CVPR 2024 Papers

Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any kind of biases. However existing works focus on detecting closed sets of biases defined a priori limiting the studies to well-known concepts. In this paper we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias a new pipeline that identifies and quantifies the severity of biases agnostically without access to any precompiled set. OpenBias has three stages. In the first phase we leverage a Large Language Model (LLM) to propose biases given a set of captions. Secondly the target generative model produces images using the same set of captions. Lastly a Vision Question Answering model recognizes the presence and extent of the previously proposed biases. We study the behavior of Stable Diffusion 1.5 2 and XL emphasizing new biases never investigated before. Via quantitative experiments we demonstrate that OpenBias agrees with current closed-set bias detection methods and human judgement.

Current approaches for 3D scene graph prediction rely on labeled datasets to train models for a fixed set of known object classes and relationship categories. We present Open3DSG an alternative approach to learn 3D scene graph prediction in an open world without requiring labeled scene graph data. We co-embed the features from a 3D scene graph prediction backbone with the feature space of powerful open world 2D vision language foundation models. This enables us to predict 3D scene graphs from 3D point clouds in a zero-shot manner by querying object classes from an open vocabulary and predicting the inter-object relationships from a grounded LLM with scene graph features and queried object classes as context. Open3DSG is the first 3D point cloud method to predict not only explicit open-vocabulary object classes but also open-set relationships that are not limited to a predefined label set making it possible to express rare as well as specific objects and relationships in the predicted 3D scene graph. Our experiments show that Open3DSG is effective at predicting arbitrary object classes as well as their complex inter-object relationships describing spatial supportive semantic and comparative relationships.

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.

In this paper we develop MP-HOI a powerful Multi-modal Prompt-based HOI detector designed to leverage both textual descriptions for open-set generalization and visual exemplars for handling high ambiguity in descriptions realizing HOI detection in the open world. Specifically it integrates visual prompts into existing language-guided-only HOI detectors to handle situations where textual descriptions face difficulties in generalization and to address complex scenarios with high interaction ambiguity. To facilitate MP-HOI training we build a large-scale HOI dataset named Magic-HOI which gathers six existing datasets into a unified label space forming over 186K images with 2.4K objects 1.2K actions and 20K HOI interactions. Furthermore to tackle the long-tail issue within the Magic-HOI dataset we introduce an automated pipeline for generating realistically annotated HOI images and present SynHOI a high-quality synthetic HOI dataset containing 100K images. Leveraging these two datasets MP-HOI optimizes the HOI task as a similarity learning process between multi-modal prompts and objects/interactions via a unified contrastive loss to learn generalizable and transferable objects/interactions representations from large-scale data. MP-HOI could serve as a generalist HOI detector surpassing the HOI vocabulary of existing expert models by more than 30 times. Concurrently our results demonstrate that MP-HOI exhibits remarkable zero-shot capability in real-world scenarios and consistently achieves a new state-of-the-art performance across various benchmarks. Our project homepage is available at https://MP-HOI.github.io/.

Current video anomaly detection (VAD) approaches with weak supervisions are inherently limited to a closed-set setting and may struggle in open-world applications where there can be anomaly categories in the test data unseen during training. A few recent studies attempt to tackle a more realistic setting open-set VAD which aims to detect unseen anomalies given seen anomalies and normal videos. However such a setting focuses on predicting frame anomaly scores having no ability to recognize the specific categories of anomalies despite the fact that this ability is essential for building more informed video surveillance systems. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD) in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies. To this end we propose a model that decouples OVVAD into two mutually complementary tasks - class-agnostic detection and class-specific classification - and jointly optimizes both tasks. Particularly we devise a semantic knowledge injection module to introduce semantic knowledge from large language models for the detection task and design a novel anomaly synthesis module to generate pseudo unseen anomaly videos with the help of large vision generation models for the classification task. These semantic knowledge and synthesis anomalies substantially extend our model's capability in detecting and categorizing a variety of seen and unseen anomalies. Extensive experiments on three widely-used benchmarks demonstrate our model achieves state-of-the-art performance on OVVAD task.

