Applications · computer vision

The advent of Vision Transformers (ViTs) marks a substantial paradigm shift in the realm of computer vision. ViTs capture the global information of images through self-attention modules which perform dot product computations among patchified image tokens. While self-attention modules empower ViTs to capture long-range dependencies the computational complexity grows quadratically with the number of tokens which is a major hindrance to the practical application of ViTs. Moreover the self-attention mechanism in deep ViTs is also susceptible to the attention saturation issue. Accordingly we argue against the necessity of computing the attention scores in every layer and we propose the Less-Attention Vision Transformer (LaViT) which computes only a few attention operations at each stage and calculates the subsequent feature alignments in other layers via attention transformations that leverage the previously calculated attention scores. This novel approach can mitigate two primary issues plaguing traditional self-attention modules: the heavy computational burden and attention saturation. Our proposed architecture offers superior efficiency and ease of implementation merely requiring matrix multiplications that are highly optimized in contemporary deep learning frameworks. Moreover our architecture demonstrates exceptional performance across various vision tasks including classification detection and segmentation.

Although Vision Transformer (ViT) has achieved significant success in computer vision it does not perform well in dense prediction tasks due to the lack of inner-patch information interaction and the limited diversity of feature scale. Most existing studies are devoted to designing vision-specific transformers to solve the above problems which introduce additional pre-training costs. Therefore we present a plain pre-training-free and feature-enhanced ViT backbone with Convolutional Multi-scale feature interaction named ViT-CoMer which facilitates bidirectional interaction between CNN and transformer. Compared to the state-of-the-art ViT-CoMer has the following advantages: (1) We inject spatial pyramid multi-receptive field convolutional features into the ViT architecture which effectively alleviates the problems of limited local information interaction and single-feature representation in ViT. (2) We propose a simple and efficient CNN-Transformer bidirectional fusion interaction module that performs multi-scale fusion across hierarchical features which is beneficial for handling dense prediction tasks. (3) We evaluate the performance of ViT-CoMer across various dense prediction tasks different frameworks and multiple advanced pre-training. Notably our ViT-CoMer-L achieves 64.3% AP on COCO val2017 without extra training data and 62.1% mIoU on ADE20K val both of which are comparable to state-of-the-art methods. We hope ViT-CoMer can serve as a new backbone for dense prediction tasks to facilitate future research. The code will be released at https://github.com/Traffic-X/ViT-CoMer.

Humans possess the remarkable skill of Visual Perception the ability to see and understand the seen helping them make sense of the visual world and in turn reason. Multimodal Large Language Models (MLLM) have recently achieved impressive performance on vision-language tasks ranging from visual question-answering and image captioning to visual reasoning and image generation. However when prompted to identify or count (perceive) the entities in a given image existing MLLM systems fail. Working towards developing an accurate MLLM system for perception and reasoning we propose using Versatile vision enCoders (VCoder) as perception eyes for Multimodal LLMs. We feed the VCoder with perception modalities such as segmentation or depth maps improving the MLLM's perception abilities. Secondly we leverage the images from COCO and outputs from off-the-shelf vision perception models to create our COCO Segmentation Text (COST) dataset for training and evaluating MLLMs on the object perception task. Thirdly we introduce metrics to assess the object perception abilities in MLLMs on our COST dataset. Lastly we provide extensive experimental evidence proving the VCoder's improved object-level perception skills over existing Multimodal LLMs including GPT-4V. We open-source our dataset code and models to promote research.

Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks (DNNs) especially Transformers for super-resolution have seen significant advancements in recent years challenges still remain particularly in limited receptive field caused by window-based self-attention. To address these issues we introduce a group of auxiliary Adaptive Token Dictionary to SR Transformer and establish an ATD-SR method. The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step. The refinement strategy could not only provide global information to all input tokens but also group image tokens into categories. Based on category partitions we further propose a category-based self-attention mechanism designed to leverage distant but similar tokens for enhancing input features. The experimental results show that our method achieves the best performance on various single image super-resolution benchmarks.

Vision-language (VL) models have achieved unprecedented success recently in which the connection module is the key to bridge the modality gap. Nevertheless the abundant visual clues are not sufficiently exploited in most existing methods. On the vision side most existing approaches only use the last feature of the vision tower without using the low-level features. On the language side most existing methods only introduce shallow vision-language interactions. In this paper we present a vision-inspired vision-language connection module dubbed as VIVL which efficiently exploits the vision cue for VL models. To take advantage of the lowerlevel information from the vision tower a feature pyramid extractor (FPE) is introduced to combine features from different intermediate layers which enriches the visual cue with negligible parameters and computation overhead. To enhance VL interactions we propose deep vision-conditioned prompts (DVCP) that allows deep interactions of vision and language features efficiently. Our VIVL exceeds the previous state-of-the-art method by 18.1 CIDEr when training from scratch on the COCO caption task which greatly improves the data efficiency. When used as a plug-in module VIVL consistently improves the performance for various backbones and VL frameworks delivering new state-of-the-art results on multiple benchmarks e.g. NoCaps and VQAv2.

