RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically datasets in multispectral pedestrian detection mainly distribute between ROTO (day) and RXTO (night) data; the majority of the pedestrian labels statistically co-occur with their thermal features. As a result multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation such as ROTX data. To address this problem we propose a novel Causal Mode Multiplexer (CMM) framework that effectively learns the causalities between multispectral inputs and predictions. Moreover we construct a new dataset (ROTX-MP) to evaluate modality bias in multispectral pedestrian detection. ROTX-MP mainly includes ROTX examples not presented in previous datasets. Extensive experiments demonstrate that our proposed CMM framework generalizes well on existing datasets (KAIST CVC-14 FLIR) and the new ROTX-MP. Our code and dataset are available open-source.
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We propose the problem of point-level 3D scene interpolation which aims to simultaneously reconstruct a 3D scene in two states from multiple views synthesize smooth point-level interpolations between them and render the scene from novel viewpoints all without any supervision between the states. The primary challenge is on achieving a smooth transition between states that may involve significant and non-rigid changes. To address these challenges we introduce "PAPR in Motion" a novel approach that builds upon the recent Proximity Attention Point Rendering (PAPR) technique which can deform a point cloud to match a significantly different shape and render a visually coherent scene even after non-rigid deformations. Our approach is specifically designed to maintain the temporal consistency of the geometric structure by introducing various regularization techniques for PAPR. The result is a method that can effectively bridge large scene changes and produce visually coherent and temporally smooth interpolations in both geometry and appearance. Evaluation across diverse motion types demonstrates that "PAPR in Motion" outperforms the leading neural renderer for dynamic scenes. For more results and code please visit our project website at https://niopeng.github.io/PAPR-in-Motion/.
Accurate data association is crucial in reducing confusion such as ID switches and assignment errors in multi-object tracking (MOT). However existing advanced methods often overlook the diversity among trajectories and the ambiguity and conflicts present in motion and appearance cues leading to confusion among detections trajectories and associations when performing simple global data association. To address this issue we propose a simple versatile and highly interpretable data association approach called Decomposed Data Association (DDA). DDA decomposes the traditional association problem into multiple sub-problems using a series of non-learning-based modules and selectively addresses the confusion in each sub-problem by incorporating targeted exploitation of new cues. Additionally we introduce Occlusion-aware Non-Maximum Suppression (ONMS) to retain more occluded detections thereby increasing opportunities for association with trajectories and indirectly reducing the confusion caused by missed detections. Finally based on DDA and ONMS we design a powerful multi-object tracker named DeconfuseTrack specifically focused on resolving confusion in MOT. Extensive experiments conducted on the MOT17 and MOT20 datasets demonstrate that our proposed DDA and ONMS significantly enhance the performance of several popular trackers. Moreover DeconfuseTrack achieves state-of-the-art performance on the MOT17 and MOT20 test sets significantly outperforms the baseline tracker ByteTrack in metrics such as HOTA IDF1 AssA. This validates that our tracking design effectively reduces confusion caused by simple global association.
Manually creating 3D environments for AR/VR applications is a complex process requiring expert knowledge in 3D modeling software. Pioneering works facilitate this process by generating room meshes conditioned on textual style descriptions. Yet many of these automatically generated 3D meshes do not adhere to typical room layouts compromising their plausibility e.g. by placing several beds in one bedroom. To address these challenges we present ControlRoom3D a novel method to generate high-quality room meshes. Central to our approach is a user-defined 3D semantic proxy room that outlines a rough room layout based on semantic bounding boxes and a textual description of the overall room style. Our key insight is that when rendered to 2D this 3D representation provides valuable geometric and semantic information to control powerful 2D models to generate 3D consistent textures and geometry that aligns well with the proxy room. Backed up by an extensive study including quantitative metrics and qualitative user evaluations our method generates diverse and globally plausible 3D room meshes thus empowering users to design 3D rooms effortlessly without specialized knowledge.
Despite the recent success of sign language research the widely adopted CNN-based backbones are mainly migrated from other computer vision tasks in which the contours and texture of objects are crucial for identifying objects. They usually treat sign frames as grids and may fail to capture effective cross-region features. In fact sign language tasks need to focus on the correlation of different regions in one frame and the interaction of different regions among adjacent frames for identifying a sign sequence. In this paper we propose to represent a sign sequence as graphs and introduce a simple yet effective graph-based sign language processing architecture named SignGraph to extract cross-region features at the graph level. SignGraph consists of two basic modules: Local Sign Graph (LSG) module for learning the correlation of intra-frame cross-region features in one frame and Temporal Sign Graph (TSG) module for tracking the interaction of inter-frame cross-region features among adjacent frames. With LSG and TSG we build our model in a multiscale manner to ensure that the representation of nodes can capture cross-region features at different granularities. Extensive experiments on current public sign language datasets demonstrate the superiority of our SignGraph model. Our model achieves very competitive performances with the SOTA model while not using any extra cues. Code and models are available at: https://github.com/gswycf/SignGraph.
