CVPR 2024 论文列表

We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language. An agent can achieve such an understanding by either drawing upon episodic memory exemplified by agents on smart glasses or by actively exploring the environment as in the case of mobile robots. We accompany our formulation with OpenEQA -- the first open-vocabulary benchmark dataset for EQA supporting both episodic memory and active exploration use cases. OpenEQA contains over 1600 high-quality human generated questions drawn from over 180 real-world environments. In addition to the dataset we also provide an automatic LLM-powered evaluation protocol that has excellent correlation with human judgement. Using this dataset and evaluation protocol we evaluate several state-of-the-art foundation models including GPT-4V and find that they significantly lag behind human-level performance. Consequently OpenEQA stands out as a straightforward measurable and practically relevant benchmark that poses a considerable challenge to current generation of foundation models. We hope this inspires and stimulates future research at the intersection of Embodied AI conversational agents and world models.

Multiple instance learning (MIL) is the most widely used framework in computational pathology encompassing sub-typing diagnosis prognosis and more. However the existing MIL paradigm typically requires an offline instance feature extractor such as a pre-trained ResNet or a foundation model. This approach lacks the capability for feature fine-tuning within the specific downstream tasks limiting its adaptability and performance. To address this issue we propose a Re-embedded Regional Transformer (RRT) for re-embedding the instance features online which captures fine-grained local features and establishes connections across different regions. Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator RRT is tailored to re-embed instance features online. It serves as a portable module that can seamlessly integrate into mainstream MIL models. Extensive experimental results on common computational pathology tasks validate that: 1) feature re-embedding improves the performance of MIL models based on ResNet-50 features to the level of foundation model features and further enhances the performance of foundation model features; 2) the RRT can introduce more significant performance improvements to various MIL models; 3) RRT-MIL as an RRT-enhanced AB-MIL outperforms other latest methods by a large margin. The code is available at: https://github.com/DearCaat/RRT-MIL.

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

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.

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.