Scene reconstruction from multi-view images is a fundamental problem in computer vision and graphics. Recent neural implicit surface reconstruction methods have achieved high-quality results; however editing and manipulating the 3D geometry of reconstructed scenes remains challenging due to the absence of naturally decomposed object entities and complex object/background compositions. In this paper we present Total-Decom a novel method for decomposed 3D reconstruction with minimal human interaction. Our approach seamlessly integrates the Segment Anything Model (SAM) with hybrid implicit-explicit neural surface representations and a mesh-based region-growing technique for accurate 3D object decomposition. Total-Decom requires minimal human annotations while providing users with real-time control over the granularity and quality of decomposition. We extensively evaluate our method on benchmark datasets and demonstrate its potential for downstream applications such as animation and scene editing.

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.

Vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example although CLIP achieves impressive accuracy on ImageNet (60-80%) its performance drops below 10% for more than ten concepts like night snake presumably due to their limited presence in the pretraining data. However measuring the frequency of concepts in VLMs' large-scale datasets is challenging. We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts. Our analysis confirms that popular datasets such as LAION exhibit a long-tailed concept distribution yielding biased performance in VLMs. We also find that downstream applications of VLMs including visual chatbots (e.g. GPT-4V) and text-to-image models (e.g. Stable Diffusion) often fail to recognize or generate images of rare concepts identified by our method. To mitigate the imbalanced performance of zero-shot VLMs we propose REtrieval-Augmented Learning (REAL). First instead of prompting VLMs using the original class names REAL uses their most frequent synonyms found in pretraining texts. This simple change already outperforms costly human-engineered and LLM-enriched prompts over nine benchmark datasets. Second REAL trains a linear classifier on a small yet balanced set of pretraining data retrieved using concept synonyms. REAL surpasses the previous zero-shot SOTA using 400x less storage and 10000x less training time!

Gradient-based saliency maps have been widely used to explain the decisions of deep neural network classifiers. However standard gradient-based interpretation maps including the simple gradient and integrated gradient algorithms often lack desired structures such as sparsity and connectedness in their application to real-world computer vision models. A common approach to induce sparsity-based structures into gradient-based saliency maps is to modify the simple gradient scheme using sparsification or norm-based regularization. However one drawback with such post-processing approaches is the potentially significant loss in fidelity to the original simple gradient map. In this work we propose to apply adversarial training as an in-processing scheme to train neural networks with structured simple gradient maps. We demonstrate an existing duality between the regularized norms of the adversarial perturbations and gradient-based maps whereby we design adversarial training schemes promoting sparsity and group-sparsity properties in simple gradient maps. We present comprehensive numerical results to show the influence of our proposed norm-based adversarial training methods on the standard gradient-based maps of standard neural network architectures on benchmark image datasets.

Understanding and reasoning about spatial relationships is crucial for Visual Question Answering (VQA) and robotics. Vision Language Models (VLMs) have shown impressive performance in some VQA benchmarks but struggle with 3D spatial reasoning such as recognizing distances or size differences between physical objects. This limitation may stem from a lack of 3D spatial knowledge in their training data. To address this we propose training VLMs with extensive spatial reasoning data from the internet. Our approach includes developing an automatic 3D spatial VQA data generation framework capable of creating 2 billion VQA examples from 10 million real-world images. We explore various factors in the training process such as data quality training pipeline and VLM architecture. Our work introduces the first Internet-scale 3D spatial reasoning dataset in metric space. By co-training a VLM with this dataset we significantly improve its performance in both qualitative and quantitative spatial VQA. Additionally this enhanced VLM enables new applications in chain-of-thought spatial reasoning and robotics particularly in quantitative estimation.

