique to aggregate permutation-invariant mask predictions. Our experiments demonstrate that our method surpasses all baselines in both Panoptic Scene Completion and uncertainty estimation on three large-scale autonomous driving datasets. Our code and data are available at https://astra-vision.github.io/PaSCo .

With photo-realistic image generation Neural Radiance Field (NeRF) is widely used for large-scale dynamic scene reconstruction as autonomous driving simulator. However large-scale scene reconstruction still suffers from extremely long training time and rendering time. Low-resolution (LR) rendering combined with upsampling can alleviate this problem but it degrades image quality. In this paper we design a lightweight reference decoder which exploits prior information from known views to improve image reconstruction quality of new views. In addition to speed up prior information search we propose an optical flow and structural similarity based prior information search method. Results on KITTI and VKITTI2 datasets show that our method significantly outperforms the baseline method in terms of training speed rendering speed and rendering quality.

Recent temporal LiDAR-based 3D object detectors achieve promising performance based on the two-stage proposal-based approach. They generate 3D box candidates from the first-stage dense detector followed by different temporal aggregation methods. However these approaches require per-frame objects or whole point clouds posing challenges related to memory bank utilization. Moreover point clouds and trajectory features are combined solely based on concatenation which may neglect effective interactions between them. In this paper we propose a point-trajectory transformer with long short-term memory for efficient temporal 3D object detection. To this end we only utilize point clouds of current-frame objects and their historical trajectories as input to minimize the memory bank storage requirement. Furthermore we introduce modules to encode trajectory features focusing on long short-term and future-aware perspectives and then effectively aggregate them with point cloud features. We conduct extensive experiments on the large-scale Waymo dataset to demonstrate that our approach performs well against state-of-the-art methods. The source codes and trained models will be made publicly available. Code and models will be made publicly available at https://github.com/kuanchihhuang/PTT.

Segment Anything Model (SAM) has achieved impressive performance in many computer vision tasks. However as a large-scale model the immense memory and computation costs hinder its practical deployment. In this paper we propose a post-training quantization (PTQ) framework for Segment Anything Model namely PTQ4SAM. First we investigate the inherent bottleneck of SAM quantization attributed to the bimodal distribution in \cls post-Key-Linear activations. We analyze its characteristics from both per-tensor and per-channel perspectives and propose a Bimodal Integration strategy which utilizes a mathematically equivalent sign operation to transform the bimodal distribution into a relatively easy-quantized normal distribution offline. Second SAM encompasses diverse attention mechanisms (i.e. self-attention and two-way cross-attention) resulting in substantial variations in the post-Softmax distributions. Therefore we introduce an Adaptive Granularity Quantization for Softmax through searching the optimal power-of-two base which is hardware-friendly. Extensive experimental results across various vision tasks (instance segmentation semantic segmentation and object detection) datasets and model variants show the superiority of PTQ4SAM. For example when quantizing SAM-L to 6-bit we achieve lossless accuracy for instance segmentation about 0.5% drop with theoretical 3.9xacceleration. The code is available at https://github.com/chengtao-lv/PTQ4SAM.

Video quality assessment (VQA) is a challenging problem due to the numerous factors that can affect the perceptual quality of a video e.g. content attractiveness distortion type motion pattern and level. However annotating the Mean opinion score (MOS) for videos is expensive and time-consuming which limits the scale of VQA datasets and poses a significant obstacle for deep learning-based methods. In this paper we propose a VQA method named PTM-VQA which leverages PreTrained Models to transfer knowledge from models pretrained on various pre-tasks enabling benefits for VQA from different aspects. Specifically we extract features of videos from different pretrained models with frozen weights and integrate them to generate representation. Since these models possess various fields of knowledge and are often trained with labels irrelevant to quality we propose an Intra-Consistency and Inter-Divisibility (ICID) loss to impose constraints on features extracted by multiple pretrained models. The intra-consistency constraint ensures that features extracted by different pretrained models are in the same unified quality-aware latent space while the inter-divisibility introduces pseudo clusters based on the annotation of samples and tries to separate features of samples from different clusters. Furthermore with a constantly growing number of pretrained models it is crucial to determine which models to use and how to use them. To address this problem we propose an efficient scheme to select suitable candidates. Models with better clustering performance on VQA datasets are chosen to be our candidates. Extensive experiments demonstrate the effectiveness of the proposed method.

Image-level Weakly Supervised Semantic Segmentation (WSSS) has received increasing attention due to its low annotation cost. Class Activation Mapping (CAM) generated through classifier weights in WSSS inevitably ignores certain useful cues while the CAM generated through class prototypes can alleviate that. However because of the different goals of image classification and semantic segmentation the class prototypes still focus on activating primary discriminative pixels learned from classification loss leading to incomplete CAM. In this paper we propose a plugand-play Prototype-based Secondary Discriminative Pixels Mining (PSDPM) framework for enabling class prototypes to activate more secondary discriminative pixels thus generating a more complete CAM. Specifically we introduce a Foreground Pixel Estimation Module (FPEM) for estimating potential foreground pixels based on the correlations between primary and secondary discriminative pixels and the semantic segmentation results of baseline methods. Then we enable WSSS model to learn discriminative features from secondary discriminative pixels through a consistency loss calculated between FPEM result and class-prototype CAM. Experimental results show that our PSDPM improves various baseline methods significantly and achieves new state-of-the-art performances on WSSS benchmarks. Codes are available at https://github.com/xinqiaozhao/PSDPM.