We introduce Open3DIS a novel solution designed to tackle the problem of Open-Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments exhibit diverse shapes scales and colors making precise instance-level identification a challenging task. Recent advancements in Open-Vocabulary scene understanding have made significant strides in this area by employing class-agnostic 3D instance proposal networks for object localization and learning queryable features for each 3D mask. While these methods produce high-quality instance proposals they struggle with identifying small-scale and geometrically ambiguous objects. The key idea of our method is a new module that aggregates 2D instance masks across frames and maps them to geometrically coherent point cloud regions as high-quality object proposals addressing the above limitations. These are then combined with 3D class-agnostic instance proposals to include a wide range of objects in the real world. To validate our approach we conducted experiments on three prominent datasets including ScanNet200 S3DIS and Replica demonstrating significant performance gains in segmenting objects with diverse categories over the state-of-the-art approaches.
Single-modal object re-identification (ReID) faces great challenges in maintaining robustness within complex visual scenarios. In contrast multi-modal object ReID utilizes complementary information from diverse modalities showing great potentials for practical applications. However previous methods may be easily affected by irrelevant backgrounds and usually ignore the modality gaps. To address above issues we propose a novel learning framework named EDITOR to select diverse tokens from vision Transformers for multi-modal object ReID. We begin with a shared vision Transformer to extract tokenized features from different input modalities. Then we introduce a Spatial-Frequency Token Selection (SFTS) module to adaptively select object-centric tokens with both spatial and frequency information. Afterwards we employ a Hierarchical Masked Aggregation (HMA) module to facilitate feature interactions within and across modalities. Finally to further reduce the effect of backgrounds we propose a Background Consistency Constraint (BCC) and an Object-Centric Feature Refinement (OCFR). They are formulated as two new loss functions which improve the feature discrimination with background suppression. As a result our framework can generate more discriminative features for multi-modal object ReID. Extensive experiments on three multi-modal ReID benchmarks verify the effectiveness of our methods. The code is available at https://github.com/924973292/EDITOR.
Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However the lack of extensively annotated large-scale multi-disease datasets has hindered the achievement of this goal. In this paper we explore the feasibility of leveraging language as a naturally high-quality supervision for chest CT imaging. In light of the limited availability of image-report pairs we bootstrap the understanding of 3D chest CT images by distilling chest-related diagnostic knowledge from an extensively pre-trained 2D X-ray expert model. Specifically we propose a language-guided retrieval method to match each 3D CT image with its semantically closest 2D X-ray image and perform pair-wise and semantic relation knowledge distillation. Subsequently we use contrastive learning to align images and reports within the same patient while distinguishing them from the other patients. However the challenge arises when patients have similar semantic diagnoses such as healthy patients potentially confusing if treated as negatives. We introduce a robust contrastive learning that identifies and corrects these false negatives. We train our model with over 12K pairs of chest CT images and radiology reports. Extensive experiments across multiple scenarios including zero-shot learning report generation and fine-tuning processes demonstrate the model's feasibility in interpreting chest CT images.
Existing approaches to unsupervised video instance segmentation typically rely on motion estimates and experience difficulties tracking small or divergent motions. We present VideoCutLER a simple method for unsupervised multi-instance video segmentation without using motion-based learning signals like optical flow or training on natural videos. Our key insight is that using high-quality pseudo masks and a simple video synthesis method for model training is surprisingly sufficient to enable the resulting video model to effectively segment and track multiple instances across video frames. We show the first competitive unsupervised learning results on the challenging YouTubeVIS-2019 benchmark achieving 50.7% AP50 surpassing the previous state-of-the-art by a large margin. VideoCutLER can also serve as a strong pretrained model for supervised video instance segmentation tasks exceeding DINO by 15.9% on YouTubeVIS-2019 in terms of AP.
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation enabling testing of AD systems and as an advanced training data augmentation technique. However existing methods often require long training times dense semantic supervision or lack generalizability. This in turn hinders the application of NeRFs for AD at scale. In this paper we propose \modelname a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design extensive sensor modeling for both camera and lidar -- including rolling shutter beam divergence and ray dropping -- and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets achieving state-of-the-art performance across the board. To encourage further development we openly release the NeuRAD source code at https://github.com/georghess/NeuRAD.
