Pre-trained vision-language models (VLMs) have achieved high performance on various downstream tasks which have been widely used for visual grounding tasks in a weakly supervised manner. However despite the performance gains contributed by large vision and language pre-training we find that state-of-the-art VLMs struggle with compositional reasoning on grounding tasks. To demonstrate this we propose Attribute Relation and Priority grounding (ARPGrounding) benchmark to test VLMs' compositional reasoning ability on visual grounding tasks. ARPGrounding contains 11425 samples and evaluates the compositional understanding of VLMs in three dimensions: 1) attribute denoting comprehension of objects' properties; 2) relation indicating an understanding of relation between objects; 3) priority reflecting an awareness of the part of speech associated with nouns. Using the ARPGrounding benchmark we evaluate several mainstream VLMs. We empirically find that these models perform quite well on conventional visual grounding datasets achieving performance comparable to or surpassing state-of-the-art methods but showing strong deficiencies in compositional reasoning. Furthermore we propose a composition-aware fine-tuning pipeline demonstrating the potential to leverage cost-effective image-text annotations for enhancing the compositional understanding of VLMs in grounding tasks.
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Different from a unimodal model whose input is from a single modality the input (called multi-modal input) of a multi-modal model is from multiple modalities such as image 3D points audio text etc. Similar to unimodal models many existing studies show that a multi-modal model is also vulnerable to adversarial perturbation where an attacker could add small perturbation to all modalities of a multi-modal input such that the multi-modal model makes incorrect predictions for it. Existing certified defenses are mostly designed for unimodal models which achieve sub-optimal certified robustness guarantees when extended to multi-modal models as shown in our experimental results. In our work we propose MMCert the first certified defense against adversarial attacks to a multi-modal model. We derive a lower bound on the performance of our MMCert under arbitrary adversarial attacks with bounded perturbations to both modalities (e.g. in the context of auto-driving we bound the number of changed pixels in both RGB image and depth image). We evaluate our MMCert using two benchmark datasets: one for the multi-modal road segmentation task and the other for the multi-modal emotion recognition task. Moreover we compare our MMCert with a state-of-the-art certified defense extended from unimodal models. Our experimental results show that our MMCert outperforms the baseline.
Existing counting tasks are limited to the class level which don't account for fine-grained details within the class. In real applications it often requires in-context or referring human input for counting target objects. Take urban analysis as an example fine-grained information such as traffic flow in different directions pedestrians and vehicles waiting or moving at different sides of the junction is more beneficial. Current settings of both class-specific and class-agnostic counting treat objects of the same class indifferently which pose limitations in real use cases. To this end we propose a new task named Referring Expression Counting (REC) which aims to count objects with different attributes within the same class. To evaluate the REC task we create a novel dataset named REC-8K which contains 8011 images and 17122 referring expressions. Experiments on REC-8K show that our proposed method achieves state-of-the-art performance compared with several text-based counting methods and an open-set object detection model. We also outperform prior models on the class agnostic counting (CAC) benchmark [36] for the zero-shot setting and perform on par with the few-shot methods. Code and dataset is available at https://github.com/sydai/referring-expression-counting.
Recent transformer-based architectures have shown impressive results in the field of image segmentation. Thanks to their flexibility they obtain outstanding performance in multiple segmentation tasks such as semantic and panoptic under a single unified framework. To achieve such impressive performance these architectures employ intensive operations and require substantial computational resources which are often not available especially on edge devices. To fill this gap we propose Prototype-based Efficient MaskFormer (PEM) an efficient transformer-based architecture that can operate in multiple segmentation tasks. PEM proposes a novel prototype-based cross-attention which leverages the redundancy of visual features to restrict the computation and improve the efficiency without harming the performance. In addition PEM introduces an efficient multi-scale feature pyramid network capable of extracting features that have high semantic content in an efficient way thanks to the combination of deformable convolutions and context-based self-modulation. We benchmark the proposed PEM architecture on two tasks semantic and panoptic segmentation evaluated on two different datasets Cityscapes and ADE20K. PEM demonstrates outstanding performance on every task and dataset outperforming task-specific architectures while being comparable and even better than computationally expensive baselines. Code is available at https://github.com/NiccoloCavagnero/PEM.
