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
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Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation
PDF ↗Open-vocabulary semantic segmentation aims at segmenting arbitrary categories expressed in textual form. Previous works have trained over large amounts of image-caption pairs to enforce pixel-level multimodal alignments. However captions provide global information about the semantics of a given image but lack direct localization of individual concepts. Further training on large-scale datasets inevitably brings significant computational costs. In this paper we propose FreeDA a training-free diffusion-augmented method for open-vocabulary semantic segmentation which leverages the ability of diffusion models to visually localize generated concepts and local-global similarities to match class-agnostic regions with semantic classes. Our approach involves an offline stage in which textual-visual reference embeddings are collected starting from a large set of captions and leveraging visual and semantic contexts. At test time these are queried to support the visual matching process which is carried out by jointly considering class-agnostic regions and global semantic similarities. Extensive analyses demonstrate that FreeDA achieves state-of-the-art performance on five datasets surpassing previous methods by more than 7.0 average points in terms of mIoU and without requiring any training. Our source code is available at https://aimagelab.github.io/freeda/.
Existing learning-based solutions to medical image segmentation have two important shortcomings. First for most new segmentation tasks a new model has to be trained or fine-tuned. This requires extensive resources and machine-learning expertise and is therefore often infeasible for medical researchers and clinicians. Second most existing segmentation methods produce a single deterministic segmentation mask for a given image. In practice however there is often considerable uncertainty about what constitutes the correct segmentation and different expert annotators will often segment the same image differently. We tackle both of these problems with Tyche a framework that uses a context set to generate stochastic predictions for previously unseen tasks without the need to retrain. Tyche differs from other in-context segmentation methods in two important ways. (1) We introduce a novel convolution block architecture that enables interactions among predictions. (2) We introduce in-context test-time augmentation a new mechanism to provide prediction stochasticity. When combined with appropriate model design and loss functions Tyche can predict a set of plausible diverse segmentation candidates for new or unseen medical images and segmentation tasks without the need to retrain. Code available at: https://tyche.csail.mit.edu/.
Post-training quantization (PTQ) is an efficient model compression technique that quantizes a pretrained full-precision model using only a small calibration set of unlabeled samples without retraining. PTQ methods for convolutional neural networks (CNNs) provide quantization results comparable to full-precision counterparts. Directly applying them to vision transformers (ViTs) however incurs severe performance degradation mainly due to the differences in architectures between CNNs and ViTs. In particular the distribution of activations for each channel vary drastically according to input instances making PTQ methods for CNNs inappropriate for ViTs. To address this we introduce instance-aware group quantization for ViTs (IGQ-ViT). To this end we propose to split the channels of activation maps into multiple groups dynamically for each input instance such that activations within each group share similar statistical properties. We also extend our scheme to quantize softmax attentions across tokens. In addition the number of groups for each layer is adjusted to minimize the discrepancies between predictions from quantized and full-precision models under a bit-operation (BOP) constraint. We show extensive experimental results on image classification object detection and instance segmentation with various transformer architectures demonstrating the effectiveness of our approach.
This paper introduces a novel unified representation of diffusion models for image generation and segmentation. Specifically we use a colormap to represent entity-level masks addressing the challenge of varying entity numbers while aligning the representation closely with the image RGB domain. Two novel modules including the location-aware color palette and progressive dichotomy module are proposed to support our mask representation. On the one hand a location-aware palette guarantees the colors' consistency to entities' locations. On the other hand the progressive dichotomy module can efficiently decode the synthesized colormap to high-quality entity-level masks in a depth-first binary search without knowing the cluster numbers. To tackle the issue of lacking large-scale segmentation training data we employ an inpainting pipeline and then improve the flexibility of diffusion models across various tasks including inpainting image synthesis referring segmentation and entity segmentation. Comprehensive experiments validate the efficiency of our approach demonstrating comparable segmentation mask quality to state-of-the-art and adaptability to multiple tasks.
Limited training data is a long-standing problem for video emotion analysis (VEA). Existing works leverage the power of large-scale image datasets for transferring while failing to extract the temporal correlation of affective cues in the video. Inspired by psychology research and empirical theory we verify that the degree of emotion may vary in different segments of the video thus introducing the sentiment complementary and emotion intrinsic among temporal segments. We propose an MAE-style method for learning robust affective representation of videos via masking termed MART. First we extract the affective cues of the lexicon and verify the extracted one by computing its matching score with video content in terms of sentiment and emotion scores alongside the temporal dimension. Then with the verified cues we propose masked affective modeling to recover temporal emotion distribution. We present temporal affective complementary learning that pulls the complementary part and pushes the intrinsic one of masked multimodal features where the constraint is set with cross-modal attention among features to mask the video and recover the degree of emotion among segments. Extensive experiments on five benchmarks show the superiority of our method in video sentiment analysis video emotion recognition multimodal sentiment analysis and multimodal emotion recognition.
