Vision systems that see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The complex relations between objects and their locations, ambiguities, and variations in the real-world environment can be better described in human language, naturally governed by grammatical rules and other modalities such as audio and depth. The models learned to bridge the gap between such modalities and large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. These models are referred to as foundation models. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions. In this survey, we provide a comprehensive review of such emerging foundation models, including typical architecture designs to combine different modalities (vision, text, audio, etc.), training objectives (contrastive, generative), pre-training datasets, fine-tuning mechanisms, and the common prompting patterns; textual, visual, and heterogeneous. We discuss the open challenges and research directions for foundation models in computer vision, including difficulties in their evaluations and benchmarking, gaps in their real-world understanding, limitations of contextual understanding, biases, vulnerability to adversarial attacks, and interpretability issues. We review recent developments in this field, covering a wide range of applications of foundation models systematically and comprehensively. A comprehensive list of foundation models studied in this work is available at https://github.com/awaisrauf/Awesome-CV-Foundational-Models.

Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformer-based architectures, originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long-range dependencies. Inspired by recent advances in computer vision, the remote sensing community has also witnessed an increased exploration of vision transformers for a diverse set of tasks. Although a number of surveys have focused on transformers in computer vision in general, to the best of our knowledge we are the first to present a systematic review of recent advances based on transformers in remote sensing. Our survey covers more than 60 recent transformer-based methods for different remote sensing problems in sub-areas of remote sensing: very high-resolution (VHR), hyperspectral (HSI) and synthetic aperture radar (SAR) imagery. We conclude the survey by discussing different challenges and open issues of transformers in remote sensing.

Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-training on another CD dataset and then fine-tuning on the target benchmark. This current strategy is driven by the fact that transformers typically require a large amount of training data to learn inductive biases, which is insufficient in standard CD datasets due to their small size. We develop an end-to-end CD approach with transformers that is trained from scratch and yet achieves state-of-the-art performance on five benchmarks. Instead of using conventional self-attention that struggles to capture inductive biases when trained from scratch, our architecture utilizes a shuffled sparse-attention operation that focuses on selected sparse informative regions to capture the inherent characteristics of the CD data. Moreover, we introduce a change-enhanced feature fusion (CEFF) module to fuse the features from input image pairs by performing a per-channel re-weighting. Our CEFF module aids in enhancing the relevant semantic changes while suppressing the noisy ones. Extensive experiments on five CD datasets reveal the merits of the proposed contributions, achieving gains as high as 1.35% in intersection over union (IoU) score, compared to the best-published results in the literature. Code is available at https://github.com/mustansarfiaz/ScratchFormer.

This paper proposes a scribble-based weakly supervised RGB-D salient object detection (SOD) method to relieve the annotation burden from pixel-wise annotations. In view of the ensuing performance drop, we summarize two natural deficiencies of the scribbles and try to alleviate them, which are the weak richness of the pixel training samples (WRPS) and the poor structural integrity of the salient objects (PSIO). WRPS hinders robust saliency perception learning, which can be alleviated via model design for robust feature learning and pseudo labels generation for training sample enrichment. Specifically, we first design a dynamic searching process module as a meta operation to conduct multi-scale and multi-modal feature fusion for the robust RGB-D SOD model construction. Then, a dual-branch consistency learning mechanism is proposed to generate enough pixel training samples for robust saliency perception learning. PSIO makes direct structural learning infeasible since scribbles can not provide integral structural supervision. Thus, we propose an edge-region structure-refinement loss to recover the structural information and make precise segmentation. We deploy all components and conduct ablation studies on two baselines to validate their effectiveness and generalizability. Experimental results on eight datasets show that our method outperforms other scribble-based SOD models and achieves comparable performance with fully supervised state-of-the-art methods.

Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are intra-class occlusions involving other pedestrians and inter-class occlusions caused by other objects, such as cars and bicycles. These result in a multitude of occlusion patterns. We propose an approach for occluded pedestrian detection with the following contributions. First, we introduce a novel mask-guided attention network that fits naturally into popular pedestrian detection pipelines. Our attention network emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features. Second, we propose the occlusion-sensitive hard example mining method and occlusion-sensitive loss that mines hard samples according to the occlusion level and assigns higher weights to the detection errors occurring at highly occluded pedestrians. Third, we empirically demonstrate that weak box-based segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts. Experiments are performed on CityPersons, Caltech and ETH datasets. Our approach sets a new state-of-the-art on all three datasets. Our approach obtains an absolute gain of 10.3% in log-average miss rate, compared with the best reported results on the heavily occluded HO pedestrian set of the CityPersons test set. Code and models are available at: https://github.com/Leotju/MGAN.

Few-shot segmentation (FSS) aims to accurately segment target objects in a query image using only a limited number of annotated support images. Existing approaches typically follow a paradigm that directly leverages category information from the support set to identify target objects in the query. However, these methods often ignore the category information gap between query and support images, leading to suboptimal performance when faced with images containing objects exhibiting significant intra-class diversity. To address this issue, we propose a novel framework that introduces intermediate prototypes to capture both deterministic information from the support images and adaptive knowledge from the query at multiple scales. Our framework, named the K-shot Multi-scale Intermediate Prototype Mining Transformer (KMIPMT), is based on the Transformer architecture and learns intermediate prototypes in an iterative manner, where each KMIPMT layer propagates category information from both K-shot support features and multi-scale query features to intermediate prototypes. This information is then utilized to activate the query feature map. Through repeated iterations, both intermediate prototypes and the query feature are progressively enhanced, and the final refined query feature is used for generating precise segmentation predictions. Despite its simplicity, our method achieves remarkable performance gains on standard benchmarks, including PASCAL-$5^{i}$, COCO-$20^{i}$, and FSS-1000, setting new state-of-the-art results. Furthermore, we explore several practical and challenging extensions of our method, including 3D point cloud FSS, zero-shot segmentation, weak-label FSS, and cross-domain FSS. These extensions showcase the versatility and effectiveness of our proposed KMIPMT framework across different domains and scenarios.