Long-term time series forecasting has been widely studied, yet two aspects remain insufficiently explored: the interaction learning between different frequency components and the exploitation of periodic characteristics inherent in timestamps. To address the above issues, we propose CFPT, a novel method that empowering time series forecasting through Cross-Frequency Interaction (CFI) and Periodic-Aware Timestamp Modeling (PTM). To learn cross-frequency interactions, we design the CFI branch to process signals in frequency domain and captures their interactions through a feature fusion mechanism. Furthermore, to enhance prediction performance by leveraging timestamp periodicity, we develop the PTM branch which transforms timestamp sequences into 2D periodic tensors and utilizes 2D convolution to capture both intra-period dependencies and inter-period correlations of time series based on timestamp patterns. Extensive experiments on multiple real-world benchmarks demonstrate that CFPT achieves state-of-the-art performance in long-term forecasting tasks. The code is publicly available at this repository: https://github.com/BUPT-SN/CFPT.

The advancements in generative models and the real-world attack of machine-generated text(MGT) create a demand for more robust detection methods. The existing MGT detection methods for adversarial environments primarily consist of manually designed statistical-based methods and fine-tuned classifier-based approaches. Statistical-based methods extract intrinsic features but suffer from rigid decision boundaries vulnerable to adaptive attacks, while fine-tuned classifiers achieve outstanding performance at the cost of overfitting to superficial textual feature. We argue that the key to detection in current adversarial environments lies in how to extract intrinsic invariant features and ensure that the classifier possesses dynamic adaptability. In that case, we propose OSTAR, a novel MGT detection framework designed for adversarial environments which composed of a statistical enhanced classifier and a Multi-Faceted Contrastive Learning(MFCL). In the classifier aspect, our Multi-Dimensional Statistical Profiling (MDSP) module extracts intrinsic difference between human and machine texts, complementing classifiers with useful stable features. In the model optimization aspect, the MFCL strategy enhances robustness by contrasting feature variations before and after text attacks, jointly optimizing statistical feature mapping and baseline pre-trained models. Experimental results on three public datasets under various adversarial scenarios demonstrate that our framework outperforms existing MGT detection methods, achieving state-of-the-art performance and robust against attacks.The code is available at https://github.com/BUPT-SN/OSTAR.

Recent advances in large multimodal models (LMMs) have enabled substantial progress in various visual question answering (VQA) benchmarks, including the challenging text-centric ones that require a simultaneous understanding of both the visual and textual contents in the images. Despite the prominence of existing text-centric VQA benchmarks, they either have limited textual information or have a limited number of questions requiring complex reasoning skills beyond the basic OCR. To this end, we present SMVQA—a novel text-centric VQA benchmark based on street map images. SMVQA contains more than 10K real-world street map images from the open geospatial database OpenStreetMap. Each image in SMVQA is also associated with detailed geospatial annotations, enabling it to automatically generate up to 57.5K distinctive QA pairs of five representative question types. In addition to the standard test split, SMVQA introduces an extra test split to verify the generalization abilities over out-of-domain images and novel reasoning skills. The evaluation of the state-of-the-art open-source and commercial LMMs reflects the great challenge posed by SMVQA. The latest LMMs, such as GPT-4o, only achieve accuracies of 49.9%, showing plenty of room for improvement. To further improve the latest LMMs’ performance on SMVQA, we introduce a LMM-based agentic framework LHR, which consists of the localizing, highlighting, and reasoning stages. Specifically, LHR first prompts the LMM to localize region-of-interest (RoI) to the question and then highlight the RoI and perform chain-of-thought reasoning for answer prediction. By integrating LHR with GPT-4o, we observe a significant improvement over the vanilla counterpart, showing the effectiveness of our framework.

The issue of face privacy protection has aroused wide social concern along with the increasing applications of face images. The latest methods focus on achieving a good privacy-utility tradeoff so that the protected results can still be used to support the downstream computer vision tasks. However, they may suffer from limited flexibility in manipulating this tradeoff because the practical requirements may vary under different scenarios. In this paper, we present a novel recurrent latent representation reorganization (LReOrg) framework to deal with the problem. LReOrg relies on two key modules to deal with the privacy-utility tradeoff, where the first one is responsible for anonymizing the privacy sensitive information and the other is responsible for recovering the destroyed useful insensitive information according to user requirements. LReOrg is advantageous in: (a) enabling users to recurrently process fine-grained attributes; (b) providing flexible control over privacy-utility tradeoff by manipulating which attributes to anonymize or preserve using cross-modal keywords; and (c) eliminating the need of data annotations for network training. The experimental results on benchmark datasets have reported the superior ability of our approach for providing flexible protection on facial information.

