Object Detection

Referring multi-object tracking (RMOT) aims to track multiple objects based on input textual descriptions. Previous works realize it by simply integrating an extra textual module into the multi-object tracker. However they typically need to retrain the entire framework and have difficulties in optimization. In this work we propose an insertable Knowledge Unification Network termed iKUN to enable communication with off-the-shelf trackers in a plug-and-play manner. Concretely a knowledge unification module (KUM) is designed to adaptively extract visual features based on textual guidance. Meanwhile to improve the localization accuracy we present a neural version of Kalman filter (NKF) to dynamically adjust process noise and observation noise based on the current motion status. Moreover to address the problem of open-set long-tail distribution of textual descriptions a test-time similarity calibration method is proposed to refine the confidence score with pseudo frequency. Extensive experiments on Refer-KITTI dataset verify the effectiveness of our framework. Finally to speed up the development of RMOT we also contribute a more challenging dataset Refer-Dance by extending public DanceTrack dataset with motion and dressing descriptions. The codes and dataset are available at https://github.com/dyhBUPT/iKUN.

Out-of-distribution (OOD) detection has attracted a large amount of attention from the machine learning research community in recent years due to its importance in deployed systems. Most of the previous studies focused on the detection of OOD samples in the multi-class classification task. However OOD detection in the multi-label classification task a more common real-world use case remains an underexplored domain. In this research we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task. Object detection models have an inherent ability to distinguish between objects of interest (in-distribution data) and irrelevant objects (OOD data) in images that contain multiple objects belonging to different class categories. These abilities allow us to convert a regular object detection model into an image classifier with inherent OOD detection capabilities with just minor changes. We compare our approach to state-of-the-art OOD detection methods and demonstrate YolOOD's ability to outperform these methods on a comprehensive suite of in-distribution and OOD benchmark datasets.

The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation we introduce YOLO-World an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically we propose a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset YOLO-World achieves 35.4 AP with 52.0 FPS on V100 which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore the fine-tuned YOLO-World achieves remarkable performance on several downstream tasks including object detection and open-vocabulary instance segmentation. Code and models are available at https://github.com/AILab-CVC/YOLO-World

We introduce WildlifeMapper (WM) a flexible model designed to detect locate and identify multiple species in aerial imagery. It addresses the limitations of traditional labor-intensive wildlife population assessments that are central to advancing environmental conservation efforts worldwide. While a number of methods exist to automate this process they are often limited in their ability to generalize to different species or landscapes due to the dominance of homogeneous backgrounds and/or poorly captured local image structures. WM introduces two novel modules that help to capture the local structure and context of objects of interest to accurately localize and identify them achieving a state-of-the-art (SOTA) detection rate of 0.56 mAP. Further we introduce a large aerial imagery dataset with more than 11k Images and 28k annotations verified by trained experts. WM also achieves SOTA performance on 3 other publicly available aerial survey datasets collected across 4 different countries improving mAP by 42%. Source code and trained models are available at Github

It is a well-known fact that the performance of deep learning models deteriorates when they encounter a distribution shift at test time. Test-time adaptation (TTA) algorithms have been proposed to adapt the model online while inferring test data. However existing research predominantly focuses on classification tasks through the optimization of batch normalization layers or classification heads but this approach limits its applicability to various model architectures like Transformers and makes it challenging to apply to other tasks such as object detection. In this paper we propose a novel online adaption approach for object detection in continually changing test domains considering which part of the model to update how to update it and when to perform the update. By introducing architecture-agnostic and lightweight adaptor modules and only updating these while leaving the pre-trained backbone unchanged we can rapidly adapt to new test domains in an efficient way and prevent catastrophic forgetting. Furthermore we present a practical and straightforward class-wise feature aligning method for object detection to resolve domain shifts. Additionally we enhance efficiency by determining when the model is sufficiently adapted or when additional adaptation is needed due to changes in the test distribution. Our approach surpasses baselines on widely used benchmarks achieving improvements of up to 4.9%p and 7.9%p in mAP for COCO ? COCO-corrupted and SHIFT respectively while maintaining about 20 FPS or higher. The implementation code is available at https://github.com/natureyoo/ContinualTTA_ObjectDetection.

