Camera-based Bird's-Eye-View (BEV) perception often struggles between adopting 3D-to-2D or 2D-to-3D view transformation (VT). The 3D-to-2D VT typically employs resource-intensive Transformer to establish robust correspondences between 3D and 2D feature, while the 2D-to-3D VT utilizes the Lift-Splat-Shoot (LSS) pipeline for real-time application, potentially missing distant information. To address these limitations, we propose DualBEV, a unified framework that utilizes a shared feature transformation incorporating three probabilistic measurements for both strategies. By considering dual-view correspondences in one-stage, DualBEV effectively bridges the gap between these strategies, harnessing their individual strengths. Our method achieves state-of-the-art performance without Transformer, delivering comparable efficiency to the LSS approach, with 55.2% mAP and 63.4% NDS on the nuScenes test set. Code will be released.
论文检索
输入标题、作者或关键词，从 2,387 篇学术成果中精准定位
Most existing neural video codecs (NVCs) only extract short-term temporal context by optical flow-based motion compensation. However, such short-term temporal context suffers from error propagation and lacks awareness of long-term relevant information. This limits their performance, particularly in a long prediction chain. In this paper, we address the issue by facilitating the synergy of both long-term and short-term temporal contexts during feature propagation. Specifically, we introduce a Long-term Temporal Context Gathering (LTCG) module to search the diverse and relevant context from the long-term reference feature. The searched long-term context is leveraged to refine the feature propagation by integrating into the short-term reference feature, which can enhance the reconstruction quality and mitigate the propagation errors. During the search process, how to distinguish the helpful context and filter the irrelevant information is challenging and vital. To this end, we cluster the reference feature and perform the searching process in an intra-cluster fashion to improve the context mining. This synergistic integration of long-term and short-term temporal contexts can significantly enhance the temporal correlation modeling. Additionally, to improve the probability estimation in variable-bitrate coding, we introduce the quantization parameter as an extra prior to the entropy model. Comprehensive evaluations demonstrate the effectiveness of our method, which offers an average 11.3\% bitrate saving over the ECM on 1080p video datasets, using the single intra-frame setting.
Early smoke segmentation (ESS) plays a crucial role in accurately locating the source of smoke, facilitating prompt fire rescue operations and gas leak detection. Unlike regular objects, which are typically rigid, opaque, and have clear boundaries, ESS presents challenges due to the large areas of high transparency in early smoke. This leads to a significant similarity between smoke features and the surrounding background features. The key solution is to obtain a discriminative embedding space. Some distance-based methods have pursued this goal by using specific loss functions (e.g., pair-based Triplet loss and proxy-based NCA loss) to constrain the feature extractor. In this paper, we propose a novel approach called discriminative scatter analysis (DSA). Instead of solely measuring Euclidean distance, DSA assesses the compactness and separation of the embedding space from a sample scatter perspective. DSA is performed on both pixel-proxy scatter (IOS) and proxy-proxy scatter (OOS), and a unified loss function is designed to optimize the feature extractor. DSA can be easily integrated with regular segmentation methods. It is applied only during training and without incurring any additional computational cost during inference. Extensive experiments have demonstrated that DSA can consistently improve the performance of various models in ESS.
Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models
PDF ↗Understanding the inherent human knowledge in interacting with a given environment (e.g., affordance) is essential for improving AI to better assist humans. While existing approaches primarily focus on human-object contacts during interactions, such affordance representation cannot fully address other important aspects of human-object interactions (HOIs), i.e., patterns of relative positions and orientations. In this paper, we introduce a novel affordance representation, named Comprehensive Affordance (ComA). Given a 3D object mesh, ComA models the distribution of relative orientation and proximity of vertices in interacting human meshes, capturing plausible patterns of contact, relative orientations, and spatial relationships. To construct the distribution, we present a novel pipeline that synthesizes diverse and realistic 3D HOI samples given any 3D target object mesh. The pipeline leverages a pre-trained 2D inpainting diffusion model to generate HOI images from object renderings and lifts them into 3D. To avoid the generation of false affordances, we propose a new inpainting framework, Adaptive Mask Inpainting. Since ComA is built on synthetic samples, it can extend to any object in an unbounded manner. Through extensive experiments, we demonstrate that ComA outperforms competitors that rely on human annotations in modeling contact-based affordance. Importantly, we also showcase the potential of ComA to reconstruct human-object interactions in 3D through an optimization framework, highlighting its advantage in incorporating both contact and non-contact properties.