Intelligent generation of vector graphics has very promising applications in the fields of advertising and logo design artistic painting animation production etc. However current mainstream vector image generation methods lack the encoding of image appearance information that is associated with the original vector representation and therefore lose valid supervision signal from the strong correlation between the discrete vector parameter (drawing instruction) sequence and the target shape/structure of the corresponding pixel image. On the one hand the generation process based on pure vector domain completely ignores the similarity measurement between shape parameter (and their combination) and the paired pixel image appearance pattern; on the other hand two-stage methods (i.e. generation-and-vectorization) based on pixel diffusion followed by differentiable image-to-vector translation suffer from wrong error-correction signal caused by approximate gradients. To address the above issues we propose a novel generation framework based on dual-domain (vector-pixel) diffusion with cross-modality impulse signals from each other. First in each diffusion step the current representation extracted from the other domain is used as a condition variable to constrain the subsequent sampling operation yielding shape-aware new parameterizations; second independent supervision signals from both domains avoid the gradient error accumulation problem caused by cross-domain representation conversion. Extensive experimental results on popular benchmarks including font and icon datasets demonstrate the great advantages of our proposed framework in terms of generated shape quality.
Video summarization aims to generate a concise representation of a video capturing its essential content and key moments while reducing its overall length. Although several methods employ attention mechanisms to handle long-term dependencies they often fail to capture the visual significance inherent in frames. To address this limitation we propose a CNN-based SpatioTemporal Attention (CSTA) method that stacks each feature of frames from a single video to form image-like frame representations and applies 2D CNN to these frame features. Our methodology relies on CNN to comprehend the inter and intra-frame relations and to find crucial attributes in videos by exploiting its ability to learn absolute positions within images. In contrast to previous work compromising efficiency by designing additional modules to focus on spatial importance CSTA requires minimal computational overhead as it uses CNN as a sliding window. Extensive experiments on two benchmark datasets (SumMe and TVSum) demonstrate that our proposed approach achieves state-of-the-art performance with fewer MACs compared to previous methods. Codes are available at https://github.com/thswodnjs3/CSTA.
Place Recognition is crucial for unmanned vehicles in terms of localization and mapping. Recent years have witnessed numerous explorations in the field where 2D cameras and 3D LiDARs are mostly employed. Despite their admirable performance they may encounter challenges in adverse weather such as rain and fog. Hopefully 4D millimeter-wave Radar emerges as a promising alternative as its longer wavelength makes it virtually immune to interference from tiny particles of fog and rain. Therefore in this work we propose a novel 4D Radar place recognition model TransLoc4D based on sparse convolution and Transformer structures. Specifically a Minkloc4D backbone is first proposed to leverage the geometric intensity and velocity information from 4D Radar scans. While mainstream 3D LiDAR solutions merely capture geometric structures of point clouds Minkloc4D explores the intensity and velocity properties of 4D Radar scans and demonstrates their effectiveness. After feature extraction a Transformer layer is introduced to enhance local features where linear self-attention captures the long-range dependency of point cloud alleviating its sparsity and noise. To validate TransLoc4D we construct two datasets and set up benchmarks for 4D radar place recognition. Experiments show TransLoc4D is feasible and can robustly deal with dynamic and adverse environments.
Partial-label learning (PLL) is an important weakly supervised learning problem which allows each training example to have a candidate label set instead of a single ground-truth label. Identification-based methods have been widely explored to tackle label ambiguity issues in PLL which regard the true label as a latent variable to be identified. However identifying the true labels accurately and completely remains challenging causing noise in pseudo labels during model training. In this paper we propose a new method called CroSel which leverages historical predictions from the model to identify true labels for most training examples. First we introduce a cross selection strategy which enables two deep models to select true labels of partially labeled data for each other. Besides we propose a novel consistency regularization term called co-mix to avoid sample waste and tiny noise caused by false selection. In this way CroSel can pick out the true labels of most examples with high precision. Extensive experiments demonstrate the superiority of CroSel which consistently outperforms previous state-of-the-art methods on benchmark datasets. Additionally our method achieves over 90% accuracy and quantity for selecting true labels on CIFAR-type datasets under various settings.