Monocular 3D detection (M3D) aims for precise 3D object localization from a single-view image which usually involves labor-intensive annotation of 3D detection boxes. Weakly supervised M3D has recently been studied to obviate the 3D annotation process by leveraging many existing 2D annotations but it often requires extra training data such as LiDAR point clouds or multi-view images which greatly degrades its applicability and usability in various applications. We propose SKD-WM3D a weakly supervised monocular 3D detection framework that exploits depth information to achieve M3D with a single-view image exclusively without any 3D annotations or other training data. One key design in SKD-WM3D is a self-knowledge distillation framework which transforms image features into 3D-like representations by fusing depth information and effectively mitigates the inherent depth ambiguity in monocular scenarios with little computational overhead in inference. In addition we design an uncertainty-aware distillation loss and a gradient-targeted transfer modulation strategy which facilitate knowledge acquisition and knowledge transfer respectively. Extensive experiments show that SKD-WM3D surpasses the state-of-the-art clearly and is even on par with many fully supervised methods.

Visible-infrared (RGB-IR) image fusion has shown great potentials in object detection based on unmanned aerial vehicles (UAVs). However the weakly misalignment problem between multimodal image pairs limits its performance in object detection. Most existing methods often ignore the modality gap and emphasize a strict alignment resulting in an upper bound of alignment quality and an increase of implementation costs. To address these challenges we propose a novel method named Offset-guided Adaptive Feature Alignment (OAFA) which could adaptively adjust the relative positions between multimodal features. Considering the impact of modality gap on the cross-modality spatial matching a Cross-modality Spatial Offset Modeling (CSOM) module is designed to establish a common subspace to estimate the precise feature-level offsets. Then an Offset-guided Deformable Alignment and Fusion (ODAF) module is utilized to implicitly capture optimal fusion positions for detection task rather than conducting a strict alignment. Comprehensive experiments demonstrate that our method not only achieves state-of-the-art performance in the UAVs-based object detection task but also shows strong robustness to the weakly misalignment problem.

This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection. Current methods often rely on supplementary modules or specific architectural designs potentially limiting their applicability to new and evolving architectures. To our knowledge we are the first to propose a versatile technique that seamlessly integrates into any existing framework for 3D Object Detection marking the first instance of Weak-to-Strong generalization in 3D computer vision. We introduce a novel framework X-Ray Distillation with Object-Complete Frames suitable for both supervised and semi-supervised settings that leverages the temporal aspect of point cloud sequences. This method extracts crucial information from both previous and subsequent LiDAR frames creating Object-Complete frames that represent objects from multiple viewpoints thus addressing occlusion and sparsity. Given the limitation of not being able to generate Object-Complete frames during online inference we utilize Knowledge Distillation within a Teacher-Student framework. This technique encourages the strong Student model to emulate the behavior of the weaker Teacher which processes simple and informative Object-Complete frames effectively offering a comprehensive view of objects as if seen through X-ray vision. Our proposed methods surpass state-of-the-art in semi-supervised learning by 1-1.5 mAP and enhance the performance of five established supervised models by 1-2 mAP on standard autonomous driving datasets even with default hyperparameters. Code for Object-Complete frames is available here: https://github.com/sakharok13/X-Ray-Teacher-Patching-Tools.

Monocular 3D object detection poses a significant challenge in 3D scene understanding due to its inherently ill-posed nature in monocular depth estimation. Existing methods heavily rely on supervised learning using abundant 3D labels typically obtained through expensive and labor-intensive annotation on LiDAR point clouds. To tackle this problem we propose a novel weakly supervised 3D object detection framework named VSRD (Volumetric Silhouette Rendering for Detection) to train 3D object detectors without any 3D supervision but only weak 2D supervision. VSRD consists of multi-view 3D auto-labeling and subsequent training of monocular 3D object detectors using the pseudo labels generated in the auto-labeling stage. In the auto-labeling stage we represent the surface of each instance as a signed distance field (SDF) and render its silhouette as an instance mask through our proposed instance-aware volumetric silhouette rendering. To directly optimize the 3D bounding boxes through rendering we decompose the SDF of each instance into the SDF of a cuboid and the residual distance field (RDF) that represents the residual from the cuboid. This mechanism enables us to optimize the 3D bounding boxes in an end-to-end manner by comparing the rendered instance masks with the ground truth instance masks. The optimized 3D bounding boxes serve as effective training data for 3D object detection. We conduct extensive experiments on the KITTI-360 dataset demonstrating that our method outperforms the existing weakly supervised 3D object detection methods. The code is available at https://github.com/skmhrk1209/VSRD.

