22 accepted papers
While humans develop core visual skills long before acquiring language, contemporary Multimodal LLMs (MLLMs) still rely heavily on linguistic priors to compensate for their fragile visual understanding. We uncovered a crucial fact: state-of-the-art MLLMs consistently fail on basic visual tasks that …
When MLLMs fail at Science, Technology, Engineering, and Mathematics (STEM) visual reasoning, a fundamental question arises: is it due to perceptual deficiencies or reasoning limitations? Through systematic scaling analysis that independently scales perception and reasoning components, we uncover a
The cold-start initialization stage plays a pivotal role in training Multimodal Large Reasoning Models (MLRMs), yet its mechanisms remain insufficiently understood. To analyze this stage, we introduce the Visual Attention Score (VAS), an attention-based metric that quantifies how much a model attend…
The transition from image to video understanding requires vision-language models (VLMs) to shift from recognizing static patterns to reasoning over temporal dynamics such as motion trajectories, speed changes, and state transitions. Yet current post-training methods fall short due to two critical li
Large Multimodal Models (LMMs) have demonstrated impressive performance in recognizing document images with natural language instructions. However, it remains unclear to what extent capabilities in literacy with rich structure and fine-grained visual challenges. The current landscape lacks a compreh…
In this letter, we present a deep reinforcement learning-based dimension-configurable local planner (DRL-DCLP) for solving robot navigation problems. DRL-DCLP is the first neural-network local planner capable of handling rectangular differential-drive robots with varying dimension configurations wit
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in document understanding. However, their reasoning processes remain largely black-box, making it difficult to ensure reliability and trustworthiness, especially in high-stakes domains such as legal, financial, and me…
This work focuses on enhancing the generalization performance of deep reinforcement learning-based robot navigation in unseen environments. We present a novel data augmentation approach called scenario augmentation, which enables robots to navigate effectively across diverse settings without alterin
Deep Reinforcement Learning (DRL) based navigation methods have demonstrated promising results for mobile robots, but suffer from limited action flexibility in confined spaces. Conventional DRL approaches predominantly learn forward-motion policies, causing robots to become trapped in complex enviro
Sea surface temperature (SST) plays an important role in our Earth’s atmosphere, wielding significant influence over both local and global climates and profoundly impacting ecosystems. However, this task presents unique challenges due to the inherent complexity and uncertainty within ocean systems.…
"For computer systems to effectively interact with humans using spoken language, they need to understand how the words being generated affect the users’ moment-by-moment attention. Our study focuses on the incremental prediction of attention as a person is seeing an image and hearing a referring exp…
Recently visually-situated text parsing (VsTP) has experienced notable advancements driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to…
Most models of visual attention aim at predicting either top-down or bottom-up control as studied using different visual search and free-viewing tasks. In this paper we propose the Human Attention Transformer (HAT) a single model that predicts both forms of attention control. HAT uses a novel transf…
Predicting human gaze is important in Human-Computer Interaction (HCI). However, to practically serve HCI applications, gaze prediction models must be scalable, fast, and accurate in their spatial and temporal gaze predictions. Recent scanpath prediction models focus on goal-directed attention (sear…
Recently, Visual Information Extraction (VIE) has been becoming increasingly important in both academia and industry, due to the wide range of real-world applications. Previously, numerous works have been proposed to tackle this problem. However, the benchmarks used to assess these methods are relat…
This paper addresses the problem of document image dewarping, which aims at eliminating the geometric distortion in document images for document digitization. Instead of designing a better neural network to approximate the optical flow fields between the inputs and outputs, we pursue the best readab…
"The prediction of human gaze behavior is important for building human-computer interactive systems that can anticipate a user’s attention. Computer vision models have been developed to predict the fixations made by people as they search for target objects. But what about when the image has no targe…
Recently, vision-language joint representation learning has proven to be highly effective in various scenarios. In this paper, we specifically adapt vision-language joint learning for scene text detection, a task that intrinsically involves cross-modal interaction between the two modalities: vision…
Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios. However, they might still fall short when handling text instances of extreme aspect ratios and varying scales. To tackle such difficult…
This paper tackles the problem of table structure pars-ing (TSP) from images in the wild. In contrast to existingstudies that mainly focus on parsing well-aligned tabularimages with simple layouts from scanned PDF documents,we aim to establish a practical table structure parsing sys-tem for real-wor…
Scene text spotting aims to detect and recognize the entire word or sentence with multiple characters in natural images. It is still challenging because ambiguity often occurs when the spacing between characters is large or the characters are evenly spread in multiple rows and columns, making many v…
Human gaze behavior prediction is important for behavioral vision and for computer vision applications. Most models mainly focus on predicting free-viewing behavior using saliency maps, but do not generalize to goal-directed behavior, such as when a person searches for a visual target object. We pro…