CVPR 2024 论文列表

This work presents Depth Anything a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules we aim to build a simple yet powerful foundation model dealing with any images under any circumstances. To this end we scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data ( 62M) which significantly enlarges the data coverage and thus is able to reduce the generalization error. We investigate two simple yet effective strategies that make data scaling-up promising. First a more challenging optimization target is created by leveraging data augmentation tools. It compels the model to actively seek extra visual knowledge and acquire robust representations. Second an auxiliary supervision is developed to enforce the model to inherit rich semantic priors from pre-trained encoders. We evaluate its zero-shot capabilities extensively including six public datasets and randomly captured photos. It demonstrates impressive generalization ability. Further through fine-tuning it with metric depth information from NYUv2 and KITTI new SOTAs are set. Our better depth model also results in a better depth-conditioned ControlNet. Our models are released at https://github.com/LiheYoung/Depth-Anything.

Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail CLOUDS is a framework that integrates Foundation Models of various kinds: (i) CLIP backbone for its robust feature representation (ii) Diffusion Model to diversify the content thereby covering various modes of the possible target distribution and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions notably outperforming prior methods by 5.6% and 6.7% on averaged mIoU respectively. Our code is available at https://github.com/yasserben/CLOUDS

In the realm of geospatial analysis the diversity of remote sensors encompassing both optical and microwave technologies offers a wealth of distinct observational capabilities. Recognizing this we present msGFM a multisensor geospatial foundation model that effectively unifies data from four key sensor modalities. This integration spans an expansive dataset of two million multisensor images. msGFM is uniquely adept at handling both paired and unpaired sensor data. For data originating from identical geolocations our model employs an innovative cross-sensor pretraining approach in masked image modeling enabling the synthesis of joint representations from diverse sensors. msGFM incorporating four remote sensors upholds strong performance forming a comprehensive model adaptable to various sensor types. msGFM has demonstrated enhanced proficiency in a range of both single-sensor and multisensor downstream tasks. These include scene classification segmentation cloud removal and pan-sharpening. A key discovery of our research is that representations derived from natural images are not always compatible with the distinct characteristics of geospatial remote sensors underscoring the limitations of existing representations in this field. Our work can serve as a guide for developing multisensor geospatial pretraining models paving the way for more advanced geospatial capabilities. Code can be found at \url https://github.com/boranhan/Geospatial_Foundation_Models

The recently proposed SparseFormer architecture provides an alternative approach to visual understanding by utilizing a significantly lower number of visual tokens via adjusting RoIs greatly reducing computational costs while still achieving promising performance. However training SparseFormers from scratch is still expensive and scaling up the number of parameters can be challenging. In this paper we propose to bootstrap SparseFormers from ViT-based vision foundation models in a simple and efficient way. Since the majority of SparseFormer blocks are the standard transformer ones we can inherit weights from large-scale pre-trained vision transformers and freeze them as much as possible. Therefore we only need to train the SparseFormer-specific lightweight focusing transformer to adjust token RoIs and fine-tune a few early pre-trained blocks to align the final token representation. In such a way we can bootstrap SparseFormer architectures from various large-scale pre-trained models (e.g. IN-21K pre-trained AugRegs or CLIPs) using a rather smaller amount of training samples (e.g. IN-1K) and without labels or captions within just a few hours. As a result the bootstrapped unimodal SparseFormer (from AugReg-ViT-L/16-384) can reach 84.9% accuracy on IN-1K with only 49 tokens and the multimodal SparseFormer from CLIPs also demonstrates notable zero-shot performance with highly reduced computational cost without seeing any caption during the bootstrapping procedure. In addition CLIP-bootstrapped SparseFormers which align the output space with language without seeing a word can serve as efficient vision encoders in multimodal large language models. Code and models are available at https://github.com/showlab/sparseformer

Supernet is a core component in many recent Neural Architecture Search (NAS) methods. It not only helps embody the search space but also provides a (relative) estimation of the final performance of candidate architectures. Thus it is critical that the top architectures ranked by a supernet should be consistent with those ranked by true performance which is known as the order-preserving ability. In this work we analyze the order-preserving ability on the whole search space (global) and a sub-space of top architectures (local) and empirically show that the local order-preserving for current two-stage NAS methods still need to be improved. To rectify this we propose a novel concept of Supernet Shifting a refined search strategy combining architecture searching with supernet fine-tuning. Specifically apart from evaluating the training loss is also accumulated in searching and the supernet is updated every iteration. Since superior architectures are sampled more frequently in evolutionary searching the supernet is encouraged to focus on top architectures thus improving local order-preserving. Besides a pre-trained supernet is often un-reusable for one-shot methods. We show that Supernet Shifting can fulfill transferring supernet to a new dataset. Specifically the last classifier layer will be unset and trained through evolutionary searching. Comprehensive experiments show that our method has better order-preserving ability and can find a dominating architecture. Moreover the pre-trained supernet can be easily transferred into a new dataset with no loss of performance.