The significant amount of manual efforts in annotating pixel-level labels has triggered the advancement of unsupervised saliency learning. However without supervision signals state-of-the-art methods can only infer region-level saliency. In this paper we propose to explore the unsupervised salient instance detection (USID) problem for a more fine-grained visual understanding. Our key observation is that self-supervised transformer features may exhibit local similarities as well as different levels of contrast to other regions which provide informative cues to identify salient instances. Hence we propose SCoCo a novel network that models saliency coherence and contrast for USID. SCoCo includes two novel modules: (1) a global background adaptation (GBA) module with a scene-level contrastive loss to extract salient regions from the scene by searching the adaptive "saliency threshold" in the self-supervised transformer features and (2) a locality-aware similarity (LAS) module with an instance-level contrastive loss to group salient regions into instances by modeling the in-region saliency coherence and cross-region saliency contrasts. Extensive experiments show that SCoCo outperforms state-of-the-art weakly-supervised SID methods and carefully designed unsupervised baselines and has comparable performances to fully-supervised SID methods.

Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics e.g. for embodied agents or to train 3D generative models. However so far methods that estimate the category-level object pose require either large amounts of human annotations CAD models or input from RGB-D sensors. In contrast we tackle the problem of learning to estimate the category-level 3D pose only from casually taken object-centric videos without human supervision. We propose a two-step pipeline: First we introduce a multi-view alignment procedure that determines canonical camera poses across videos with a novel and robust cyclic distance formulation for geometric and appearance matching using reconstructed coarse meshes and DINOv2 features. In a second step the canonical poses and reconstructed meshes enable us to train a model for 3D pose estimation from a single image. In particular our model learns to estimate dense correspondences between images and a prototypical 3D template by predicting for each pixel in a 2D image a feature vector of the corresponding vertex in the template mesh. We demonstrate that our method outperforms all baselines at the unsupervised alignment of object-centric videos by a large margin and provides faithful and robust predictions in-the-wild on the Pascal3D+ and ObjectNet3D datasets.

Deep Convolutional Neural Networks (DCNNs) have achieved remarkable performance in synthetic aperture radar (SAR) object detection but this comes at the cost of tremendous computational resources partly due to extracting redundant features within a single convolutional layer. Recent works either delve into model compression methods or focus on the carefully-designed lightweight models both of which result in performance degradation. In this paper we propose an efficient convolution module for SAR object detection called SFS-Conv which increases feature diversity within each convolutional layer through a shunt-perceive-select strategy. Specifically we shunt input feature maps into space and frequency aspects. The former perceives the context of various objects by dynamically adjusting receptive field while the latter captures abundant frequency variations and textural features via fractional Gabor transformer. To adaptively fuse features from space and frequency aspects a parameter-free feature selection module is proposed to ensure that the most representative and distinctive information are preserved. With SFS-Conv we build a lightweight SAR object detection network called SFS-CNet. Experimental results show that SFS-CNet outperforms state-of-the-art (SoTA) models on a series of SAR object detection benchmarks while simultaneously reducing both the model size and computational cost.

Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality which can be interpreted as generalization across various distributions of training or test data. More precisely for novelty detection distribution shifts may occur in the training set or the test set. Shifts in the training set refer to cases where we train a novelty detector on a new dataset and expect strong transferability. Conversely distribution shifts in the test set indicate the methods' performance when the trained model encounters a shifted test sample. We experimentally show that existing methods falter in maintaining universality which stems from their rigid inductive biases. Motivated by this we aim for more generalized techniques that have more adaptable inductive biases. In this context we leverage the fact that contrastive learning provides an efficient framework to easily switch and adapt to new inductive biases through the proper choice of augmentations in forming the negative pairs. We propose a novel probabilistic auto-negative pair generation method AutoAugOOD along with contrastive learning to yield a universal novelty detector method. Our experiments demonstrate the superiority of our method under different distribution shifts in various image benchmark datasets. Notably our method emerges universality in the lens of adaptability to different setups of novelty detection including one-class unlabeled multi-class and labeled multi-class settings.

We present UnionFormer a novel framework that integrates tampering clues across three views by unified learning for image manipulation detection and localization. Specifically we construct a BSFI-Net to extract tampering features from RGB and noise views achieving enhanced responsiveness to boundary artifacts while modulating spatial consistency at different scales. Additionally to explore the inconsistency between objects as a new view of clues we combine object consistency modeling with tampering detection and localization into a three-task unified learning process allowing them to promote and improve mutually. Therefore we acquire a unified manipulation discriminative representation under multi-scale supervision that consolidates information from three views. This integration facilitates highly effective concurrent detection and localization of tampering. We perform extensive experiments on diverse datasets and the results show that the proposed approach outperforms state-of-the-art methods in tampering detection and localization.

