Visual Grounding

Visual grounding refers to the process of associating natural language expressions with corresponding regions within an image. Existing benchmarks for visual grounding primarily operate within small-scale scenes with a few objects. Nevertheless recent advances in imaging technology have enabled the acquisition of gigapixel-level images providing high-resolution details in large-scale scenes containing numerous objects. To bridge this gap between imaging and computer vision benchmarks and make grounding more practically valuable we introduce a novel dataset named GigaGrounding designed to challenge visual grounding models in gigapixel-level large-scale scenes. We extensively analyze and compare the dataset with existing benchmarks demonstrating that GigaGrounding presents unique challenges such as large-scale scene understanding gigapixel-level resolution significant variations in object scales and the "multi-hop expressions". Furthermore we introduced a simple yet effective grounding approach which employs a "glance-to-zoom-in" paradigm and exhibits enhanced capabilities for addressing the GigaGrounding task. The dataset is available at www.gigavision.ai.

3D Visual Grounding (3DVG) aims at localizing 3D object based on textual descriptions. Conventional supervised methods for 3DVG often necessitate extensive annotations and a predefined vocabulary which can be restrictive. To address this issue we propose a novel visual programming approach for zero-shot open-vocabulary 3DVG leveraging the capabilities of large language models (LLMs). Our approach begins with a unique dialog-based method engaging with LLMs to establish a foundational understanding of zero-shot 3DVG. Building on this we design a visual program that consists of three types of modules i.e. view-independent view-dependent and functional modules. Furthermore we develop an innovative language-object correlation module to extend the scope of existing 3D object detectors into open-vocabulary scenarios. Extensive experiments demonstrate that our zero-shot approach can outperform some supervised baselines marking a significant stride towards effective 3DVG. Code is available at https://curryyuan.github.io/ZSVG3D.

Referring expressions for visual objects often include descriptions of relative spatial arrangements to other objects -- e.g. "to the right of" -- that depend on the point of view of the speaker. In 2D referring expression tasks this viewpoint is captured unambiguously in the image. However grounding expressions with such spatial language in 3D without viewpoint annotations can be ambiguous. In this paper we investigate the significance of viewpoint information in 3D visual grounding -- introducing a model that explicitly predicts the speaker's viewpoint based on the referring expression and scene. We pretrain this model on a synthetically generated dataset that provides viewpoint annotations and then finetune on 3D referring expression datasets. Further we introduce an auxiliary uniform object representation loss to encourage viewpoint invariance in learned object representations. We find that our proposed ViewPoint Prediction Network (VPP-Net) achieves state-of-the-art performance on ScanRefer SR3D and NR3D -- improving Accuracy@0.25IoU by 1.06% 0.60% and 2.00% respectively compared to prior work.

In this paper we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation creating the VRP-SAM model. In essence VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target image. It is note that the VRP encoder can support a variety of annotation formats for reference images including point box scribble and mask. VRP-SAM achieves a breakthrough within the SAM framework by extending its versatility and applicability while preserving SAM's inherent strengths thus enhancing user-friendliness. To enhance the generalization ability of VRP-SAM the VRP encoder adopts a meta-learning strategy. To validate the effectiveness of VRP-SAM we conducted extensive empirical studies on the Pascal and COCO datasets. Remarkably VRP-SAM achieved state-of-the-art performance in visual reference segmentation with minimal learnable parameters. Furthermore VRP-SAM demonstrates strong generalization capabilities allowing it to perform segmentation of unseen objects and enabling cross-domain segmentation. The source code and models will be available at https://github.com/syp2ysy/VRP-SAM

When we look around and perform complex tasks how we see and selectively process what we see is crucial. However the lack of this visual search mechanism in current multimodal LLMs (MLLMs) hinders their ability to focus on important visual details especially when handling high-resolution and visually crowded images. To address this we introduce V* an LLM-guided visual search mechanism that employs the world knowledge in LLMs for efficient visual querying. When combined with an MLLM this mechanism enhances collaborative reasoning contextual understanding and precise visual grounding. This integration results in a new MLLM meta-architecture named Show sEArch and TelL (SEAL). We further create V*Bench a benchmark specifically designed to evaluate MLLMs in their ability to process high-resolution images and focus on visual details. Our study highlights the necessity of incorporating visual search capabilities into multimodal systems. The code is available at https://github.com/penghao-wu/vstar