Semantic segmentation models while effective for in-distribution categories face challenges in real-world deployment due to encountering out-of-distribution (OoD) objects. Detecting these OoD objects is crucial for safety-critical applications. Existing methods rely on anomaly scores but choosing a suitable threshold for generating masks presents difficulties and can lead to fragmentation and inaccuracy. This paper introduces a method to convert anomaly Score To segmentation Mask called S2M a simple and effective framework for OoD detection in semantic segmentation. Unlike assigning anomaly scores to pixels S2M directly segments the entire OoD object. By transforming anomaly scores into prompts for a promptable segmentation model S2M eliminates the need for threshold selection. Extensive experiments demonstrate that S2M outperforms the state-of-the-art by approximately 20% in IoU and 40% in mean F1 score on average across various benchmarks including Fishyscapes Segment-Me-If-You-Can and RoadAnomaly datasets.
Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D poses in cluttered scenes presenting significant challenges for model generalizability. Fortunately the recent Segment Anything Model (SAM) has showcased remarkable zero-shot transfer performance which provides a promising solution to tackle this task. Motivated by this we introduce SAM-6D a novel framework designed to realize the task through two steps including instance segmentation and pose estimation. Given the target objects SAM-6D employs two dedicated sub-networks namely Instance Segmentation Model (ISM) and Pose Estimation Model (PEM) to perform these steps on cluttered RGB-D images. ISM takes SAM as an advanced starting point to generate all possible object proposals and selectively preserves valid ones through meticulously crafted object matching scores in terms of semantics appearance and geometry. By treating pose estimation as a partial-to-partial point matching problem PEM performs a two-stage point matching process featuring a novel design of background tokens to construct dense 3D-3D correspondence ultimately yielding the pose estimates. Without bells and whistles SAM-6D outperforms the existing methods on the seven core datasets of the BOP Benchmark for both instance segmentation and pose estimation of novel objects.
Move as You Say Interact as You Can: Language-guided Human Motion Generation with Scene Affordance
PDF ↗Despite significant advancements in text-to-motion synthesis generating language-guided human motion within 3D environments poses substantial challenges. These challenges stem primarily from (i) the absence of powerful generative models capable of jointly modeling natural language 3D scenes and human motion and (ii) the generative models' intensive data requirements contrasted with the scarcity of comprehensive high-quality language-scene-motion datasets. To tackle these issues we introduce a novel two-stage framework that employs scene affordance as an intermediate representation effectively linking 3D scene grounding and conditional motion generation. Our framework comprises an Affordance Diffusion Model (ADM) for predicting explicit affordance map and an Affordance-to-Motion Diffusion Model (AMDM) for generating plausible human motions. By leveraging scene affordance maps our method overcomes the difficulty in generating human motion under multimodal condition signals especially when training with limited data lacking extensive language-scene-motion pairs. Our extensive experiments demonstrate that our approach consistently outperforms all baselines on established benchmarks including HumanML3D and HUMANISE. Additionally we validate our model's exceptional generalization capabilities on a specially curated evaluation set featuring previously unseen descriptions and scenes.
Quantized neural networks employ reduced precision representations for both weights and activations. This quantization process significantly reduces the memory requirements and computational complexity of the network. Binary Neural Networks (BNNs) are the extreme quantization case representing values with just one bit. Since the sign function is typically used to map real values to binary values smooth approximations are introduced to mimic the gradients during error backpropagation. Thus the mismatch between the forward and backward models corrupts the direction of the gradient causing training inconsistency problems and performance degradation. In contrast to current BNN approaches we propose to employ a binary periodic (BiPer) function during binarization. Specifically we use a square wave for the forward pass to obtain the binary values and employ the trigonometric sine function with the same period of the square wave as a differentiable surrogate during the backward pass. We demonstrate that this approach can control the quantization error by using the frequency of the periodic function and improves network performance. Extensive experiments validate the effectiveness of BiPer in benchmark datasets and network architectures with improvements of up to 1% and 0.69% with respect to state-of-the-art methods in the classification task over CIFAR-10 and ImageNet respectively. Our code is publicly available at https://github.com/edmav4/BiPer.