Object detection in aerial images is a longstanding yet challenging task. Despite the significant advancements in recent years, most works still show unsatisfactory performance due to the scale variation of objects. A standard strategy to address this problem is multi-scale training, aiming at learning scale-invariant feature representations. Albeit achieving inspiring improvements, such a multi-scale strategy is impractical for real application as inference time increases considerably. Besides, the original images are resized to different scales and subsequently trained separately, lacking information interaction across multi-scales. In this paper, we present a novel method called multi-scale cross distillation (MSCD) to address the above-mentioned issues. MSCD combines the merits of multi-scale training and knowledge distillation, enabling single-scale inference to achieve comparable or superior performance than multi-scale inference. Specifically, we first construct a parallel multi-branch architecture, in which each branch shares the same parameters yet takes images with different scales as input. Furthermore, we design an adaptive cross-scale distillation module that adaptively integrates the knowledge of different branches into a single one. Thus, the detectors trained with MSCD only require single-scale inference. Extensive experiments demonstrate the effectiveness of MSCD. Without bells and whistles, MSCD can facilitate prevalent two-stage detectors to outperform corresponding single-scale models by ~5 mAP and ~7 mAP improvement on DOTA and DIOR-R datasets, respectively.
In this work, we emphasize and demonstrate the importance of visual relation learning for motion forecasting task in autonomous driving (AD). Since exploiting the benefits of RGB images in the existing vision-based joint perception and prediction (PnP) networks is limited in the perception stage, we delve into how the explicit utilization of the visual semantics in motion forecasting can enhance its performance. Specifically, this work proposes ViRR (Visual Relation Reasoning), which aims to provide the prediction module with complex visual reasoning of relationships among scene agents. To achieve this, we construct a novel visual scene graph, where the pairwise visual relations are first aggregated as each agent's node feature. Then, the relations of the nodes are learned via our proposed higher-order relation reasoning method, which leverages the consecutive powers of the graph adjacency matrix. As a result, the extracted complex visual interrelations between the scene agents enable precise forecasting and provide explainable reasons for the model prediction. The proposed module is fully differentiable and thus can be easily applied to any existing vision-based PnP networks. We evaluate the motion forecasting performance of ViRR with challenging nuScenes benchmark and demonstrate its high necessity.
Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for accurately representing scenes. However, despite its superior novel view synthesis capabilities, extracting the geometry of the scene directly from the Gaussian properties remains a challenge, as those are optimized based on a photometric loss. While some concurrent models have tried adding geometric constraints during the Gaussian optimization process, they still produce noisy, unrealistic surfaces. We propose a novel approach for bridging the gap between the noisy 3DGS representation and the smooth 3D mesh representation, by injecting real-world knowledge into the depth extraction process. Instead of extracting the geometry of the scene directly from the Gaussian properties, we instead extract the geometry through a pre-trained stereo-matching model. We render stereo-aligned pairs of images corresponding to the original training poses, feed the pairs into a stereo model to get a depth profile, and finally fuse all of the profiles together to get a single mesh. The resulting reconstruction is smoother, more accurate and shows more intricate details compared to other methods for surface reconstruction from Gaussian Splatting, while only requiring a small overhead on top of the fairly short 3DGS optimization process. We performed extensive testing of the proposed method on in-the-wild scenes, obtained using a smartphone, showcasing its superior reconstruction abilities. Additionally, we tested the method on the Tanks and Temples and DTU benchmarks, achieving state-of-the-art results.
