Object detection, which aims to locate and recognize objects in images, is evolving toward reduced reliance on manual annotations and enhanced adaptability to open-world scenarios. This shift has led to open-vocabulary object detection (OVD), which enables zero-shot detection of objects from novel categories beyond the base categories. In this work, we identify three key challenges in detecting unseen class instances: 1) locating the instances of new classes; 2) distinguishing new class instances from the background; 3) recognizing new class instances. We propose a detection framework that leverages vision-language pre-trained (VLPT) models, such as CLIP, as the backbone to jointly address these three challenges. Specifically, we treat localization as a box-deformation decision process, where the agent interacts with the image to learn a universal deformation strategy, enhancing generalization for unseen class objects. We further reformulate the foreground-background classification as an objectness ranking task to improve objectness evaluation, utilizing a specially designed AP loss. Additionally, a feature magnitude minimization constraint is introduced for the adapter during fine-tuning, boosting recognition performance for both base and novel classes. Experiments on COCO and LVIS datasets demonstrate that our method outperforms previous approaches in open-vocabulary object detection.

Visual grounding, aiming to align image regions with textual queries, is a fundamental task for cross-modal learning. We study the weakly supervised visual grounding, where only image-text pairs at a coarse-grained level are available. Due to the lack of fine-grained correspondence information, existing approaches often encounter matching ambiguity. To overcome this challenge, we introduce the cycle consistency constraint into region-phrase pairs, which strengthens correlated pairs and weakens unrelated pairs. This cycle pairing makes use of the bidirectional association between image regions and text phrases to alleviate matching ambiguity. Furthermore, we propose a parallel grounding framework, where backbone networks and subsequent relation modules extract individual and contextual representations to calculate context-free and context-aware similarities between regions and phrases separately. Those two representations characterize visual/linguistic individual concepts and inter-relationships, respectively, and then complement each other to achieve cross-modal alignment. The whole framework is trained by minimizing an image-text contrastive loss and a cycle consistency loss. During inference, the above two similarities are fused to give the final region-phrase matching score. Experiments on five popular datasets about visual grounding demonstrate a noticeable improvement in our method. The source code is available at https://github.com/Evergrow/WSVG.

Recent literature has developed two advanced tools for image inpainting: appearance propagation and attention matching. However, given the ineffective feature reorganization and vulnerable attention maps, existing works yield suboptimal results with distorted structures and inconsistent contents. Furthermore, we observe that deep sampling layers (DSL) and shallow skip connections (SSC) in U-Net separately promote image structure inference and texture synthesis. To address the above two issues, we devise a W-shaped network (W-Net), which consists of two key components: a texture spatial attention (TSA) module in SSC and a structure channel excitation (SCE) module in DSL. W-Net is a two-stage network, with coarse and refined structures derived at each stage. Meanwhile, the TSA module fills incomplete textures with reliable attention scores under the guidance of coarse structures, which effectively diminishes inconsistency from appearance to semantics. The SCE module rectifies structures according to the difference between coarse structures and refined structures enhanced by texture features. Then the module motivates them to produce more reasonable shapes. Complete textures and refined structures constitute desired inpainted images, as the output of W-Net. Experiments on multiple datasets demonstrate the superior performance of W-Net.

Image inpainting has made remarkable progress with recent advances in deep learning. Popular networks mainly follow an encoder-decoder architecture (sometimes with skip connections) and possess sufficiently large receptive field, i.e., larger than the image resolution. The receptive field refers to the set of input pixels that are path-connected to a neuron. For image inpainting task, however, the size of surrounding areas needed to repair different kinds of missing regions are different, and the very large receptive field is not always optimal, especially for the local structures and textures. In addition, a large receptive field tends to involve more undesired completion results, which will disturb the inpainting process. Based on these insights, we rethink the process of image inpainting from a different perspective of receptive field, and propose a novel three-stage inpainting framework with local and global refinement. Specifically, we first utilize an encoder-decoder network with skip connection to achieve coarse initial results. Then, we introduce a shallow deep model with small receptive field to conduct the local refinement, which can also weaken the influence of distant undesired completion results. Finally, we propose an attention-based encoder-decoder network with large receptive field to conduct the global refinement. Experimental results demonstrate that our method outperforms the state of the arts on three popular publicly available datasets for image inpainting. Our local and global refinement network can be directly inserted into the end of any existing networks to further improve their inpainting performance. Code is available at https://github.com/weizequan/LGNet.git.