An efficient and effective decoding mechanism is crucial in medical image segmentation especially in scenarios with limited computational resources. However these decoding mechanisms usually come with high computational costs. To address this concern we introduce EMCAD a new efficient multi-scale convolutional attention decoder designed to optimize both performance and computational efficiency. EMCAD leverages a unique multi-scale depth-wise convolution block significantly enhancing feature maps through multi-scale convolutions. EMCAD also employs channel spatial and grouped (large-kernel) gated attention mechanisms which are highly effective at capturing intricate spatial relationships while focusing on salient regions. By employing group and depth-wise convolution EMCAD is very efficient and scales well (e.g. only 1.91M parameters and 0.381G FLOPs are needed when using a standard encoder). Our rigorous evaluations across 12 datasets that belong to six medical image segmentation tasks reveal that EMCAD achieves state-of-the-art (SOTA) performance with 79.4% and 80.3% reduction in #Params and #FLOPs respectively. Moreover EMCAD's adaptability to different encoders and versatility across segmentation tasks further establish EMCAD as a promising tool advancing the field towards more efficient and accurate medical image analysis. Our implementation is available at https://github.com/SLDGroup/EMCAD.
Existing audio-visual segmentation datasets typically focus on short-trimmed videos with only one pixel-map annotation for a per-second video clip. In contrast for untrimmed videos the sound duration start- and end-sounding time positions and visual deformation of audible objects vary significantly. Therefore we observed that current AVS models trained on trimmed videos might struggle to segment sounding objects in long videos. To investigate the feasibility of grounding audible objects in videos along both temporal and spatial dimensions we introduce the Long-Untrimmed Audio-Visual Segmentation dataset (LU-AVS) which includes precise frame-level annotations of sounding emission times and provides exhaustive mask annotations for all frames. Considering that pixel-level annotations are difficult to achieve in some complex scenes we also provide the bounding boxes to indicate the sounding regions. Specifically LU-AVS contains 10M mask annotations across 6.6K videos and 11M bounding box annotations across 7K videos. Compared with the existing datasets LU-AVS videos are on average 4 8 times longer with the silent duration being 3 15 times greater. Furthermore we try our best to adapt some baseline models that were originally designed for audio-visual-relevant tasks to examine the challenges of our newly curated LU-AVS. Through comprehensive evaluation we demonstrate the challenges of LU-AVS compared to the ones containing trimmed videos. Therefore LU-AVS provides an ideal yet challenging platform for evaluating audio-visual segmentation and localization on untrimmed long videos. The dataset is publicly available at: https://yenanliu.github.io/LU-AVS/.
We propose an unsupervised method for parsing large 3D scans of real-world scenes with easily-interpretable shapes. This work aims to provide a practical tool for analyzing 3D scenes in the context of aerial surveying and mapping without the need for user annotations. Our approach is based on a probabilistic reconstruction model that decomposes an input 3D point cloud into a small set of learned prototypical 3D shapes. The resulting reconstruction is visually interpretable and can be used to perform unsupervised instance and low-shot semantic segmentation of complex scenes. We demonstrate the usefulness of our model on a novel dataset of seven large aerial LiDAR scans from diverse real-world scenarios. Our approach outperforms state-of-the-art unsupervised methods in terms of decomposition accuracy while remaining visually interpretable. Our code and dataset are available at https://romainloiseau.fr/learnable-earth-parser/.
Computer-Aided Design (CAD) model reconstruction from point clouds is an important problem at the intersection of computer vision graphics and machine learning; it saves the designer significant time when iterating on in-the-wild objects. Recent advancements in this direction achieve relatively reliable semantic segmentation but still struggle to produce an adequate topology of the CAD model. In this work we analyze the current state of the art for that ill-posed task and identify shortcomings of existing methods. We propose a hybrid analytic-neural reconstruction scheme that bridges the gap between segmented point clouds and structured CAD models and can be readily combined with different segmentation backbones. Moreover to power the surface fitting stage we propose a novel implicit neural representation of freeform surfaces driving up the performance of our overall CAD reconstruction scheme. We extensively evaluate our method on the popular ABC benchmark of CAD models and set a new state-of-the-art for that dataset. Code is available at https://github.com/YujiaLiu76/point2cad.