Promptly identifying procedural errors from egocentric videos in an online setting is highly challenging and valuable for detecting mistakes as soon as they happen. This capability has a wide range of applications across various fields such as manufacturing and healthcare. The nature of procedural mistakes is open-set since novel types of failures might occur which calls for one-class classifiers trained on correctly executed procedures. However no technique can currently detect open-set procedural mistakes online. We propose PREGO the first online one-class classification model for mistake detection in PRocedural EGOcentric videos. PREGO is based on an online action recognition component to model the current action and a symbolic reasoning module to predict the next actions. Mistake detection is performed by comparing the recognized current action with the expected future one. We evaluate PREGO on two procedural egocentric video datasets Assembly101 and Epic-tent which we adapt for online benchmarking of procedural mistake detection to establish suitable benchmarks thus defining the Assembly101-O and Epic-tent-O datasets respectively. The code is available at https://github.com/aleflabo/PREGO

Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives. Remarkable success has been achieved in the language domain by using reinforcement learning (RL) to maximize rewards that reflect human preference. However in the vision domain existing RL-based reward finetuning methods are limited by their instability in large-scale training rendering them incapable of generalizing to complex unseen prompts. In this paper we propose Proximal Reward Difference Prediction (PRDP) enabling stable black-box reward finetuning for diffusion models for the first time on large-scale prompt datasets with over 100K prompts. Our key innovation is the Reward Difference Prediction (RDP) objective that has the same optimal solution as the RL objective while enjoying better training stability. Specifically the RDP objective is a supervised regression objective that tasks the diffusion model with predicting the reward difference of generated image pairs from their denoising trajectories. We theoretically prove that the diffusion model that obtains perfect reward difference prediction is exactly the maximizer of the RL objective. We further develop an online algorithm with proximal updates to stably optimize the RDP objective. In experiments we demonstrate that PRDP can match the reward maximization ability of well-established RL-based methods in small-scale training. Furthermore through large-scale training on text prompts from the Human Preference Dataset v2 and the Pick-a-Pic v1 dataset PRDP achieves superior generation quality on a diverse set of complex unseen prompts whereas RL-based methods completely fail.

Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet the first dataset tailored to the preferences of young audiences enabling the generation of aesthetically oriented dances. And it surpasses the AIST++ dataset in music genre diversity and the intricacy and depth of dance movements. Moreover the proposed POPDG model within the iDDPM framework enhances dance diversity and through the Space Augmentation Algorithm strengthens spatial physical connections between human body joints ensuring that increased diversity does not compromise generation quality. A streamlined Alignment Module is also designed to improve the temporal alignment between dance and music. Extensive experiments show that POPDG achieves SOTA results on two datasets. Furthermore the paper also expands on current evaluation metrics. The dataset and code are available at https://github.com/Luke-Luo1/POPDG.

Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy both cumulative and state-wise costs from offline datasets. This arrangement provides an effective approach for the widespread application of RL in high-risk scenarios where both cumulative and state-wise costs need to be considered simultaneously. However previously constrained offline RL algorithms are primarily designed to handle single-constraint problems related to cumulative cost which faces challenges when addressing multi-constraint tasks that involve both cumulative and state-wise costs. In this work we propose a novel Primal policy Optimization with Conservative Estimation algorithm (POCE) to address the problem of multi-constraint offline RL. Concretely we reframe the objective of multi-constraint offline RL by introducing the concept of Maximum Markov Decision Processes (MMDP). Subsequently we present a primal policy optimization algorithm to confront the multi-constraint problems which improves the stability and convergence speed of model training. Furthermore we propose a conditional Bellman operator to estimate cumulative and state-wise Q-values reducing the extrapolation error caused by out-of-distribution (OOD) actions. Finally extensive experiments demonstrate that the POCE algorithm achieves competitive performance across multiple experimental tasks particularly outperforming baseline algorithms in terms of safety. Our code is available at \href https://github.com/guanjiayi/poce github.POCE .

The primary focus of Neural Representation for Videos (NeRV) is to effectively model its spatiotemporal consistency. However current NeRV systems often face a significant issue of spatial inconsistency leading to decreased perceptual quality. To address this issue we introduce the Pyramidal Neural Representation for Videos (PNeRV) which is built on a multi-scale information connection and comprises a lightweight rescaling operator Kronecker Fully-connected layer (KFc) and a Benign Selective Memory (BSM) mechanism. The KFc inspired by the tensor decomposition of the vanilla Fully-connected layer facilitates low-cost rescaling and global correlation modeling. BSM merges high-level features with granular ones adaptively. Furthermore we provide an analysis based on the Universal Approximation Theory of the NeRV system and validate the effectiveness of the proposed PNeRV. We conducted comprehensive experiments to demonstrate that PNeRV surpasses the performance of contemporary NeRV models achieving the best results in video regression on UVG and DAVIS under various metrics (PSNR SSIM LPIPS and FVD). Compared to vanilla NeRV PNeRV achieves a +4.49 dB gain in PSNR and a 231% increase in FVD on UVG along with a +3.28 dB PSNR and 634% FVD increase on DAVIS.