Temporal action detection not only requires correct classification but also needs to detect the start and end times of each action accurately. However, traditional approaches always employ sliding windows or actionness to predict the actions, and it is different to train to model with sliding windows or actionness by end-to-end means. In this article, we attempt a different idea to detect the actions end-to-end, which can calculate the probabilities of actions directly through one network as one part of the results. We present PCAD, a novel proposal complementary action detector to deal with video streams under continuous, untrimmed conditions. Our approach first uses a simple fully 3D convolutional network to encode the video streams and then generates candidate temporal proposals for activities by using anchor segments. To generate more precise proposals, we also design a boundary proposal network to offer some complementary information for the candidate proposals. Finally, we learn an efficient classifier to classify the generated proposals into different activities and refine their temporal boundaries at the same time. Our model can achieve end-to-end training by jointly optimizing classification loss and regression loss. When evaluating on the THUMOS’14 detection benchmark, PCAD achieves state-of-the-art performance in high-speed models.

Temporal action detection is still a challenging task. This task not only requires correct classification, but also needs to accurately detect the start and end times of each action. In this paper, we present a novel proposal complementary action detector (PCAD) to deal with video streams under continuous, untrimmed conditions. Our approach first uses a simple fully 3D convolutional (Conv3D) network to encode the video streams and then generates candidate temporal proposals for activities by using anchor segments. To generate more precise proposals, we also designed a boundary proposal network (BPN) to offer some complementary information for the candidate proposals. Finally, we learn an efficient classifier to classify the generated proposals into different activities and refine their temporal boundaries at the same time. Our model can achieve end-to-end training by jointly optimizing classification loss and regression loss. When evaluating on THUMOS'14 detection benchmark, PCAD achieves the state-of-the-art performance in high-speed models.

Phrase‐based visual grounding aims to localise the object in the image referred by a textual query phrase. Most existing approaches adopt a two‐stage mechanism to address this problem: first, an off‐the‐shelf proposal generation model is adopted to extract region‐based visual features, and then a deep model is designed to score the proposals based on the query phrase and extracted visual features. In contrast to that, the authors design an end‐to‐end approach to tackle the visual grounding problem in this study. They use a region proposal network to generate object proposals and the corresponding visual features simultaneously, and multi‐modal factorised bilinear pooling model to fuse the multi‐modal features effectively. After that, two novel losses are posed on top of the multi‐modal features to rank and refine the proposals, respectively. To verify the effectiveness of the proposed approach, the authors conduct experiments on three real‐world visual grounding datasets, namely Flickr‐30k Entities, ReferItGame and RefCOCO. The experimental results demonstrate the significant superiority of the proposed method over the existing state‐of‐the‐arts.

The applications of isometric 3-D objects have recently received sufficient attention and, thus, it is very attractive to retrieve such isometric 3-D objects from large-scale collections. Although existing approaches have presented some interesting ideas, their performance is limited to their ability on feature representation. To improve the performance of 3-D object (shape) recognition, some recent algorithms prefer using complicated deep neural networks to learn discriminative features, but they consume huge amounts of computing resources. Instead, this paper presents a more effective solution by seamlessly integrating the traditional local descriptor with a deep pointwise convolutional network to extract 1-D features for shape recognition and retrieval. To reduce the costs of designing a complicated deep network, the first step of our algorithm is to describe the shape deformation by sampling a set of intrinsic point descriptors. Then, we introduce a simple yet effective pointwise convolutional network to integrate these descriptors as a global feature and the learning process can be significantly accelerated with the help of downsampling. Furthermore, a knowledge transfer strategy is used to upgrade our feature by compensating for information loss. Finally, we carry out experimental evaluations over popular shape benchmarks, and the results suggest that our approach exhibits superior accuracy rates and robustness on shape recognition and retrieval.