Image Captioning With Controllable and Adaptive Length Levels.

Image captioning is a core challenge in computer vision, attracting significant attention. Traditional methods prioritize caption quality, often overlooking style control. Our research enhances method controllability, enabling descriptions of varying detail. By integrating a length level embedding into current models, they can produce detailed or concise captions, increasing diversity. We introduce a length-level reranking transformer to correlate image and text complexity, optimizing caption length for informativeness without redundancy. Additionally, with caption length increase, computational complexity grows due to the autoregressive (AR) design of existing methods. To address this, our non-autoregressive (NAR) model maintains constant complexity regardless of caption length. We've developed a training approach that includes refinement sequence training and sequence-level knowledge distillation to close the performance gap between NAR and AR models. In testing, our models set new standards for caption quality on the MS COCO dataset and offer enhanced controllability and diversity. Our NAR model excels over AR models in these aspects and shows greater efficiency with longer captions. With advanced training techniques, our NAR's caption quality rivals that of leading AR models.

Visual Grounding Via Accumulated Attention.

Visual grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query. Generally, it requires the machine to first understand the query, identify the key concepts in the image, and then locate the target object by specifying its bounding box. However, in many real-world visual grounding applications, we have to face with ambiguous queries and images with complicated scene structures. Identifying the target based on highly redundant and correlated information can be very challenging, and often leading to unsatisfactory performance. To tackle this, in this paper, we exploit an attention module for each kind of information to reduce internal redundancies. We then propose an accumulated attention (A-ATT) mechanism to reason among all the attention modules jointly. In this way, the relation among different kinds of information can be explicitly captured. Moreover, to improve the performance and robustness of our VG models, we additionally introduce some noises into the training procedure to bridge the distribution gap between the human-labeled training data and the real-world poor quality data. With this "noised" training strategy, we can further learn a bounding box regressor, which can be used to refine the bounding box of the target object. We evaluate the proposed methods on four popular datasets (namely ReferCOCO, ReferCOCO+, ReferCOCOg, and GuessWhat?!). The experimental results show that our methods significantly outperform all previous works on every dataset in terms of accuracy.

Deep High-Resolution Representation Learning for Visual Recognition.

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions in series (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams in parallel and (ii) repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at https://github.com/HRNet.