3D object Detection with LiDAR-camera encounters overfitting in algorithm development which is derived from the violation of some fundamental rules. We refer to the data annotation in dataset construction for theoretical optimization and argue that the regression task prediction should not involve the feature from the camera branch. By following the cutting-edge perspective of 'Detecting As Labeling', we propose a novel paradigm dubbed DAL. With the most classical elementary algorithms, a simple predicting pipeline is constructed by imitating the data annotation process. Then we train it in the simplest way to minimize its dependency and strengthen its portability. Though simple in construction and training, the proposed DAL paradigm not only substantially pushes the performance boundary but also provides a superior trade-off between speed and accuracy among all existing methods. With comprehensive superiority, DAL is an ideal baseline for both future work development and practical deployment. The code has been released to facilitate future work.
Recently, transformer-based large vision models, \eg, the Segment Anything Model (SAM) and Stable Diffusion (SD), have achieved remarkable success in the computer vision field. However, the quartic complexity within the transformer's Multi-Head Attention (MHA) leads to substantial computational costs in these models whose inputs and outputs are high-resolution. Although several prior works attempted to alleviate this challenge, none have successfully reduced the complexity and latency of large vision models while preserving their remarkable capabilities without requiring enormous efforts and GPU hours to re-train or fine-tune the models. To address the challenge, we propose a simple yet effective plug-and-play transformer block called Grid-Attention(GridAttn). The GridAttn integrates the proposed Grid Clustering module, Grid Distributing strategies, and Grid Recovering module with common MHA to enhance the large vision models' computational efficiency and preserve their performance without the need for re-training or fine-tuning their parameters. We conduct extensive experiments on recent high-resolution tasks, including zero-shot instance segmentation (SAM, Expedit-SAM), text-to-image generation (Stable Diffusion V2.1), and semantic segmentation (SegFormer B0-B5). The experiments demonstrate that: Without any training or fine-tuning, GridAttn reduces GFlops by the range of [4.6%, 16.1%] and GPU inference latency by [8.2%, 21.4%], all while achieving equivalent performance (the performance bias ratio is less than 1%). Furthermore, the experiments present that GridAttn can also be trained from scratch or fine-tuned with very slight fine-tuning costs, resulting in a significantly improved performance-efficiency tradeoff. As a recommendation, we encourage the community to incorporate our GridAttn whenever deploying a well-trained transformer directly, fine-tuning a pre-trained one, or training a new one from scratch. The source code will be released.
Representing Topological Self-Similarity Using Fractal Feature Maps for Accurate Segmentation of Tubular Structures
PDF ↗Accurate segmentation of long and thin tubular structures is required in a wide variety of areas such as biology, medicine, and remote sensing. The complex topology and geometry of such structures often pose significant technical challenges. A fundamental property of such structures is their topological self-similarity, which can be quantified by fractal features such as fractal dimension (FD). In this study, we incorporate fractal features into a deep learning model by extending FD to the pixel-level using a sliding window technique. The resulting fractal feature maps (FFMs) are then incorporated as additional input to the model and additional weight in the loss function to enhance segmentation performance by utilizing the topological self-similarity. Moreover, we extend the U-Net architecture by incorporating an edge decoder and a skeleton decoder to improve boundary accuracy and skeletal continuity of segmentation, respectively. Extensive experiments on five tubular structure datasets validate the effectiveness and robustness of our approach. Furthermore, the integration of FFMs with other popular segmentation models such as HR-Net also yields performance enhancement, suggesting FFM can be incorporated as a plug-in module with different model architectures. Code and data are openly accessible at https://xxx.