While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets they have to rely on small datasets due to the cost associated with manually annotating dozens of objects in images. We propose UnCounTR a model that can learn this task without requiring any manual annotations. To this end we construct "Self-Collages" images with various pasted objects as training samples that provide a rich learning signal covering arbitrary object types and counts. Our method builds on existing unsupervised representations and segmentation techniques to successfully demonstrate for the first time the ability of reference-based counting without manual supervision. Our experiments show that our method not only outperforms simple baselines and generic models such as FasterRCNN and DETR but also matches the performance of supervised counting models in some domains.
As recent advances in mobile camera technology have enabled the capability to capture high-resolution images such as 4K images the demand for an efficient deblurring model handling large motion has increased. In this paper we discover that the image residual errors i.e. blur-sharp pixel differences can be grouped into some categories according to their motion blur type and how complex their neighboring pixels are. Inspired by this we decompose the deblurring (regression) task into blur pixel discretization (pixel-level blur classification) and discrete-to-continuous conversion (regression with blur class map) tasks. Specifically we generate the discretized image residual errors by identifying the blur pixels and then transform them to a continuous form which is computationally more efficient than naively solving the original regression problem with continuous values. Here we found that the discretization result i.e. blur segmentation map remarkably exhibits visual similarity with the image residual errors. As a result our efficient model shows comparable performance to state-of-the-art methods in realistic benchmarks while our method is up to 10 times computationally more efficient.
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.
Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention
PDF ↗Generalizability in deep neural networks plays a pivotal role in medical image segmentation. However deep learning-based medical image analyses tend to overlook the importance of frequency variance which is critical element for achieving a model that is both modality-agnostic and domain-generalizable. Additionally various models fail to account for the potential information loss that can arise from multi-task learning under deep supervision a factor that can impair the model's representation ability. To address these challenges we propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation which comprises two key components: a Multi-Frequency in Multi-Scale Attention (MFMSA) block and Ensemble Sub-Decoding Module (E-SDM). The MFMSA block refines the process of spatial feature extraction particularly in capturing boundary features by incorporating multi-frequency and multi-scale features thereby offering informative cues for tissue outline and anatomical structures. Moreover we propose E-SDM to mitigate information loss in multi-task learning with deep supervision especially during substantial upsampling from low resolution. We evaluate the segmentation performance of MADGNet across six modalities and fifteen datasets. Through extensive experiments we demonstrate that MADGNet consistently outperforms state-of-the-art models across various modalities showcasing superior segmentation performance. This affirms MADGNet as a robust solution for medical image segmentation that excels in diverse imaging scenarios. Our MADGNet code is available in GitHub Link.
Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However current MLLMs primarily focus on image-level or box-level understanding falling short in achieving fine-grained vision-language alignment at pixel level. Besides the lack of mask-based instruction data limits their advancements. In this paper we propose Osprey a mask-text instruction tuning approach to extend MLLMs by incorporating fine-grained mask regions into language instruction aiming at achieving pixel-wise visual understanding. To achieve this goal we first meticulously curate a mask-based region-text dataset with 724K samples and then design a vision-language model by injecting pixel-level representation into LLM. Specifically Osprey adopts a convolutional CLIP backbone as the vision encoder and employs a mask-aware visual extractor to extract precise visual mask features from high resolution input. Experimental results demonstrate Osprey's superiority in various region understanding tasks showcasing its new capability for pixel-level instruction tuning. In particular Osprey can be integrated with Segment Anything Model (SAM) seamlessly to obtain multi-granularity semantics. The source code dataset and demo can be found at https://github.com/CircleRadon/Osprey.