Open-vocabulary semantic segmentation is a challenging task which requires the model to output semantic masks of an image beyond a close-set vocabulary. Although many efforts have been made to utilize powerful CLIP models to accomplish this task they are still easily overfitting to training classes due to the natural gaps in semantic information between training and new classes. To overcome this challenge we propose a novel framework for open-vocabulary semantic segmentation called EBSeg incorporating an Adaptively Balanced Decoder (AdaB Decoder) and a Semantic Structure Consistency loss (SSC Loss). The AdaB Decoder is designed to generate different image embeddings for both training and new classes. Subsequently these two types of embeddings are adaptively balanced to fully exploit their ability to recognize training classes and generalization ability for new classes. To learn a consistent semantic structure from CLIP the SSC Loss aligns the inter-classes affinity in the image feature space with that in the text feature space of CLIP thereby improving the generalization ability of our model. Furthermore we employ a frozen SAM image encoder to complement the spatial information that CLIP features lack due to the low training image resolution and image-level supervision inherent in CLIP. Extensive experiments conducted across various benchmarks demonstrate that the proposed EBSeg outperforms the state-of-the-art methods. Our code and trained models will be here: https://github.com/slonetime/EBSeg.

This paper studies open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with generalized contextual prior of CLIP. As the core of open-vocabulary understanding alignment of visual content with the semantics of unbounded text has become the bottleneck of this field. To address this challenge recent works propose to utilize CLIP as an additional classifier and aggregate model predictions with CLIP classification results. Despite their remarkable progress performance of OVS methods in relevant scenarios is still unsatisfactory compared with supervised counterparts. We attribute this to the in-vocabulary embedding and domain-biased CLIP prediction. To this end we present a Semantic-assisted CAlibration Network (SCAN). In SCAN we incorporate generalized semantic prior of CLIP into proposal embedding to avoid collapsing on known categories. Besides a contextual shift strategy is applied to mitigate the lack of global context and unnatural background noise. With above designs SCAN achieves state-of-the-art performance on all popular open-vocabulary segmentation benchmarks. Furthermore we also focus on the problem of existing evaluation system that ignores semantic duplication across categories and propose a new metric called Semantic-Guided IoU (SG-IoU).

We introduce the new setting of open-vocabulary object 6D pose estimation in which a textual prompt is used to specify the object of interest. In contrast to existing approaches in our setting (i) the object of interest is specified solely through the textual prompt (ii) no object model (e.g. CAD or video sequence) is required at inference and (iii) the object is imaged from two RGBD viewpoints of different scenes. To operate in this setting we introduce a novel approach that leverages a Vision-Language Model to segment the object of interest from the scenes and to estimate its relative 6D pose. The key of our approach is a carefully devised strategy to fuse object-level information provided by the prompt with local image features resulting in a feature space that can generalize to novel concepts. We validate our approach on a new benchmark based on two popular datasets REAL275 and Toyota-Light which collectively encompass 34 object instances appearing in four thousand image pairs. The results demonstrate that our approach outperforms both a well-established hand-crafted method and a recent deep learning-based baseline in estimating the relative 6D pose of objects in different scenes. Code and dataset are available at https://jcorsetti.github.io/oryon.

Diffusion models represent a new paradigm in text-to-image generation. Beyond generating high-quality images from text prompts models such as Stable Diffusion have been successfully extended to the joint generation of semantic segmentation pseudo-masks. However current extensions primarily rely on extracting attentions linked to prompt words used for image synthesis. This approach limits the generation of segmentation masks derived from word tokens not contained in the text prompt. In this work we introduce Open-Vocabulary Attention Maps (OVAM)--a training-free method for text-to-image diffusion models that enables the generation of attention maps for any word. In addition we propose a lightweight optimization process based on OVAM for finding tokens that generate accurate attention maps for an object class with a single annotation. We evaluate these tokens within existing state-of-the-art Stable Diffusion extensions. The best-performing model improves its mIoU from 52.1 to 86.6 for the synthetic images' pseudo-masks demonstrating that our optimized tokens are an efficient way to improve the performance of existing methods without architectural changes or retraining.

In dynamic 3D environments the ability to recognize a diverse range of objects without the constraints of predefined categories is indispensable for real-world applications. In response to this need we introduce OV3D an innovative framework designed for open-vocabulary 3D semantic segmentation. OV3D leverages the broad open-world knowledge embedded in vision and language foundation models to establish a fine-grained correspondence between 3D points and textual entity descriptions. These entity descriptions are enriched with contextual information enabling a more open and comprehensive understanding. By seamlessly aligning 3D point features with entity text features OV3D empowers open-vocabulary recognition in the 3D domain achieving state-of-the-art open-vocabulary semantic segmentation performance across multiple datasets including ScanNet Matterport3D and nuScenes.

