Recently, pixel-to-pixel contrastive learning in single-scale feature space has been widely studied in semantic segmentation to learn a unified feature expression for pixels of the same category. However, the unified representation is too extreme, and the receptive field of each single-scale pixel is limited, which is insufficient to reflect the representative features of the category. To address these problems, this article extends the single-scale feature space to that of multiscale and proposes a hierarchical contrastive learning (Hi-CL) method to explore pixel-to-component semantic relationships. First, we generate multiscale candidate samples by applying several pooling windows with different sizes on a feature map, where different windows may represent different parts of the objects in the image. Then, we prune the sample set through threshold-based criteria to select appropriate samples for feature representation learning. Finally, Hi-CL is performed to learn the pixel-to-component consistency with the pruned samples. Our method is easy to be applied on existing semantic segmentation models and obtains consistent improvement. Furthermore, we achieve state-of-the-art results on three popular benchmarks, including Cityscapes, ADE20K, and COCO Stuff datasets.

While large visual-language models (LVLM) have shown promising results on traditional visual question answering benchmarks, it is still challenging for them to answer complex VQA problems which requires diverse world knowledge.Motivated by the research of retrievalaugmented generation in the field of natural language processing, we use Dense Passage Retrieval (DPR) to retrieve related knowledge to help the model answer questions.However, DPR conduct retrieving in natural language space, which may not ensure comprehensive acquisition of image information.Thus, the retrieved knowledge is not truly conducive to helping answer the question, affecting the performance of the overall system.To address this issue, we propose a novel framework that leverages the visual-language model to select the key knowledge retrieved by DPR and answer questions.The framework consists of two modules: Selector and Answerer, where both are initialized by the LVLM and parameterefficiently finetuned by self-bootstrapping: find key knowledge in the retrieved knowledge documents using the Selector, and then use them to finetune the Answerer to predict answers; obtain the pseudo-labels of key knowledge documents based on the predictions of the Answerer and weak supervision labels, and then finetune the Selector to select key knowledge; repeat.Our framework significantly enhances the performance of the baseline on the challenging open-domain Knowledge-based VQA benchmark, OK-VQA, achieving a state-ofthe-art accuracy of 62.83%.Our code is publicly available at https://github.com/ haodongze/Self-KSel-QAns.

Recent studies on semantic segmentation are exploiting contextual information to address the problem of inconsistent parsing prediction in big objects and ignorance in small objects. However, they utilize multilevel contextual information equally across pixels, overlooking those different pixels may demand different levels of context. Motivated by the above-mentioned intuition, we propose a novel global-guided selective context network (GSCNet) to adaptively select contextual information for improving scene parsing. Specifically, we introduce two global-guided modules, called global-guided global module (GGM) and global-guided local module (GLM), to, respectively, select global context (GC) and local context (LC) for pixels. When given an input feature map, GGM jointly employs the input feature map and its globally pooled feature to learn its global contextual demand based on which per-pixel GC is selected. While GLM adopts low-level feature from the adjacent stage as LC and synthetically models the input feature map, its globally pooled feature and LC to generate local contextual demand, based on which per-pixel LC is selected. Furthermore, we combine these two modules as a selective context block and import such SCBs in different levels of the network to propagate contextual information in a coarse-to-fine manner. Finally, we conduct extensive experiments to verify the effectiveness of our proposed model and achieve state-of-the-art performance on four challenging scene parsing data sets, i.e., Cityscapes, ADE20K, PASCAL Context, and COCO Stuff. Especially, GSCNet-101 obtains 82.6% on Cityscapes test set without using coarse data and 56.22% on ADE20K test set.

In this article, we propose a Dual Relation-aware Attention Network (DRANet) to handle the task of scene segmentation. How to efficiently exploit context is essential for pixel-level recognition. To address the issue, we adaptively capture contextual information based on the relation-aware attention mechanism. Especially, we append two types of attention modules on the top of the dilated fully convolutional network (FCN), which model the contextual dependencies in spatial and channel dimensions, respectively. In the attention modules, we adopt a self-attention mechanism to model semantic associations between any two pixels or channels. Each pixel or channel can adaptively aggregate context from all pixels or channels according to their correlations. To reduce the high cost of computation and memory caused by the abovementioned pairwise association computation, we further design two types of compact attention modules. In the compact attention modules, each pixel or channel is built into association only with a few numbers of gathering centers and obtains corresponding context aggregation over these gathering centers. Meanwhile, we add a cross-level gating decoder to selectively enhance spatial details that boost the performance of the network. We conduct extensive experiments to validate the effectiveness of our network and achieve new state-of-the-art segmentation performance on four challenging scene segmentation data sets, i.e., Cityscapes, ADE20K, PASCAL Context, and COCO Stuff data sets. In particular, a Mean IoU score of 82.9% on the Cityscapes test set is achieved without using extra coarse annotated data.

Self-attention mechanism which aggregates features by capturing long-range relation between each pixel and its context, has been widely used in semantic segmentation task. However, only considering the similarity between pixels is easily disturbed by noisy pixels. In this paper, we propose a Point Set Attention Network (PSANet) for improving self-attention mechanism by correcting the noisy pixels. Specifically, we emphasize to contribute mutual improvement between pixels of the same class. For the pixel at a certain position, we consider current pixel and pixels in its neighborhood as a point set and generate a context-aware mask for selecting pixels belong to the same class as current pixel. Then the selected pixels are aggregated for a center pixel, with which current pixel is updated. In this way, pixels belong to the same class may have similar feature representation, thus promoting intra-class mutual improvement. Next, we compute the relation between each updated pixel and its context features, which often represent all pixels in the feature map. Finally, we aggregate context features on each pixel according to their relations with the pixel. We conduct extensive experiments to validate the effectiveness of our network and achieve outstanding performance on Cityscapes and PASCAL Context datasets.

Most image captioning models are autoregressive, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. Recently, non-autoregressive decoding has been proposed in machine translation to speed up the inference time by generating all words in parallel. Typically, these models use the word-level cross-entropy loss to optimize each word independently. However, such a learning process fails to consider the sentence-level consistency, thus resulting in inferior generation quality of these non-autoregressive models. In this paper, we propose a Non-Autoregressive Image Captioning (NAIC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL). CMAL formulates NAIC as a multi-agent reinforcement learning system where positions in the target sequence are viewed as agents that learn to cooperatively maximize a sentence-level reward. Besides, we propose to utilize massive unlabeled images to boost captioning performance. Extensive experiments on MSCOCO image captioning benchmark show that our NAIC model achieves a performance comparable to state-of-the-art autoregressive models, while brings 13.9x decoding speedup.