This paper, for the first time, marries large foundation models with human sketch understanding. We demonstrate what this brings -- a paradigm shift in terms of generalised sketch representation learning (e.g., classification). This generalisation happens on two fronts: (i) generalisation across unknown categories (i.e., open-set), and (ii) generalisation traversing abstraction levels (i.e., good and bad sketches), both being timely challenges that remain unsolved in the sketch literature. Our design is intuitive and centred around transferring the already stellar generalisation ability of CLIP to benefit generalised learning for sketches. We first "condition" the vanilla CLIP model by learning sketch-specific prompts using a novel auxiliary head of raster to vector sketch conversion. This importantly makes CLIP "sketch-aware". We then make CLIP acute to the inherently different sketch abstraction levels. This is achieved by learning a codebook of abstraction-specific prompt biases, a weighted combination of which facilitates the representation of sketches across abstraction levels -- low abstract edge-maps, medium abstract sketches in TU-Berlin, and highly abstract doodles in QuickDraw. Our framework surpasses popular sketch representation learning algorithms in both zero-shot and few-shot setups and in novel settings across different abstraction boundaries.
MARs: Multi-view Attention Regularizations for Patch-based Feature Recognition of Space Terrain
PDF ↗The visual detection and tracking of surface terrain is required for spacecraft to safely land on or navigate within close proximity to celestial objects. Current approaches rely on template matching with pre-gathered patch-based features, which are expensive to obtain and a limiting factor in perceptual capability. While recent literature has focused on in-situ detection methods to enhance navigation and operational autonomy, robust description is still needed. In this work, we explore metric learning as the lightweight feature description mechanism and find that current solutions fail to address inter class similarity and multi-view observational geometry. We attribute this to the view-unaware attention mechanism and introduce Multi-view Attention Regularizations (MARs) to constrain the channel and spatial attention across multiple feature views, regularizing the what and where of attention focus. We thoroughly analyze many modern metric learning losses with and without MARs and demonstrate improved terrain-feature recognition performance by upwards of 85%. We additionally introduce the Luna-1 dataset, consisting of Moon crater landmarks and reference navigation frames from NASA mission data to support future research in this difficult task. Luna-1 and source code are publicly available at https://droneslab.github.io/mars/.
Neural Radiance Fields (NeRF) have become increasingly popular for their ability to reconstruct 3D scenes and create new viewpoints with outstanding quality. However, their effectiveness is hindered by rolling shutter (RS) effects commonly found in most camera systems. To solve this, we present RS-NeRF, a method designed to synthesize normal images from novel views using input with RS distortions. This involves a physical model that replicates the image formation process under RS conditions and jointly optimizes NeRF parameters and camera extrinsic for each image row. We further address the inherent shortcomings of the basic RS-NeRF model by delving into RS characteristics and developing algorithms to enhance its functionality. First, we impose a smoothness regularization to better estimate trajectories and improve synthesis quality, in line with the camera movement prior. We also identify and address a fundamental flaw in the vanilla RS model by introducing a multi-sampling algorithm. This new approach greatly improves the model's performance by comprehensively exploiting the RGB data across different rows for each intermediate camera pose. Through rigorous experimentation, we demonstrate that RS-NeRF surpasses previous methods in both synthetic and real-world scenarios, proving its ability to correct RS-related distortions effectively.
SOTA facial expression recognition (FER) methods fail on test sets that have domain gaps with the train set. Recent domain adaptation FER methods need to acquire labeled or unlabeled samples of target domains to fine-tune the FER model, which might be infeasible in real-world deployment. In this paper, we aim to improve the zero-shot generalization ability of FER methods on different unseen test sets using only one train set. Inspired by how humans first detect faces and then select expression features, we propose a novel FER pipeline to extract expression-related features from any given face images. Our method is based on the generalizable face features extracted by large models like CLIP. However, it is non-trivial to adapt the general features of CLIP for specific tasks like FER. To preserve the generalization ability of CLIP and the high precision of the FER model, we design a novel approach that learns sigmoid masks based on the fixed CLIP face features to extract expression features. To further improve the generalization ability on unseen test sets, we separate the channels of the learned masked features according to the expression classes to directly generate logits and avoid using the FC layer to reduce overfitting. We also introduce a channel-diverse loss to make the learned masks separated. Extensive experiments on five different FER datasets verify that our method outperforms SOTA FER methods by large margins. Code is available in the Supp. material.