We study the underexplored but fundamental vision problem of machine understanding of abstract freehand scene sketches. We introduce a sketch encoder that results in semantically-aware feature space which we evaluate by testing its performance on a semantic sketch segmentation task. To train our model we rely only on the availability of bitmap sketches with their brief captions and do not require any pixel-level annotations. To obtain generalization to a large set of sketches and categories we build on a vision transformer encoder pretrained with the CLIP model. We freeze the text encoder and perform visual-prompt tuning of the visual encoder branch while introducing a set of critical modifications. Firstly we augment the classical key-query (k-q) self-attention blocks with value-value (v-v) self-attention blocks. Central to our model is a two-level hierarchical network design that enables efficient semantic disentanglement: The first level ensures holistic scene sketch encoding and the second level focuses on individual categories. We then in the second level of the hierarchy introduce a cross-attention between textual and visual branches. Our method outperforms zero-shot CLIP pixel accuracy of segmentation results by 37 points reaching an accuracy of 85.5% on the FS-COCO sketch dataset. Finally we conduct a user study that allows us to identify further improvements needed over our method to reconcile machine and human understanding of scene sketches.

The challenge of open-vocabulary recognition lies in the model has no clue of new categories it is applied to. Existing works have proposed different methods to embed category cues into the model e.g. through few-shot fine-tuning providing category names or textual descriptions to Vision-Language Models. Fine-tuning is time-consuming and degrades the generalization capability. Textual descriptions could be ambiguous and fail to depict visual details. This paper tackles open-vocabulary recognition from a different perspective by referring to multi-modal clues composed of textual descriptions and exemplar images. Our method named OVMR adopts two innovative components to pursue a more robust category cues embedding. A multi-modal classifier is first generated by dynamically complementing textual descriptions with image exemplars. A preference-based refinement module is hence applied to fuse uni-modal and multi-modal classifiers with the aim to alleviate issues of low-quality exemplar images or textual descriptions. The proposed OVMR is a plug-and-play module and works well with exemplar images randomly crawled from the Internet. Extensive experiments have demonstrated the promising performance of OVMR e.g. it outperforms existing methods across various scenarios and setups. Codes are publicly available at \href https://github.com/Zehong-Ma/OVMR https://github.com/Zehong-Ma/OVMR .

In the realm of food computing segmenting ingredients from images poses substantial challenges due to the large intra-class variance among the same ingredients the emergence of new ingredients and the high annotation costs associated with large food segmentation datasets. Existing approaches primarily utilize a closed-vocabulary and static text embeddings setting. These methods often fall short in effectively handling the ingredients particularly new and diverse ones. In response to these limitations we introduce OVFoodSeg a framework that adopts an open-vocabulary setting and enhances text embeddings with visual context. By integrating vision-language models (VLMs) our approach enriches text embedding with image-specific information through two innovative modules e.g. an image-to-text learner FoodLearner and an Image-Informed Text Encoder. The training process of OVFoodSeg is divided into two stages: the pre-training of FoodLearner and the subsequent learning phase for segmentation. The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task. By addressing the deficiencies of previous models OVFoodSeg demonstrates a significant improvement achieving an 4.9% increase in mean Intersection over Union (mIoU) on the FoodSeg103 dataset setting a new milestone for food image segmentation.

Recent advances in Iterative Vision-and-Language Navigation(IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task it poses more challenges on how to utilize the highly unstructured navigation memory with extremely sparse supervision. Towards this end we propose OVER-NAV which aims to go over and beyond the current arts of IVLN techniques. In particular we propose to incorporate LLMs and open-vocabulary detectors to distill key information and establish correspondence between multi-modal signals. Such a mechanism introduces reliable cross-modal supervision and enables on-the-fly generalization to unseen scenes without the need of extra annotation and re-training. To fully exploit the interpreted navigation data we further introduce a structured representation coded Omnigraph to effectively integrate multi-modal information along the tour. Accompanied with a novel omnigraph fusion mechanism OVER-NAV is able to extract the most relevant knowledge from omnigraph for a more accurate navigating action. In addition OVER-NAV seamlessly supports both discrete and continuous environments under a unified framework. We demonstrate the superiority of OVER-NAV in extensive experiments.