Image Quality Assessment (IQA) constitutes a fundamental task within the field of computer vision yet it remains an unresolved challenge owing to the intricate distortion conditions diverse image contents and limited availability of data. Recently the community has witnessed the emergence of numerous large-scale pretrained foundation models. However it remains an open problem whether the scaling law in high-level tasks is also applicable to IQA tasks which are closely related to low-level clues. In this paper we demonstrate that with a proper injection of local distortion features a larger pretrained vision transformer (ViT) foundation model performs better in IQA tasks. Specifically for the lack of local distortion structure and inductive bias of the large-scale pretrained ViT we use another pretrained convolution neural networks (CNNs) which is well known for capturing the local structure to extract multi-scale image features. Further we propose a local distortion extractor to obtain local distortion features from the pretrained CNNs and a local distortion injector to inject the local distortion features into ViT. By only training the extractor and injector our method can benefit from the rich knowledge in the powerful foundation models and achieve state-of-the-art performance on popular IQA datasets indicating that IQA is not only a low-level problem but also benefits from stronger high-level features drawn from large-scale pretrained models. Codes are publicly available at: https://github.com/NeosXu/LoDa.

Images of the natural world collected by a variety of cameras from drones to individual phones are increasingly abundant sources of biological information. There is an explosion of computational methods and tools particularly computer vision for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions contexts and datasets. A vision model for general organismal biology questions on images is of timely need. To approach this we curate and release TreeOfLife-10M the largest and most diverse ML-ready dataset of biology images. We then develop BioCLIP a foundation model for the tree of life leveraging the unique properties of biology captured by TreeOfLife-10M namely the abundance and variety of images of plants animals and fungi together with the availability of rich structured biological knowledge. We rigorously benchmark our approach on diverse fine-grained biology classification tasks and find that BioCLIP consistently and substantially outperforms existing baselines (by 17% to 20% absolute). Intrinsic evaluation reveals that BioCLIP has learned a hierarchical representation conforming to the tree of life shedding light on its strong generalizability. All data code and models will be publicly released upon acceptance.

Self-supervised foundation models have shown great potential in computer vision thanks to the pre-training paradigm of masked autoencoding. Scale is a primary factor influencing the performance of these foundation models. However these large foundation models often result in high computational cost. This paper focuses on pre-training relatively small vision transformer models that could be efficiently adapted to downstream tasks. Specifically taking inspiration from knowledge distillation in model compression we propose a new asymmetric masked distillation (AMD) framework for pre-training relatively small models with autoencoding. The core of AMD is to devise an asymmetric masking strategy where the teacher model is enabled to see more context information with a lower masking ratio while the student model is still equipped with a high masking ratio. We design customized multi-layer feature alignment between the teacher encoder and student encoder to regularize the pre-training of student MAE. To demonstrate the effectiveness and versatility of AMD we apply it to both ImageMAE and VideoMAE for pre-training relatively small ViT models. AMD achieved 84.6% classification accuracy on IN1K using the ViT-B model. And AMD achieves 73.3% classification accuracy using the ViT-B model on the Something-in-Something V2 dataset a 3.7% improvement over the original ViT-B model from VideoMAE. We also transfer AMD pre-trained models to downstream tasks and obtain consistent performance improvement over the original masked autoencoding. The code and models are available at https://github.com/MCG-NJU/AMD.

In Re-identification (ReID) recent advancements yield noteworthy progress in both unimodal and cross-modal retrieval tasks. However the challenge persists in developing a unified framework that could effectively handle varying multimodal data including RGB infrared sketches and textual information. Additionally the emergence of large-scale models shows promising performance in various vision tasks but the foundation model in ReID is still blank. In response to these challenges a novel multimodal learning paradigm for ReID is introduced referred to as All-in-One (AIO) which harnesses a frozen pre-trained big model as an encoder enabling effective multimodal retrieval without additional fine-tuning. The diverse multimodal data in AIO are seamlessly tokenized into a unified space allowing the modality-shared frozen encoder to extract identity-consistent features comprehensively across all modalities. Furthermore a meticulously crafted ensemble of cross-modality heads is designed to guide the learning trajectory. AIO is the first framework to perform all-in-one ReID encompassing four commonly used modalities. Experiments on cross-modal and multimodal ReID reveal that AIO not only adeptly handles various modal data but also excels in challenging contexts showcasing exceptional performance in zero-shot and domain generalization scenarios. Code will be available at: https://github.com/lihe404/AIO.