Recent years have witnessed rapid progress in monocular human mesh recovery. Despite their impressive performance on public benchmarks existing methods are vulnerable to unusual poses which prevents them from deploying to challenging scenarios such as dance and martial arts. This issue is mainly attributed to the domain gap induced by the data scarcity in relevant cases. Most existing datasets are captured in constrained scenarios and lack samples of such complex movements. For this reason we propose a data collection pipeline comprising automatic crawling precise annotation and hardcase mining. Based on this pipeline we establish a large dataset in a short time. The dataset named HardMo contains 7M images along with precise annotations covering 15 categories of dance and 14 categories of martial arts. Empirically we find that the prediction failure in dance and martial arts is mainly characterized by the misalignment of hand-wrist and foot-ankle. To dig deeper into the two hardcases we leverage the proposed automatic pipeline to filter collected data and construct two subsets named HardMo-Hand and HardMo-Foot. Extensive experiments demonstrate the effectiveness of the annotation pipeline and the data-driven solution to failure cases. Specifically after being trained on HardMo HMR an early pioneering method can even outperform the current state of the art 4DHumans on our benchmarks.
Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multimodal understanding and generation tasks. However the hallucinations inherent in machine-generated data which could lead to hallucinatory outputs in MLLMs remain under-explored. This work aims to investigate various hallucinations (i.e. object relation attribute hallucinations) and mitigate those hallucinatory toxicities in large-scale machine-generated visual instruction datasets. Drawing on the human ability to identify factual errors we present a novel hallucination detection and elimination framework HalluciDoctor based on the cross-checking paradigm. We use our framework to identify and eliminate hallucinations in the training data automatically. Interestingly HalluciDoctor also indicates that spurious correlations arising from long-tail object co-occurrences contribute to hallucinations. Based on that we execute counterfactual visual instruction expansion to balance data distribution thereby enhancing MLLMs' resistance to hallucinations. Comprehensive experiments on hallucination evaluation benchmarks show that our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA. The data and code for this paper are publicly available.
Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Nonetheless numerous limitations exist within existing public MSMO datasets including insufficient maintenance data inaccessibility limited size and the absence of proper categorization which pose significant challenges. To address these challenges and provide a comprehensive dataset for this new direction we have meticulously curated the MMSum dataset. Our new dataset features (1) Human-validated summaries for both video and textual content providing superior human instruction and labels for multimodal learning. (2) Comprehensively and meticulously arranged categorization spanning 17 principal categories and 170 subcategories to encapsulate a diverse array of real-world scenarios. (3) Benchmark tests performed on the proposed dataset to assess various tasks and methods including video summarization text summarization and multimodal summarization. To champion accessibility and collaboration we released the MMSum dataset and the data collection tool as fully open-source resources fostering transparency and accelerating future developments.
Various applications require high-fidelity and artifact-free 3D human reconstructions. However current implicit function-based methods inevitably produce artifacts while existing deformation methods are difficult to reconstruct high-fidelity humans wearing loose clothing. In this paper we propose a two-stage deformation method named Vertex Shift (VS) for reconstructing clothed 3D humans from single images. Specifically VS first stretches the estimated SMPL-X mesh into a coarse 3D human model using shift fields inferred from normal maps then refines the coarse 3D human model into a detailed 3D human model via a graph convolutional network embedded with implicit-function-learned features. This "stretch-refine" strategy addresses large deformations required for reconstructing loose clothing and delicate deformations for recovering intricate and detailed surfaces achieving high-fidelity reconstructions that faithfully convey the pose clothing and surface details from the input images. The graph convolutional network's ability to exploit neighborhood vertices coupled with the advantages inherited from the deformation methods ensure VS rarely produces artifacts like distortions and non-human shapes and never produces artifacts like holes broken parts and dismembered limbs. As a result VS can reconstruct high-fidelity and artifact-less clothed 3D humans from single images even under scenarios of challenging poses and loose clothing. Experimental results on three benchmarks and two in-the-wild datasets demonstrate that VS significantly outperforms current state-of-the-art methods. The code and models of VS are available for research purposes at https://github.com/starVisionTeam/VS.