In the context of autonomous driving the significance of effective feature learning is widely acknowledged. While conventional 3D self-supervised pre-training methods have shown widespread success most methods follow the ideas originally designed for 2D images. In this paper we present UniPAD a novel self-supervised learning paradigm applying 3D volumetric differentiable rendering. UniPAD implicitly encodes 3D space facilitating the reconstruction of continuous 3D shape structures and the intricate appearance characteristics of their 2D projections. The flexibility of our method enables seamless integration into both 2D and 3D frameworks enabling a more holistic comprehension of the scenes. We manifest the feasibility and effectiveness of UniPAD by conducting extensive experiments on various 3D perception tasks. Our method significantly improves lidar- camera- and lidar-camera-based baseline by 9.1 7.7 and 6.9 NDS respectively. Notably our pre-training pipeline achieves 73.2 NDS for 3D object detection and 79.4 mIoU for 3D semantic segmentation on the nuScenes validation set achieving state-of-the-art results in comparison with previous methods.

Realizing unified monocular 3D object detection including both indoor and outdoor scenes holds great importance in applications like robot navigation. However involving various scenarios of data to train models poses challenges due to their significantly different characteristics e.g. diverse geometry properties and heterogeneous domain distributions. To address these challenges we build a detector based on the bird's-eye-view (BEV) detection paradigm where the explicit feature projection is beneficial to addressing the geometry learning ambiguity when employing multiple scenarios of data to train detectors. Then we split the classical BEV detection architecture into two stages and propose an uneven BEV grid design to handle the convergence instability caused by the aforementioned challenges. Moreover we develop a sparse BEV feature projection strategy to reduce computational cost and a unified domain alignment method to handle heterogeneous domains. Combining these techniques a unified detector UniMODE is derived which surpasses the previous state-of-the-art on the challenging Omni3D dataset (a large-scale dataset including both indoor and outdoor scenes) by 4.9% \rm AP_ 3D revealing the first successful generalization of a BEV detector to unified 3D object detection.

Single-source domain generalization (SDG) for object detection is a challenging yet essential task as the distribution bias of the unseen domain degrades the algorithm performance significantly. However existing methods attempt to extract domain-invariant features neglecting that the biased data leads the network to learn biased features that are non-causal and poorly generalizable. To this end we propose an Unbiased Faster R-CNN (UFR) for generalizable feature learning. Specifically we formulate SDG in object detection from a causal perspective and construct a Structural Causal Model (SCM) to analyze the data bias and feature bias in the task which are caused by scene confounders and object attribute confounders. Based on the SCM we design a Global-Local Transformation module for data augmentation which effectively simulates domain diversity and mitigates the data bias. Additionally we introduce a Causal Attention Learning module that incorporates a designed attention invariance loss to learn image-level features that are robust to scene confounders. Moreover we develop a Causal Prototype Learning module with an explicit instance constraint and an implicit prototype constraint which further alleviates the negative impact of object attribute confounders. Experimental results on five scenes demonstrate the prominent generalization ability of our method with an improvement of 3.9% mAP on the Night-Clear scene.

Visual prompting of large vision language models such as CLIP exhibits intriguing zero-shot capabilities. A manually drawn red circle commonly used for highlighting can guide CLIP's attention to the surrounding region to identify specific objects within an image. Without precise object proposals however it is insufficient for localization. Our novel simple yet effective approach i.e. Differentiable Visual Prompting enables CLIP to zero-shot localize: given an image and a text prompt describing an object we first pick a rendered ellipse from uniformly distributed anchor ellipses on the image grid via visual prompting then use three loss functions to tune the ellipse coefficients to encapsulate the target region gradually. This yields promising experimental results for referring expression comprehension without precisely specified object proposals. In addition we systematically present the limitations of visual prompting inherent in CLIP and discuss potential solutions.

Deepfake detection faces a critical generalization hurdle with performance deteriorating when there is a mismatch between the distributions of training and testing data. A broadly received explanation is the tendency of these detectors to be overfitted to forgery-specific artifacts rather than learning features that are widely applicable across various forgeries. To address this issue we propose a simple yet effective detector called LSDA (\underline L atent \underline S pace \underline D ata \underline A ugmentation) which is based on a heuristic idea: representations with a wider variety of forgeries should be able to learn a more generalizable decision boundary thereby mitigating the overfitting of method-specific features (see Fig. 1). Following this idea we propose to enlarge the forgery space by constructing and simulating variations within and across forgery features in the latent space. This approach encompasses the acquisition of enriched domain-specific features and the facilitation of smoother transitions between different forgery types effectively bridging domain gaps. Our approach culminates in refining a binary classifier that leverages the distilled knowledge from the enhanced features striving for a generalizable deepfake detector. Comprehensive experiments show that our proposed method is surprisingly effective and transcends state-of-the-art detectors across several widely used benchmarks.