27 accepted papers
Embodied 3D object detection is a fundamental perception capability for embodied agents, where observations are partial, heavily occluded, and sequential, requiring modeling of temporal continuity. However, existing benchmarks and methods are primarily designed for fully reconstructed global scenes …
Simultaneous Localization and Mapping (SLAM) is increasingly expected to provide reusable spatial representations for downstream perception. However, existing approaches often struggle with scale-consistency and producing maps that lack the geometric fidelity required for reliable perception. We pro…
Chest X-rays (CXRs) are the most frequently performed imaging examinations in clinical settings. Recent advancements in Medical Large Multimodal Models (MLMMs) have enabled automated CXR interpretation, improving diagnostic accuracy and efficiency. However, despite their strong visual understanding,…
As LLM-as-a-Judge emerges as a new paradigm for assessing large language models (LLMs), concerns have been raised regarding the alignment, bias, and stability of LLM evaluators. While substantial work has focused on alignment and bias, little research has concentrated on the stability of LLM evaluat…
Generating videos in the first-person perspective has broad application prospects in the field of augmented reality and embodied intelligence. In this work, we explore the cross-view video prediction task, where given an exo-centric video, the first frame of the corresponding ego-centric video, and…
Role-playing agents (RPAs) powered by large language models (LLMs) have been widely utilized in dialogue systems for their capability to deliver personalized interactions. Current evaluations of RPAs mainly focus on personality fidelity, tone imitation, and knowledge consistency, while overlooking e…
Document-level relation extraction (DocRE) identifies relations between entities across an entire document. However, as the number and complexity of entities and entity-pair relations grow, the problem space expands quadratically, causing incomplete annotations and frequent false negatives, especial…
Large Language Models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This capability has attracted the attention of the social science community, who see the potential in leveraging LLMs to replace human participants in experiments, thereby reducing research costs…
Visual grounding involves the identification and localization of image regions given textual descriptions. To reduce the manual labeling effort on region-text pairs, unsupervised visual grounding aims to generate pseudo bounding box and query pairs for training grounding models. However, there exist…
Randomized Controlled Trials (RCTs) are rigorous clinical studies crucial for reliable decision-making, but their credibility can be compromised by bias. The Cochrane Risk of Bias tool (RoB 2) assesses this risk, yet manual assessments are time-consuming and labor-intensive. Previous approaches have…
Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion mo…
Multimodal fusion offers significant potential for enhancing medical diagnosis, particularly in the Intensive Care Unit (ICU), where integrating diverse data sources is crucial. Traditional static fusion models often fail to account for sample-wise variations in modality importance, which can impact…
Controllable captioning has received much attention in recent years. Although substantial progress has been made, existing methods still face challenges such as high training costs, intricate control signals and limited control capabilities. To address these issues, we propose a straightforward and…
Point clouds have shown significant potential in various domains, including Simultaneous Localization and Mapping (SLAM). However, existing approaches either rely on dense point clouds to achieve high localization accuracy or use generalized descriptors to reduce map size. Unfortunately, these two a…
Video paragraph captioning task aims to generate a detailed, fluent and relevant paragraph for a given video. Prior studies often focus on isolating visual objects (potential main components in a sentence) from the overall video content. They rarely explore the latent semantic relations between obje…
Generative Adversarial Networks (GANs) are often used in face sketch synthesis due to their powerful ability in image generation. However, most GAN based synthesis methods took the entire face as the minimum unit. Differently, we propose a novel fine-granularity face sketch synthesis framework in th…
Understanding human actions from videos of first-person view poses significant challenges. Most prior approaches explore representation learning on egocentric videos only while overlooking the potential benefit of exploiting existing large-scale third-person videos. In this paper (1) we develop EgoI…
This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features. In contrast to prior works which utilize object tags either manually labeled or automatically detected with a…
Fine-grained sketch-based image retrieval (FG-SBIR) aims at aligning images and sketches at the instance level. It is a challenging task as there are significant differences between sketch and image. Existing methods usually produce less desired performance due to the lack of large-scale fine-graine…
With the Generative Pre-trained Transformer 3.5 (GPT-3.5) exhibiting remarkable reasoning and comprehension abilities in Natural Language Processing (NLP), most Question Answering (QA) research has primarily centered around general QA tasks based on GPT, neglecting the specific challenges posed by C…
In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS), which aims to segment objects of arbitrary classes instead of pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor,…
Video paragraph captioning task aims at generating a fine-grained, coherent and relevant paragraph for a video. Different from the images where objects are static, the temporal states of objects are changing in videos. The dynamic information could be contributed to understanding the whole video con…
Automatic and accurate breast mass segmentation plays a crucial role in the early diagnosis of breast cancer. However, it has been a challenging task for two main reasons: (1) Breast masses are diverse; and (2) The boundaries of masses are ambiguous. To address these problems, we propose a Spatial-C…
Generating paragraph captions for untrimmed videos without event annotations is challenging, especially when aiming to enhance precision and minimize repetition at the same time. To address this challenge, we propose a module called Sparse Frame Grouping (SFG). It dynamically groups event informatio…
Recent neural models for video captioning usually employed an encoder-decoder framework. However, most approaches either neglected the spatial and temporal interactions between objects in a video or implicitly modelled the interactions, resulting in less desired performance. In this paper, we propos…
Weakly Supervised Object Localization (WSOL) aims to localize objects with image-level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived from a classification model. However, CAM-based methods usually focus on the most discriminative parts of an object (i.e., incomple…
Automatic video polyp segmentation (VPS) is highly valued for the early diagnosis of colorectal cancer. However, existing methods are limited in three respects: 1) most of them work on static images, while ignoring the temporal information in consecutive video frames; 2) all of them are fully superv…