3D visual grounding plays a crucial role in scene understanding with extensive applications in AR/VR. Despite the significant progress made in recent methods the requirement of dense textual descriptions for each individual object which is time-consuming and costly hinders their scalability. To mitigate reliance on text annotations during training researchers have explored language-free training paradigms in the 2D field via explicit text generation or implicit feature substitution. Nevertheless unlike 2D images the complexity of spatial relations in 3D coupled with the absence of robust 3D visual language pre-trained models makes it challenging to directly transfer previous strategies. To tackle the above issues in this paper we introduce a language-free training framework for 3D visual grounding. By utilizing the visual-language joint embedding in 2D large cross-modality model as a bridge we can expediently produce the pseudo-language features by leveraging the features of 2D images which are equivalent to that of real textual descriptions. We further develop a relation injection scheme with a Neighboring Relation-aware Modeling module and a Cross-modality Relation Consistency module aiming to enhance and preserve the complex relationships between the 2D and 3D embedding space. Extensive experiments demonstrate that our proposed language-free 3D visual grounding approach can obtain promising performance across three widely used datasets --ScanRefer Nr3D and Sr3D. Our codes are available at https://github.com/xibi777/3DLFVG

We present DenseAV a novel dual encoder grounding architecture that learns high-resolution semantically meaningful and audio-visual aligned features solely through watching videos. We show that DenseAV can discover the "meaning" of words and the "location" of sounds without explicit localization supervision. Furthermore it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast many other systems that learn "global" audio and video representations cannot localize words and sound. Finally we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the current state-of-the-art ImageBind on cross-modal retrieval using fewer than half of the parameters. Project Page: https://aka.ms/denseav

Learning generalizable visual representations from Internet data has yielded promising results for robotics. Yet prevailing approaches focus on pre-training 2D representations being sub-optimal to deal with occlusions and accurately localize objects in complex 3D scenes. Meanwhile 3D representation learning has been limited to single-object understanding. To address these limitations we introduce a novel 3D pre-training framework for robotics named SUGAR that captures semantic geometric and affordance properties of objects through 3D point clouds. We underscore the importance of cluttered scenes in 3D representation learning and automatically construct a multi-object dataset benefiting from cost-free supervision in simulation. SUGAR employs a versatile transformer-based model to jointly address five pre-training tasks namely cross-modal knowledge distillation for semantic learning masked point modeling to understand geometry structures grasping pose synthesis for object affordance 3D instance segmentation and referring expression grounding to analyze cluttered scenes. We evaluate our learned representation on three robotic-related tasks namely zero-shot 3D object recognition referring expression grounding and language-driven robotic manipulation. Experimental results show that SUGAR's 3D representation outperforms state-of-the-art 2D and 3D representations.

Diagnosis in histopathology requires a global whole slide images (WSIs) analysis requiring pathologists to compound evidence from different WSI patches. The gigapixel scale of WSIs poses a challenge for histopathology multi-modal models. Training multi-model models for histopathology requires instruction tuning datasets which currently contain information for individual image patches without a spatial grounding of the concepts within each patch and without a wider view of the WSI. To bridge this gap we introduce QUILT-INSTRUCT a large-scale dataset of 107131 histopathology-specific instruction question/answer pairs grounded within diagnostically relevant image patches that make up the WSI. Our dataset is collected by leveraging educational histopathology videos from YouTube which provides spatial localization of narrations by automatically extracting the narrators' cursor positions. QUILT-INSTRUCT supports contextual reasoning by extracting diagnosis and supporting facts from the entire WSI. Using QUILT-INSTRUCT we train QUILT-LLAVA which can reason beyond the given single image patch enabling diagnostic reasoning across patches. To evaluate QUILT-LLAVA we propose a comprehensive evaluation dataset created from 985 images and 1283 human-generated question-answers. We also thoroughly evaluate QUILT-LLAVA using public histopathology datasets where QUILT-LLAVA significantly outperforms SOTA by over 10% on relative GPT-4 score and 4% and 9% on open and closed set VQA.