Human trajectory prediction is typically posed as a zero-shot generalization problem: a predictor is learnt on a dataset of human motion in training scenes, and then deployed on unseen test scenes. While this paradigm has yielded tremendous progress, it fundamentally assumes that trends in human behavior within the deployment scene are constant over time. As such, current prediction models are unable to adapt to scene-specific transient human behaviors, such as crowds temporarily gathering to see buskers, pedestrians hurrying through the rain and avoiding puddles, or a protest breaking out. We formalize the problem of scene-specific adaptive trajectory prediction and propose a new adaptation approach inspired by prompt tuning called latent corridors. By augmenting the input of any pre-trained human trajectory predictor with learnable image prompts, the predictor can improve in the deployment scene by inferring trends from extremely small amounts of new data (e.g., 2 humans observed for 30 seconds). With less than 0.1% additional model parameters, we see up to 23.9% ADE improvement in MOTSynth simulated data and 16.4% ADE in MOT and Wildtrack real pedestrian data. Qualitatively, we observe that latent corridors imbue predictors with an awareness of scene geometry and scene-specific human behaviors that non-adaptive predictors struggle to capture.
Efficient Active Domain Adaptation for Semantic Segmentation by Selecting Information-rich Superpixels
PDF ↗Unsupervised Domain Adaptation (UDA) for semantic segmentation has been widely studied to exploit the label-rich source data to assist the segmentation of unlabeled samples on target domain. Despite these efforts, UDA performance remains far below that of fully-supervised model owing to the lack of target annotations. To this end, we propose an efficient superpixel-level active learning method for domain adaptive semantic segmentation to maximize segmentation performance by automatically querying a small number of superpixels for labeling. To conserve annotation resources, we propose a novel low-uncertainty superpixel fusion module which amalgamates superpixels possessing low-uncertainty features based on feature affinity and thereby ensuring high-quality fusion of superpixels. As for the acquisition strategy, our method takes into account two types of information-rich superpixels: large-size superpixels with substantial information content, and superpixels with the greatest value for domain adaptation learning. Further, we employ the cross-domain mixing and pseudo label with consistency regularization techniques respectively to address the domain shift and label noise problems. Extensive experimentation demonstrates that our proposed superpixel-level method utilizes a limited budget more efficiently than previous pixel-level techniques and surpasses state-of-the-art methods at 40x lower cost.
LASS3D: Language-Assisted Semi-Supervised 3D Semantic Segmentation with Progressive Unreliable Data Exploitation
PDF ↗Precisely annotating large-scale 3D datasets for point cloud segmentation is laborious. To alleviate the annotation burden, several semi-supervised 3D segmentation methods have been proposed in literature. However, two issues remain to be tackled: 1) The utilization of large language-vision models (LVM) in semi-supervised 3D semantic segmentation remains under-explored. 2) The unlabeled points with low-confidence predictions are directly discarded by existing methods. Taking these two issues into consideration, we propose a language-assisted semi-supervised 3D semantic segmentation method named LASS3D, which is built upon the commonly used MeanTeacher framework. In LASS3D, we use two off-the-shelf LVM to generate multi-level captions and leverage the images as the bridge to connect the text data and point clouds. Then, a semantic-aware adaptive fusion module is explored in the student branch, where the semantic information encoded in the embeddings of multi-level captions is injected into 3D features by adaptive fusion and then the semantic information in the text-enhanced 3D features is transferred to the teacher branch by knowledge distillation. In addition, a progressive exploitation strategy is explored for the unreliable points in the teacher branch, which can effectively exploit the information encapsulated in unreliable points via negative learning. Experimental results on both outdoor and indoor datasets demonstrate that LASS3D outperforms the comparative methods in most cases. We will release our code upon publication.