Refractive Index Tomography is the inverse problem of reconstructing the continuously-varying 3D refractive index in a scene using 2D projected image measurements. Although a purely refractive field is not directly visible it bends light rays as they travel through space thus providing a signal for reconstruction. The effects of such fields appear in many scientific computer vision settings ranging from refraction due to transparent cells in microscopy to the lensing of distant galaxies caused by dark matter in astrophysics. Reconstructing these fields is particularly difficult due to the complex nonlinear effects of the refractive field on observed images. Furthermore while standard 3D reconstruction and tomography settings typically have access to observations of the scene from many viewpoints many refractive index tomography problem settings only have access to images observed from a single viewpoint. We introduce a method that leverages prior knowledge of light sources scattered throughout the refractive medium to help disambiguate the single-view refractive index tomography problem. We differentiably trace curved rays through a neural field representation of the refractive field and optimize its parameters to best reproduce the observed image. We demonstrate the efficacy of our approach by reconstructing simulated refractive fields analyze the effects of light source distribution on the recovered field and test our method on a simulated dark matter mapping problem where we successfully recover the 3D refractive field caused by a realistic dark matter distribution.

Despite the recent success of sign language research the widely adopted CNN-based backbones are mainly migrated from other computer vision tasks in which the contours and texture of objects are crucial for identifying objects. They usually treat sign frames as grids and may fail to capture effective cross-region features. In fact sign language tasks need to focus on the correlation of different regions in one frame and the interaction of different regions among adjacent frames for identifying a sign sequence. In this paper we propose to represent a sign sequence as graphs and introduce a simple yet effective graph-based sign language processing architecture named SignGraph to extract cross-region features at the graph level. SignGraph consists of two basic modules: Local Sign Graph (LSG) module for learning the correlation of intra-frame cross-region features in one frame and Temporal Sign Graph (TSG) module for tracking the interaction of inter-frame cross-region features among adjacent frames. With LSG and TSG we build our model in a multiscale manner to ensure that the representation of nodes can capture cross-region features at different granularities. Extensive experiments on current public sign language datasets demonstrate the superiority of our SignGraph model. Our model achieves very competitive performances with the SOTA model while not using any extra cues. Code and models are available at: https://github.com/gswycf/SignGraph.

Given an input set of 3D point pairs the goal of outlier-robust 3D registration is to compute some rotation and translation that align as many point pairs as possible. This is an important problem in computer vision for which many highly accurate approaches have been recently proposed. Despite their impressive performance these approaches lack scalability often overflowing the 16GB of memory of a standard laptop to handle roughly 30000 point pairs. In this paper we propose a 3D registration approach that can process more than ten million (10^7) point pairs with over 99% random outliers. Moreover our method is efficient entails low memory costs and maintains high accuracy at the same time. We call our method TEAR as it involves minimizing an outlier-robust loss that computes Truncated Entry-wise Absolute Residuals. To minimize this loss we decompose the original 6-dimensional problem into two subproblems of dimensions 3 and 2 respectively solved in succession to global optimality via a customized branch-and-bound method. While branch-and-bound is often slow and unscalable this does not apply to TEAR as we propose novel bounding functions that are tight and computationally efficient. Experiments on various datasets are conducted to validate the scalability and efficiency of our method.

The problem of self-calibration of two cameras from a given fundamental matrix is one of the basic problems in geometric computer vision. Under the assumption of known principal points and square pixels the Bougnoux formula offers a means to compute the two unknown focal lengths. However in many practical situations the formula yields inaccurate results due to commonly occurring singularities. Moreover the estimates are sensitive to noise in the computed fundamental matrix and to the assumed positions of the principal points. In this paper we therefore propose an efficient and robust iterative method to estimate the focal lengths along with the principal points of the cameras given a fundamental matrix and priors for the estimated camera intrinsics. In addition we study a computationally efficient check of models generated within RANSAC that improves the accuracy of the estimated models while reducing the total computational time. Extensive experiments on real and synthetic data show that our iterative method brings significant improvements in terms of the accuracy of the estimated focal lengths over the Bougnoux formula and other state-of-the-art methods even when relying on inaccurate priors. The code for the methods and experiments is available at https://github.com/kocurvik/robust_self_calibration

Vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks (CNNs) in the realm of computer vision showcasing tremendous potential. However recent research has unveiled a susceptibility of ViTs to adversarial attacks akin to their CNN counterparts. Adversarial training and randomization are two representative effective defenses for CNNs. Some researchers have attempted to apply adversarial training to ViTs and achieved comparable robustness to CNNs while it is not easy to directly apply randomization to ViTs because of the architecture difference between CNNs and ViTs. In this paper we delve into the structural intricacies of ViTs and propose a novel defense mechanism termed Random entangled image Transformer (ReiT) which seamlessly integrates adversarial training and randomization to bolster the adversarial robustness of ViTs. Recognizing the challenge posed by the structural disparities between ViTs and CNNs we introduce a novel module input-independent random entangled self-attention (II-ReSA). This module optimizes random entangled tokens that lead to "dissimilar" self-attention outputs by leveraging model parameters and the sampled random tokens thereby synthesizing the self-attention module outputs and random entangled tokens to diminish adversarial similarity. ReiT incorporates two distinct random entangled tokens and employs dual randomization offering an effective countermeasure against adversarial examples while ensuring comprehensive deduction guarantees. Through extensive experiments conducted on various ViT variants and benchmarks we substantiate the superiority of our proposed method in enhancing the adversarial robustness of Vision Transformers.

