27 accepted papers
Engineering diagrams are the core carriers of technical information in industrial contexts, where the pressing demand for their digitization from industrial sectors has driven great advancements in related research domains. However, existing research still suffers from three limitations. Firstly, th
Handwritten Text Generation (HTG) seeks to synthesize realistic and personalized handwriting by modeling stylistic and structural traits. While recent diffusion-based approaches have advanced generation fidelity, they typically rely on auxiliary style or content encoders with handcrafted objectives,…
We introduce a novel method for video Scene Text Segmentation (STS), a task critical for understanding dynamic visual content. Despite the success of foundation models like Segment Anything Model 2 (SAM2) in generic segmentation, their application to video STS is hindered by the reliance on external
Computer vision foundation models, such as DINO or OpenCLIP, are trained in a self-supervised manner on large image datasets. Analogously, substantial evidence suggests that the human visual system (HVS) is influenced by the statistical distribution of colors and patterns in the natural world, chara…
Document image segmentation is crucial in document analysis and recognition but remains challenging due to the heterogeneity of document formats and diverse segmentation tasks. Existing methods often treat these tasks separately, leading to limited generalization and resource wastage.This paper intr…
We present a novel framework for generating high-quality, animatable 4D avatar from a single image. While recent advances have shown promising results in 4D avatar creation, existing methods either require extensive multiview data or struggle with geometry accuracy and identity consistency. To addre…
Large Vision-Language Models (LVLMs) have shown impressive capabilities across various domains, but existing LVLMs have limited performance in dense perception and structured learning problems, such as Handwritten Mathematical Expression Recognition (HMER). The primary challenges stem from the compl…
Multimodal Large Language Models (MLLMs) have shown promising capabilities in mathematical reasoning within visual contexts across various datasets. However, most existing multimodal math benchmarks are limited to single-visual contexts, which diverges from the multi-visual scenarios commonly encoun…
Geometry is a fundamental branch of mathematics and plays a crucial role in evaluating the reasoning capabilities of multimodal large language models (MLLMs). However, existing multimodal mathematics benchmarks mainly focus on plane geometry and largely ignore solid geometry, which requires spatial…
Diffusion models have recently demonstrated an impressive ability to address inverse problems in an unsupervised manner. While existing methods primarily focus on modifying the posterior sampling process, the potential of the forward process remains largely unexplored. In this work, we propose Short…
Recent advancements in large language models (LLMs) and multi-modal models (MMs) have demonstrated their remarkable capabilities in problem-solving. Yet, their proficiency in tackling geometry math problems, which necessitates an integrated understanding of both textual and visual information, has n…
Geometry problem solving (GPS) is a challenging mathematical reasoning task requiring multi-modal understanding, fusion, and reasoning. Existing neural solvers take GPS as a vision-language task but are short in the representation of geometry diagrams that carry rich and complex layout information.…
Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, r…
Geometry problem solving (GPS) is a high-level mathematical reasoning requiring the capacities of multi-modal fusion and geometric knowledge application. Recently, neural solvers have shown great potential in GPS but still be short in diagram presentation and modal fusion. In this work, we convert d…
Social networks are essentially in a graph structure where persons act as nodes and the edges connecting nodes denote social relations. The prediction of social relations, therefore, relies on the context in graphs to model the higher-order constraints among relations, which has not been exploited s…
Stroke classification for online handwriting aims at providing each stroke with a semantic label so as to fulfill handwriting segmentation. This task has attracted considerable attention due to its significance in online handwriting analysis. Existing methods are designed for the static situation, w…
We target cross-domain face reenactment in this paper, i.e., driving a cartoon image with the video of a real person and vice versa. Recently, many works have focused on one-shot talking face generation to drive a portrait with a real video, i.e., within-domain reenactment. Straightforwardly applyin…
Although previous works on traffic scene understanding have achieved great success, most of them stop at a lowlevel perception stage, such as road segmentation and lane detection, and few concern high-level understanding. In this paper, we present Visual Traffic Knowledge Graph Generation (VTKGG), a…
"Recently, several spatial-temporal memory-based methods have verified that storing intermediate frames and their masks as memory are helpful to segment target objects in videos. However, they mainly focus on better matching between the current frame and the memory frames without explicitly paying a…
Geometry diagram parsing plays a key role in geometry problem solving, wherein the primitive extraction and relation parsing remain challenging due to the complex layout and between-primitive relationship. In this paper, we propose a powerful diagram parser based on deep learning and graph reasoning…
"One-shot talking face generation aims at synthesizing a high-quality talking face video from an arbitrary portrait image, driven by a video or an audio segment. In this work, we provide a solution from a novel perspective that differs from existing frameworks. We first investigate the latent featur…
Recent handwritten mathematical expression recognition (HMER) approaches treat the problem as an image-to-markup generation task where the handwritten formula is translated into a sequence (e.g. LaTeX). The encoder-decoder framework is widely used to solve this image-to-sequence problem. However, (i…
Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally. Recently, various incremental learning methods have been proposed, and some approaches achieved acceptable performance relying on stored data o…
Most existing video text detection methods track texts with appearance features, which are easily influenced by the change of perspective and illumination. Compared with appearance features, semantic features are more robust cues for matching text instances. In this paper, we propose an end-to-end t…
Most existing text spotting methods either focus on horizontal/oriented texts or perform arbitrary shaped text spotting with character-level annotations. In this paper, we propose a novel text spotting framework to detect and recognize text of arbitrary shapes in an end-to-end manner, using only wor…
Convolutional neural networks (CNNs) have been widely used for image classification. Despite its high accuracies, CNN has been shown to be easily fooled by some adversarial examples, indicating that CNN is not robust enough for pattern classification. In this paper, we argue that the lack of robustn…
In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some b…