We present a novel framework to reconstruct complete 3D human shapes from a given target image by leveraging monocular unconstrained images. The objective of this work is to reproduce high-quality details in regions of the reconstructed human body that are not visible in the input target. The proposed methodology addresses the limitations of existing approaches for reconstructing 3D human shapes from a single image, which cannot reproduce shape details in occluded body regions. The missing information of the monocular input can be recovered by using multiple views captured from multiple cameras. However, multi-view reconstruction methods necessitate accurately calibrated and registered images, which can be challenging to obtain in real-world scenarios. Given a target RGB image and a collection of multiple uncalibrated and unregistered images of the same individual, acquired using a single camera, we propose a novel framework to generate complete 3D human shapes. We introduce a novel module to generate 2D multi-view normal maps of the person registered with the target input image. The module consists of body part-based reference selection and body part-based registration. The generated 2D normal maps are then processed by a multi-view attention-based neural implicit model that estimates an implicit representation of the 3D shape, ensuring the reproduction of details in both observed and occluded regions. Extensive experiments demonstrate that the proposed approach estimates higher quality details in the occluded regions of the 3D clothed human shapes from a set of unconstrained images compared to related methods, without the use of parametric models.
Image Matching is a core component of all best-performing algorithms and pipelines in 3D vision. Yet despite matching being fundamentally a 3D problem, intrinsically linked to camera pose and scene geometry, it is typically treated as a 2D problem. This makes sense as the goal of matching is to establish correspondences between 2D pixel fields, but also seems like a potentially hazardous choice. In this work, we take a different stance and propose to cast matching as a 3D task with DUSt3R, a recent and powerful 3D reconstruction framework based on Transformers. Based on pointmaps regression, this method displayed impressive robustness in matching views with extreme viewpoint changes, yet with limited accuracy. We aim here to improve the matching capabilities of such an approach while preserving its robustness. We thus propose to augment the DUSt3R network with a new head that outputs dense local features, trained with an additional matching loss. We further address the issue of quadratic complexity of dense matching, which becomes prohibitively slow for downstream applications if not treated carefully. We introduce a fast reciprocal matching scheme that not only accelerates matching by orders of magnitude, but also comes with theoretical guarantees and, lastly, yields improved results. Extensive experiments show that our approach, coined MASt3R, significantly outperforms the state of the art on multiple matching tasks. In particular, it beats the best published methods by 230% (relative improvement) in VCRE Precision on the extremely challenging Map-free localization dataset.
We present MoE-DiffIR, an innovative universal compressed image restoration (CIR) method with task-customized diffusion priors. This intends to handle two pivotal challenges in the existing CIR methods: (i) lacking adaptability and universality for different image codecs, \eg, JPEG and WebP; (ii) poor texture generation capability, particularly at low bitrates. Specifically, our MoE-DiffIR develops the powerful mixture-of-experts (MoE) prompt module, where some basic prompts cooperate to excavate the task-customized diffusion priors from Stable Diffusion (SD) for each compression task. Moreover, the degradation-aware routing mechanism is proposed to enable the flexible assignment of basic prompts. To activate and reuse the cross-modality generation prior of SD, we design the visual-to-text adapter for MoE-DiffIR, which aims to adapt the embedding of low-quality images from the visual domain to the textual domain as the textual guidance for SD, enabling more consistent and reasonable texture generation. We also construct one comprehensive benchmark dataset for universal CIR, covering 21 types of degradations from 7 popular traditional and learned codecs. Extensive experiments on universal CIR have demonstrated the excellent robustness and texture restoration capability of our proposed MoE-DiffIR.