Large language models have achieved great success in recent years so as their variants in vision. Existing vision-language models can describe images in natural languages answer visual-related questions or perform complex reasoning about the image. However it is yet unclear how localization tasks such as word grounding or referring localization can be performed using large language models. In this work we aim to develop a vision-language model that can take locations for example a set of points or boxes as either inputs or outputs. When taking locations as inputs the model performs location-conditioned captioning which generates captions for the indicated object or region. When generating locations as outputs our model regresses pixel coordinates for each output word generated by the language model and thus performs dense word grounding. Our model is pre-trained on the Localized Narrative dataset which contains pixel-word-aligned captioning from human attention. We show our model can be applied to various location-aware vision-language tasks including referring localization location-conditioned captioning and dense object captioning archiving state-of-the-art performance on RefCOCO and Visual Genome.

Determining the location of an image anywhere on Earth is a complex visual task which makes it particularly relevant for evaluating computer vision algorithms. Determining the location of an image anywhere on Earth is a complex visual task which makes it particularly relevant for evaluating computer vision algorithms. Yet the absence of standard large-scale open-access datasets with reliably localizable images has limited its potential. To address this issue we introduce OpenStreetView-5M a large-scale open-access dataset comprising over 5.1 million geo-referenced street view images covering 225 countries and territories. In contrast to existing benchmarks we enforce a strict train/test separation allowing us to evaluate the relevance of learned geographical features beyond mere memorization. To demonstrate the utility of our dataset we conduct an extensive benchmark of various state-of-the-art image encoders spatial representations and training strategies. All associated codes and models can be found at https://github.com/gastruc/osv5m.

Unsupervised visual grounding methods alleviate the issue of expensive manual annotation of image-query pairs by generating pseudo-queries. However existing methods are prone to confusing the spatial relationships between objects and rely on designing complex prompt modules to generate query texts which severely impedes the ability to generate accurate and comprehensive queries due to ambiguous spatial relationships and manually-defined fixed templates. To tackle these challenges we propose a omni-directional language query generation approach for unsupervised visual grounding named Omni-Q. Specifically we develop a 3D spatial relation module to extend the 2D spatial representation to 3D thereby utilizing 3D location information to accurately determine the spatial position among objects. Besides we introduce a spatial graph module leveraging the power of graph structures to establish accurate and diverse object relationships and thus enhancing the flexibility of query generation. Extensive experiments on five public benchmark datasets demonstrate that our method significantly outperforms existing state-of-the-art unsupervised methods by up to 16.17%. In addition when applied in the supervised setting our method can freely save up to 60% human annotations without a loss of performance.

3D visual grounding is a challenging task that often requires direct and dense supervision notably the semantic label for each object in the scene. In this paper we instead study the naturally supervised setting that learns from only 3D scene and QA pairs where prior works underperform. We propose the Language-Regularized Concept Learner (LARC) which uses constraints from language as regularization to significantly improve the accuracy of neuro-symbolic concept learners in the naturally supervised setting. Our approach is based on two core insights: the first is that language constraints (e.g. a word's relation to another) can serve as effective regularization for structured representations in neuro-symbolic models; the second is that we can query large language models to distill such constraints from language properties. We show that LARC improves performance of prior works in naturally supervised 3D visual grounding and demonstrates a wide range of 3D visual reasoning capabilities--from zero-shot composition to data efficiency and transferability. Our method represents a promising step towards regularizing structured visual reasoning frameworks with language-based priors for learning in settings without dense supervision.

Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers which however are not always grounded in the input image. We investigate this phenomenon usually referred to as "hallucination" and show that it stems from an excessive reliance on the language prior. In particular we show that as more tokens are generated the reliance on the visual prompt decreases and this behavior strongly correlates with the emergence of hallucinations. To reduce hallucinations we introduce Multi-Modal Mutual-Information Decoding (M3ID) a new sampling method for prompt amplification. M3ID amplifies the influence of the reference image over the language prior hence favoring the generation of tokens with higher mutual information with the visual prompt. M3ID can be applied to any pre-trained autoregressive VLM at inference time without necessitating further training and with minimal computational overhead. If training is an option we show that M3ID can be paired with Direct Preference Optimization (DPO) to improve the model's reliance on the prompt image without requiring any labels. Our empirical findings show that our algorithms maintain the fluency and linguistic capabilities of pre-trained VLMs while reducing hallucinations by mitigating visually ungrounded answers. Specifically for the LLaVA 13B model M3ID and M3ID+DPO reduce the percentage of hallucinated objects in captioning tasks by 25% and 28% respectively and improve the accuracy on VQA benchmarks such as POPE by 21% and 24%.

3D visual grounding involves matching natural language descriptions with their corresponding objects in 3D spaces. Existing methods often face challenges with accuracy in object recognition and struggle in interpreting complex linguistic queries particularly with descriptions that involve multiple anchors or are view-dependent. In response we present the MiKASA (Multi-Key-Anchor Scene-Aware) Transformer. Our novel end-to-end trained model integrates a self-attention-based scene-aware object encoder and an original multi-key-anchor technique enhancing object recognition accuracy and the understanding of spatial relationships. Furthermore MiKASA improves the explainability of decision-making facilitating error diagnosis. Our model achieves the highest overall accuracy in the Referit3D challenge for both the Sr3D and Nr3D datasets particularly excelling by a large margin in categories that require viewpoint-dependent descriptions.

Referring Image Segmentation (RIS) is a challenging task that requires an algorithm to segment objects referred by free-form language expressions. Despite significant progress in recent years most state-of-the-art (SOTA) methods still suffer from considerable language-image modality gap at the pixel and word level. These methods generally 1) rely on sentence-level language features for language-image alignment and 2) lack explicit training supervision for fine-grained visual grounding. Consequently they exhibit weak object-level correspondence between visual and language features. Without well-grounded features prior methods struggle to understand complex expressions that require strong reasoning over relationships among multiple objects especially when dealing with rarely used or ambiguous clauses. To tackle this challenge we introduce a novel Mask Grounding auxiliary task that significantly improves visual grounding within language features by explicitly teaching the model to learn fine-grained correspondence between masked textual tokens and their matching visual objects. Mask Grounding can be directly used on prior RIS methods and consistently bring improvements. Furthermore to holistically address the modality gap we also design a cross-modal alignment loss and an accompanying alignment module. These additions work synergistically with Mask Grounding. With all these techniques our comprehensive approach culminates in MagNet (Mask-grounded Network) an architecture that significantly outperforms prior arts on three key benchmarks (RefCOCO RefCOCO+ and G-Ref) demonstrating our method's effectiveness in addressing current limitations of RIS algorithms. Our code and pre-trained weights will be released.

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.

Vision-and-language models trained to match images with text can be combined with visual explanation methods to point to the locations of specific objects in an image. Our work shows that the localization --"grounding'"-- abilities of these models can be further improved by finetuning for self-consistent visual explanations. We propose a strategy for augmenting existing text-image datasets with paraphrases using a large language model and SelfEQ a weakly-supervised strategy on visual explanation maps for paraphrases that encourages self-consistency. Specifically for an input textual phrase we attempt to generate a paraphrase and finetune the model so that the phrase and paraphrase map to the same region in the image. We posit that this both expands the vocabulary that the model is able to handle and improves the quality of the object locations highlighted by gradient-based visual explanation methods (e.g. GradCAM). We demonstrate that SelfEQ improves performance on Flickr30k ReferIt and RefCOCO+ over a strong baseline method and several prior works. Particularly comparing to other methods that do not use any type of box annotations we obtain 84.07% on Flickr30k (an absolute improvement of 4.69%) 67.40% on ReferIt (an absolute improvement of 7.68%) and 75.10% 55.49% on RefCOCO+ test sets A and B respectively (an absolute improvement of 3.74% on average).

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.