Automatic anomaly detection based on visual cues holds practical significance in various domains such as manufacturing and product quality assessment. This paper introduces a new conditional anomaly detection problem which involves identifying anomalies in a query image by comparing it to a reference shape. To address this challenge we have created a large dataset BrokenChairs-180K consisting of around 180K images with diverse anomalies geometries and textures paired with 8143 reference 3D shapes. To tackle this task we have proposed a novel transformer-based approach that explicitly learns the correspondence between the query image and reference 3D shape via feature alignment and leverages a customized attention mechanism for anomaly detection. Our approach has been rigorously evaluated through comprehensive experiments serving as a benchmark for future research in this domain.
EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World
PDF ↗Being able to map the activities of others into one's own point of view is one fundamental human skill even from a very early age. Taking a step toward understanding this human ability we introduce EgoExoLearn a large-scale dataset that emulates the human demonstration following process in which individuals record egocentric videos as they execute tasks guided by demonstration videos. Focusing on the potential applications of daily assistance and professional support EgoExoLearn contains egocentric and demonstration video data spanning 120 hours captured in daily life scenarios and specialized laboratories. Along with the videos we record high-quality gaze data and provide detailed multimodal annotations formulating a playground for modeling the human ability to bridge asynchronous procedural actions from different viewpoints. To this end we present benchmarks such as cross-view association cross-view action planning and cross-view referenced skill assessment along with detailed analysis. We expect EgoExoLearn can serve as an important resource for bridging the actions across views thus paving the way for creating AI agents capable of seamlessly learning by observing humans in the real world. The dataset and benchmark codes are available at https://github.com/OpenGVLab/EgoExoLearn.
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene Understanding
PDF ↗Vision-language generative AI has demonstrated remarkable promise for empowering cross-modal scene understanding of autonomous driving and high-definition (HD) map systems. However current benchmark datasets lack multi-modal point cloud image and language data pairs. Recent approaches utilize visual instruction learning and cross-modal prompt engineering to expand vision-language models into this domain. In this paper we propose a new vision-language benchmark that can be used to finetune traffic and HD map domain-specific foundation models. Specifically we annotate and leverage large-scale broad-coverage traffic and map data extracted from huge HD map annotations and use CLIP and LLaMA-2 / Vicuna to finetune a baseline model with instruction-following data. Our experimental results across various algorithms reveal that while visual instruction-tuning large language models (LLMs) can effectively learn meaningful representations from MAPLM-QA there remains significant room for further advancements. To facilitate applying LLMs and multi-modal data into self-driving research we will release our visual-language QA data and the baseline models at GitHub.com/LLVM-AD/MAPLM.
Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory
PDF ↗Automated driving fundamentally requires knowledge about the surrounding geometry of the scene. Modern approaches use only captured images to predict occupancy maps that represent the geometry. Training these approaches requires accurate data that may be acquired with the help of LiDAR scanners. We show that the techniques used for current benchmarks and training datasets to convert LiDAR scans into occupancy grid maps yield very low quality and subsequently present a novel approach using evidence theory that yields more accurate reconstructions. We demonstrate that these are superior by a large margin both qualitatively and quantitatively and that we additionally obtain meaningful uncertainty estimates. When converting the occupancy maps back to depth estimates and comparing them with the raw LiDAR measurements our method yields a MAE improvement of 30% to 52% on nuScenes and 53% on Waymo over other occupancy ground-truth data. Finally we use the improved occupancy maps to train a state-of-the-art occupancy prediction method and demonstrate that it improves the MAE by 25% on nuScenes.