We have recently seen tremendous progress in realistic text-to-motion generation. Yet the existing methods often fail or produce implausible motions with unseen text inputs which limits the applications. In this paper we present OMG a novel framework which enables compelling motion generation from zero-shot open-vocabulary text prompts. Our key idea is to carefully tailor the pretrain-then-finetune paradigm into the text-to-motion generation. At the pre-training stage our model improves the generation ability by learning the rich out-of-domain inherent motion traits. To this end we scale up a large unconditional diffusion model up to 1B parameters so as to utilize the massive unlabeled motion data up to over 20M motion instances. At the subsequent fine-tuning stage we introduce motion ControlNet which incorporates text prompts as conditioning information through a trainable copy of the pre-trained model and the proposed novel Mixture-of-Controllers (MoC) block. MoC block adaptively recognizes various ranges of the sub-motions with a cross-attention mechanism and processes them separately with the text-token-specific experts. Such a design effectively aligns the CLIP token embeddings of text prompts to various ranges of compact and expressive motion features. Extensive experiments demonstrate that our OMG achieves significant improvements over the state-of-the-art methods on zero-shot text-to-motion generation. Project page: https://tr3e.github.io/omg-page.

Edge segmentation is well-known to be subjective due to personalized annotation styles and preferred granularity. However most existing deterministic edge detection methods produce only a single edge map for one input image. We argue that generating multiple edge maps is more reasonable than generating a single one considering the subjectivity and ambiguity of the edges. Thus motivated in this paper we propose multiple granularity edge detection called MuGE which can produce a wide range of edge maps from approximate object contours to fine texture edges. Specifically we first propose to design an edge granularity network to estimate the edge granularity from an individual edge annotation. Subsequently to guide the generation of diversified edge maps we integrate such edge granularity into the multi-scale feature maps in the spatial domain. Meanwhile we decompose the feature maps into low-frequency and high-frequency parts where the encoded edge granularity is further fused into the high-frequency part to achieve more precise control over the details of the produced edge maps. Compared to previous methods MuGE is able to not only generate multiple edge maps at different controllable granularities but also achieve a competitive performance on the BSDS500 and Multicue benchmark datasets.

Monocular 3D object detection has attracted widespread attention due to its potential to accurately obtain object 3D localization from a single image at a low cost. Depth estimation is an essential but challenging subtask of monocular 3D object detection due to the ill-posedness of 2D to 3D mapping. Many methods explore multiple local depth clues such as object heights and keypoints and then formulate the object depth estimation as an ensemble of multiple depth predictions to mitigate the insufficiency of single-depth information. However the errors of existing multiple depths tend to have the same sign which hinders them from neutralizing each other and limits the overall accuracy of combined depth. To alleviate this problem we propose to increase the complementarity of depths with two novel designs. First we add a new depth prediction branch named complementary depth that utilizes global and efficient depth clues from the entire image rather than the local clues to reduce the correlation of depth predictions. Second we propose to fully exploit the geometric relations between multiple depth clues to achieve complementarity in form. Benefiting from these designs our method achieves higher complementarity. Experiments on the KITTI benchmark demonstrate that our method achieves state-of-the-art performance without introducing extra data. In addition complementary depth can also be a lightweight and plug-and-play module to boost multiple existing monocular 3d object detectors. Code is available at https://github.com/elvintanhust/MonoCD.

Open-vocabulary 3D instance segmentation is cutting-edge for its ability to segment 3D instances without predefined categories. However progress in 3D lags behind its 2D counterpart due to limited annotated 3D data. To address this recent works first generate 2D open-vocabulary masks through 2D models and then merge them into 3D instances based on metrics calculated between two neighboring frames. In contrast to these local metrics we propose a novel metric view consensus rate to enhance the utilization of multi-view observations. The key insight is that two 2D masks should be deemed part of the same 3D instance if a significant number of other 2D masks from different views contain both these two masks. Using this metric as edge weight we construct a global mask graph where each mask is a node. Through iterative clustering of masks showing high view consensus we generate a series of clusters each representing a distinct 3D instance. Notably our model is training-free. Through extensive experiments on publicly available datasets including ScanNet++ ScanNet200 and MatterPort3D we demonstrate that our method achieves state-of-the-art performance in open-vocabulary 3D instance segmentation. Our project page is at \href https://pku-epic.github.io/MaskClustering/ https://pku-epic.github.io/MaskClustering .

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories resulting in a high false positive rate. To address this issue we introduce a novel OOD detection method named 'NegPrompt' to learn a set of negative prompts each representing a negative connotation of a given class label for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only without any reliance on external outlier data. Further current methods assume the availability of samples of all ID classes rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://github.com/mala-lab/negprompt.