Head avatars animated by visual signals have gained popularity particularly in cross-driving synthesis where the driver differs from the animated character a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model with a particular focus on its latent space for facial expression descriptors and uncover several limitations with its ability to express intense face motions. Head avatars animated by visual signals have gained popularity particularly in cross-driving synthesis where the driver differs from the animated character a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model with a particular focus on its latent space for facial expression descriptors and uncover several limitations with its ability to express intense face motions. To address these limitations we propose substantial changes in both training pipeline and model architecture to introduce our EMOPortraits model where we: Enhance the model's capability to faithfully support intense asymmetric face expressions setting a new state-of-the-art result in the emotion transfer task surpassing previous methods in both metrics and quality. Incorporate speech-driven mode to our model achieving top-tier performance in audio-driven facial animation making it possible to drive source identity through diverse modalities including visual signal audio or a blend of both.Furthermore we propose a novel multi-view video dataset featuring a wide range of intense and asymmetric facial expressions filling the gap with absence of such data in existing datasets.
Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However the remarkable accuracy of recent MMDE methods is confined to their training domains. These methods fail to generalize to unseen domains even in the presence of moderate domain gaps which hinders their practical applicability. We propose a new model UniDepth capable of reconstructing metric 3D scenes from solely single images across domains. Departing from the existing MMDE methods UniDepth directly predicts metric 3D points from the input image at inference time without any additional information striving for a universal and flexible MMDE solution. In particular UniDepth implements a self-promptable camera module predicting dense camera representation to condition depth features. Our model exploits a pseudo-spherical output representation which disentangles camera and depth representations. In addition we propose a geometric invariance loss that promotes the invariance of camera-prompted depth features. Thorough evaluations on ten datasets in a zero-shot regime consistently demonstrate the superior performance of UniDepth even when compared with methods directly trained on the testing domains. Code and models are available at: github.com/lpiccinelli-eth/unidepth
In this work we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition. Our approach which we call UNITE uses an image teacher model to adapt a video student model to the target domain. UNITE first employs self-supervised pre-training to promote discriminative feature learning on target domain videos using a teacher-guided masked distillation objective. We then perform self-training on masked target data using the video student model and image teacher model together to generate improved pseudolabels for unlabeled target videos. Our self-training process successfully leverages the strengths of both models to achieve strong transfer performance across domains. We evaluate our approach on multiple video domain adaptation benchmarks and observe significant improvements upon previously reported results.
The recent work Local Implicit Image Function (LIIF) and subsequent Implicit Neural Representation (INR) based works have achieved remarkable success in Arbitrary-Scale Super-Resolution (ASSR) by using MLP to decode Low-Resolution (LR) features. However these continuous image representations typically implement decoding in High-Resolution (HR) High-Dimensional (HD) space leading to a quadratic increase in computational cost and seriously hindering the practical applications of ASSR. To tackle this problem we propose a novel Latent Modulated Function (LMF) which decouples the HR-HD decoding process into shared latent decoding in LR-HD space and independent rendering in HR Low-Dimensional (LD) space thereby realizing the first computational optimal paradigm of continuous image representation. Specifically LMF utilizes an HD MLP in latent space to generate latent modulations of each LR feature vector. This enables a modulated LD MLP in render space to quickly adapt to any input feature vector and perform rendering at arbitrary resolution. Furthermore we leverage the positive correlation between modulation intensity and input image complexity to design a Controllable Multi-Scale Rendering (CMSR) algorithm offering the flexibility to adjust the decoding efficiency based on the rendering precision. Extensive experiments demonstrate that converting existing INR-based ASSR methods to LMF can reduce the computational cost by up to 99.9% accelerate inference by up to 57x and save up to 76% of parameters while maintaining competitive performance. The code is available at https://github.com/HeZongyao/LMF.
This paper explores the possibility of extending the capability of pre-trained neural image compressors (e.g. adapting to new data or target bitrates) without breaking backward compatibility the ability to decode bitstreams encoded by the original model. We refer to this problem as continual learning of image compression. Our initial findings show that baseline solutions such as end-to-end fine-tuning do not preserve the desired backward compatibility. To tackle this we propose a knowledge replay training strategy that effectively addresses this issue. We also design a new model architecture that enables more effective continual learning than existing baselines. Experiments are conducted for two scenarios: data-incremental learning and rate-incremental learning. The main conclusion of this paper is that neural image compressors can be fine-tuned to achieve better performance (compared to their pre-trained version) on new data and rates without compromising backward compatibility. The code is publicly available online.