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.

Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently prompt learning approaches have been explored to efficiently and effectively adapt the vision-language models to a variety of downstream tasks. However most existing prompt learning methods suffer from task overfitting since the general knowledge of the pre-trained vision language models is forgotten while the prompts are finetuned on a small data set from a specific target task. To address this issue we propose a Prompt Meta-Regularization (ProMetaR) to improve the generalizability of prompt learning for vision-language models. Specifically ProMetaR meta-learns both the regularizer and the soft prompts to harness the task-specific knowledge from the downstream tasks and task-agnostic general knowledge from the vision-language models. Further ProMetaR augments the task to generate multiple virtual tasks to alleviate the meta-overfitting. In addition we provide the analysis to comprehend how ProMetaR improves the generalizability of prompt tuning in the perspective of the gradient alignment. Our extensive experiments demonstrate that our ProMetaR improves the generalizability of conventional prompt learning methods under base-to-base/base-to-new and domain generalization settings. The code of ProMetaR is available at https://github.com/mlvlab/ProMetaR.

Adding artificial patterns to objects like QR codes can ease tasks such as object tracking robot navigation and conveying information (e.g. a label or a website link). However these patterns require a physical application and they alter the object's appearance. Conversely projected patterns can temporarily change the object's appearance aiding tasks like 3D scanning and retrieving object textures and shading. However projected patterns impede dynamic tasks like object tracking because they do not `stick' to the object's surface. Or do they? This paper introduces a novel approach combining the advantages of projected and persistent physical patterns. Our system projects heat patterns using a laser beam (similar in spirit to a LIDAR) which a thermal camera observes and tracks. Such thermal patterns enable tracking poorly-textured objects whose tracking is highly challenging with standard cameras while not affecting the object's appearance or physical properties. To avail these thermal patterns in existing vision frameworks we train a network to reverse heat diffusion's effects and remove inconsistent pattern points between different thermal frames. We prototyped and tested this approach on dynamic vision tasks like structure from motion optical flow and object tracking of everyday textureless objects.

In this work we introduce ProMotion a unified prototypical transformer-based framework engineered to model fundamental motion tasks. ProMotion offers a range of compelling attributes that set it apart from current task-specific paradigms. 1. We adopt a prototypical perspective establishing a unified paradigm that harmonizes disparate motion learning approaches. This novel paradigm streamlines the architectural design enabling the simultaneous assimilation of diverse motion information. 2. We capitalize on a dual mechanism involving the feature denoiser and the prototypical learner to decipher the intricacies of motion. This approach effectively circumvents the pitfalls of ambiguity in pixel-wise feature matching significantly bolstering the robustness of motion representation. We demonstrate a profound degree of transferability across distinct motion patterns. This inherent versatility reverberates robustly across a comprehensive spectrum of both 2D and 3D downstream tasks. Empirical results demonstrate that outperforms various well-known specialized architectures achieving 0.54 and 0.054 Abs Rel error on the Sintel and KITTI depth datasets 1.04 and 2.01 average endpoint error on the clean and final pass of Sintel flow benchmark and 4.30 F1-all error on the KITTI flow benchmark. For its efficacy we hope our work can catalyze a paradigm shift in universal models in computer vision.

We propose the task of Panoptic Scene Completion (PSC) which extends the recently popular Semantic Scene Completion (SSC) task with instance-level information to produce a richer understanding of the 3D scene. Our PSC proposal utilizes a hybrid mask-based technique on the nonempty voxels from sparse multi-scale completions. Whereas the SSC literature overlooks uncertainty which is critical for robotics applications we instead propose an efficient ensembling to estimate both voxel-wise and instance-wise uncertainties along PSC. This is achieved by building on a multi-input multi-output (MIMO) strategy while improving performance and yielding better uncertainty for little additional compute. Additionally we introduce a technique to aggregate permutation-invariant mask predictions. Our experiments demonstrate that our method surpasses all baselines in both Panoptic Scene Completion and uncertainty estimation on three large-scale autonomous driving datasets. Our code and data are available at https://astra-vision.github.io/PaSCo .