Multimodal Large Language Model (MLLMs) leverages Large Language Models as a cognitive framework for diverse visual-language tasks. Recent efforts have been made to equip MLLMs with visual perceiving and grounding capabilities. However there still remains a gap in providing fine-grained pixel-level perceptions and extending interactions beyond text-specific inputs. In this work we propose \bf AnyRef a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references such as texts boxes images or audio. This innovation empowers users with greater flexibility to engage with the model beyond textual and regional prompts without modality-specific designs. Through our proposed refocusing mechanism the generated grounding output is guided to better focus on the referenced object implicitly incorporating additional pixel-level supervision. This simple modification utilizes attention scores generated during the inference of LLM eliminating the need for extra computations while exhibiting performance enhancements in both grounding masks and referring expressions. With only publicly available training data our model achieves state-of-the-art results across multiple benchmarks including diverse modality referring segmentation and region-level referring expression generation. Code and models are available at https://github.com/jwh97nn/AnyRef
While Transformers have rapidly gained popularity in various computer vision applications post-hoc explanations of their internal mechanisms remain largely unexplored. Vision Transformers extract visual information by representing image regions as transformed tokens and integrating them via attention weights. However existing post-hoc explanation methods merely consider these attention weights neglecting crucial information from the transformed tokens which fails to accurately illustrate the rationales behind the models' predictions. To incorporate the influence of token transformation into interpretation we propose TokenTM a novel post-hoc explanation method that utilizes our introduced measurement of token transformation effects. Specifically we quantify token transformation effects by measuring changes in token lengths and correlations in their directions pre- and post-transformation. Moreover we develop initialization and aggregation rules to integrate both attention weights and token transformation effects across all layers capturing holistic token contributions throughout the model. Experimental results on segmentation and perturbation tests demonstrate the superiority of our proposed TokenTM compared to state-of-the-art Vision Transformer explanation methods.
We introduce MAGICK a large-scale dataset of generated objects with high-quality alpha mattes. While image generation methods have produced segmentations they cannot generate alpha mattes with accurate details in hair fur and transparencies. This is likely due to the small size of current alpha matting datasets and the difficulty in obtaining ground-truth alpha. We propose a scalable method for synthesizing images of objects with high-quality alpha that can be used as a ground-truth dataset. A key idea is to generate objects on a single-colored background so chroma keying approaches can be used to extract the alpha. However this faces several challenges including that current text-to-image generation methods cannot create images that can be easily chroma keyed and that chroma keying is an underconstrained problem that generally requires manual intervention for high-quality results. We address this using a combination of generation and alpha extraction methods. Using our method we generate a dataset of 150000 objects with alpha. We show the utility of our dataset by training an alpha-to-rgb generation method that outperforms baselines. Please see our project website at https://ryanndagreat.github.io/MAGICK/.
Interactive Segmentation (IS) segments specific objects or parts in the image according to user input. Current IS pipelines fall into two categories: single-granularity output and multi-granularity output. The latter aims to alleviate the spatial ambiguity present in the former. However the multi-granularity output pipeline suffers from limited interaction flexibility and produces redundant results. In this work we introduce Granularity-Controllable Interactive Segmentation (GraCo) a novel approach that allows precise control of prediction granularity by introducing additional parameters to input. This enhances the customization of the interactive system and eliminates redundancy while resolving ambiguity. Nevertheless the exorbitant cost of annotating multi-granularity masks and the lack of available datasets with granularity annotations make it difficult for models to acquire the necessary guidance to control output granularity. To address this problem we design an any-granularity mask generator that exploits the semantic property of the pre-trained IS model to automatically generate abundant mask-granularity pairs without requiring additional manual annotation. Based on these pairs we propose a granularity-controllable learning strategy that efficiently imparts the granularity controllability to the IS model. Extensive experiments on intricate scenarios at object and part levels demonstrate that our GraCo has significant advantages over previous methods. This highlights the potential of GraCo to be a flexible annotation tool capable of adapting to diverse segmentation scenarios. The project page: https://zhao-yian.github.io/GraCo.
Vision Transformer (ViT) has gained increasing attention in the computer vision community in recent years. However the core component of ViT Self-Attention lacks explicit spatial priors and bears a quadratic computational complexity thereby constraining the applicability of ViT. To alleviate these issues we draw inspiration from the recent Retentive Network (RetNet) in the field of NLP and propose RMT a strong vision backbone with explicit spatial prior for general purposes. Specifically we extend the RetNet's temporal decay mechanism to the spatial domain and propose a spatial decay matrix based on the Manhattan distance to introduce the explicit spatial prior to Self-Attention. Additionally an attention decomposition form that adeptly adapts to explicit spatial prior is proposed aiming to reduce the computational burden of modeling global information without disrupting the spatial decay matrix. Based on the spatial decay matrix and the attention decomposition form we can flexibly integrate explicit spatial prior into the vision backbone with linear complexity. Extensive experiments demonstrate that RMT exhibits exceptional performance across various vision tasks. Specifically without extra training data RMT achieves 84.8% and 86.1% top-1 acc on ImageNet-1k with 27M/4.5GFLOPs and 96M/18.2GFLOPs. For downstream tasks RMT achieves 54.5 box AP and 47.2 mask AP on the COCO detection task and 52.8 mIoU on the ADE20K semantic segmentation task.