To satisfy the rapidly increasing demands on the large image (2K-8K) super-resolution (SR) prevailing methods follow two independent tracks: 1) accelerate existing networks by content-aware routing and 2) design better super-resolution networks via token mixer refining. Despite directness they encounter unavoidable defects (e.g. inflexible route or non-discriminative processing) limiting further improvements of quality-complexity trade-off. To erase the drawbacks we integrate these schemes by proposing a content-aware mixer (CAMixer) which assigns convolution for simple contexts and additional deformable window-attention for sparse textures. Specifically the CAMixer uses a learnable predictor to generate multiple bootstraps including offsets for windows warping a mask for classifying windows and convolutional attentions for endowing convolution with the dynamic property which modulates attention to include more useful textures self-adaptively and improves the representation capability of convolution. We further introduce a global classification loss to improve the accuracy of predictors. By simply stacking CAMixers we obtain CAMixerSR which achieves superior performance on large-image SR lightweight SR and omnidirectional-image SR.
Significant advancements have been made in image editing with the recent advance of the Diffusion model. However most of the current methods primarily focus on global or subject-level modifications and often face limitations when it comes to editing specific objects when there are other objects coexisting in the scene given solely textual prompts. In response to this challenge we introduce an object-level generative task called Referring Image Editing (RIE) which enables the identification and editing of specific source objects in an image using text prompts. To tackle this task effectively we propose a tailored framework called ReferDiffusion. It aims to disentangle input prompts into multiple embeddings and employs a mixed-supervised multi-stage training strategy. To facilitate further research in this domain we introduce the RefCOCO-Edit dataset comprising images editing prompts source object segmentation masks and reference edited images for training and evaluation. Our extensive experiments demonstrate the effectiveness of our approach in identifying and editing target objects while conventional general image editing and region-based image editing methods have difficulties in this challenging task.
Deep neural networks are known to be overconfident for what they don't know in the wild which is undesirable for decision-making in high-stakes applications. Despite quantities of existing works most of them focus on detecting out-of-distribution (OOD) samples from unseen classes while ignoring large parts of relevant failure sources like misclassified samples from known classes. In particular recent studies reveal that prevalent OOD detection methods are actually harmful for misclassification detection (MisD) indicating that there seems to be a tradeoff between those two tasks. In this paper we study the critical yet under-explored problem of unified failure detection which aims to detect both misclassified and OOD examples. Concretely we identify the failure of simply integrating learning objectives of misclassification and OOD detection and show the potential of sequence learning. Inspired by this we propose a reliable continual learning paradigm whose spirit is to equip the model with MisD ability first and then improve the OOD detection ability without degrading the already adequate MisD performance. Extensive experiments demonstrate that our method achieves strong unified failure detection performance. The code is available at https://github.com/Impression2805/RCL.
In Visual Question Answering (VQA) recognizing and localizing entities pose significant challenges. Pretrained vision-and-language models have addressed this problem by providing a text description as the answer. However in visual scenes with multiple entities textual descriptions struggle to distinguish the entities from the same category effectively. Consequently the VQA dataset is limited by the limitations of text description and cannot adequately cover scenarios involving multiple entities. To address this challenge we introduce a Mask for Align (Mask4Align) method which can determine the entity's position in the given image that best matches the user-input question. This method incorporates colored masks into the image enabling the VQA model to handle discrimination and localization challenges associated with multiple entities. To process an arbitrary number of similar entities Mask4Align is designed hierarchically to discern subtle differences achieving precise localization. Since Mask4Align directly utilizes pre-trained models it does not introduce additional training overhead. Extensive experiments conducted on both the gaze target prediction task dataset and our proposed multi-entity localization dataset showcase the superiority of Mask4Align.
Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However due to the unordered and non-uniform density characteristics of point clouds it is non-trivial to explore the prior knowledge of point clouds and pre-train a point cloud backbone. In this paper we propose a novel pre-training method called Point cloud Diffusion pre-training PointDif. We consider the point cloud pre-training task as a conditional point-to-point generation problem and introduce a conditional point generator. This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object. We also present a recurrent uniform sampling optimization strategy which enables the model to uniformly recover from various noise levels and learn from balanced supervision. Our PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification segmentation and detection. Specifically PointDif attains 70.0% mIoU on S3DIS Area 5 for the segmentation task and achieves an average improvement of 2.4% on ScanObjectNN for the classification task compared to TAP. Furthermore our pre-training framework can be flexibly applied to diverse point cloud backbones and bring considerable gains. Code is available at https://github.com/zhengxiaozx/PointDif