While fine-tuning is a de facto standard method for training deep neural networks it still suffers from overfitting when using small target datasets. Previous methods improve fine-tuning performance by maintaining knowledge of the source datasets or introducing regularization terms such as contrastive loss. However these methods require auxiliary source information (e.g. source labels or datasets) or heavy additional computations. In this paper we propose a simple method called adaptive random feature regularization (AdaRand). AdaRand helps the feature extractors of training models to adaptively change the distribution of feature vectors for downstream classification tasks without auxiliary source information and with reasonable computation costs. To this end AdaRand minimizes the gap between feature vectors and random reference vectors that are sampled from class conditional Gaussian distributions. Furthermore AdaRand dynamically updates the conditional distribution to follow the currently updated feature extractors and balance the distance between classes in feature spaces. Our experiments show that AdaRand outperforms the other fine-tuning regularization requiring auxiliary source information and heavy computation costs.

In the evolving landscape of computer vision foundation models have emerged as pivotal tools exhibiting exceptional adaptability to a myriad of tasks. Among these the Segment Anything Model (SAM) by Meta AI has distinguished itself in image segmentation. However SAM like its counterparts encounters limitations in specific niche applications prompting a quest for enhancement strategies that do not compromise its inherent capabilities. This paper introduces ASAM a novel methodology that amplifies SAM's performance through adversarial tuning. We harness the potential of natural adversarial examples inspired by their successful implementation in natural language processing. By utilizing a stable diffusion model we augment a subset (1%) of the SA-1B dataset generating adversarial instances that are more representative of natural variations rather than conventional imperceptible perturbations. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations thereby preserving the integrity of the segmentation task. The fine-tuned ASAM demonstrates significant improvements across a diverse range of segmentation tasks without necessitating additional data or architectural modifications. The results of our extensive evaluations confirm that ASAM establishes new benchmarks in segmentation tasks thereby contributing to the advancement of foundational models in computer vision. Our project page is in https://asam2024.github.io/.

A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks. VFMs like CLIP DINOv2 SAM are trained with distinct objectives exhibiting unique characteristics for various downstream tasks. We find that despite their conceptual differences these models can be effectively merged into a unified model through multi-teacher distillation. We name this approach AM-RADIO (Agglomerative Model -- Reduce All Domains Into One). This integrative approach not only surpasses the performance of individual teacher models but also amalgamates their distinctive features such as zero-shot vision-language comprehension detailed pixel-level understanding and open vocabulary segmentation capabilities. Additionally in pursuit of the most hardware-efficient backbone we evaluated numerous architectures in our multi-teacher distillation pipeline using the same training recipe. This led to the development of a novel architecture (E-RADIO) that exceeds the performance of its predecessors and is at least 6x faster than the teacher models at matched resolution. Our comprehensive benchmarking process covers downstream tasks including ImageNet classification semantic segmentation linear probing COCO object detection and integration into LLaVa-1.5.

Extensive advancements have been made in person ReID through the mining of semantic information. Nevertheless existing methods that utilize semantic-parts from a single image modality do not explicitly achieve this goal. Whiteness the impressive capabilities in multimodal understanding of Vision Language Foundation Model CLIP a recent two-stage CLIP-based method employs automated prompt engineering to obtain specific textual labels for classifying pedestrians. However we note that the predefined soft prompts may be inadequate in expressing the entire visual context and struggle to generalize to unseen classes. This paper presents an end-to-end Prompt-driven Semantic Guidance (PromptSG) framework that harnesses the rich semantics inherent in CLIP. Specifically we guide the model to attend to regions that are semantically faithful to the prompt. To provide personalized language descriptions for specific individuals we propose learning pseudo tokens that represent specific visual contexts. This design not only facilitates learning fine-grained attribute information but also can inherently leverage language prompts during inference. Without requiring additional labeling efforts our PromptSG achieves state-of-the-art by over 10% on MSMT17 and nearly 5% on the Market-1501 benchmark.

The lifting of a 3D structure and camera from 2D landmarks is at the cornerstone of the discipline of computer vision. Traditional methods have been confined to specific rigid objects such as those in Perspective-n-Point (PnP) problems but deep learning has expanded our capability to reconstruct a wide range of object classes (e.g. C3DPO [??] and PAUL [??]) with resilience to noise occlusions and perspective distortions. However all these techniques have been limited by the fundamental need to establish correspondences across the 3D training data significantly limiting their utility to applications where one has an abundance of "in-correspondence" 3D data. Our approach harnesses the inherent permutation equivariance of transformers to manage varying numbers of points per 3D data instance withstands occlusions and generalizes to unseen categories. We demonstrate state-of-the-art performance across 2D-3D lifting task benchmarks. Since our approach can be trained across such a broad class of structures we refer to it simply as a 3D Lifting Foundation Model (3D-LFM) -- the first of its kind.