12 accepted papers
Large Language Models (LLMs) powerful generative capabilities also pose significant risks, underscoring the need for effective detoxification methods to ensure safer deployment. Due to the polysemantic nature of LLM neurons, recent neuron intervention methods inevitably entangle unrelated concepts,
Crowdsourcing is a common approach for training data-hungry models by collecting high-quality labeled data with human labor. With crowdsourcing data, the end-to-end learning paradigm is rising, where the classifier is concatenated with annotator-specific confusion layers and the two parts are co-tra
In the data-driven era, collecting high-quality labeled data requiring human labor is a common approach for training data-hungry models, called crowdsourcing. Recently, end-to-end learning from crowds has shown its flexibility and practicality. However, existing works in an end-to-end manner focus o
Generalizable surface reconstruction aims to recover the surface the scene from a sparse set of images in a feed-forward manner. Existing volume rendering-based methods evaluate numerous points along camera rays to infer the geometry, resulting in inefficient reconstruction. Recently, 3D Gaussian Sp…
Referring Image Segmentation (RIS), aims to segment the object referred by a given sentence in an image by understanding both visual and linguistic information. However, existing RIS methods tend to explore top-performance models, disregarding considerations for practical applications on resources-l
Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article. Recent methods achieve considerable performance but still suffer from two challenges: a) the relational entity pairs are sparse, b) the representation of entity pairs is insuffic…
Using machine learning to solve combinatorial optimization (CO) problems is challenging, especially when the data is unlabeled. This work proposes an unsupervised learning framework for CO problems. Our framework follows the standard relaxation-plus-rounding approach and adopts neural networks to pa…
Document-level event extraction (DEE) is indispensable when events are described throughout a document. We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document. It is a challenging tas…
Recently, deep learning based point cloud descriptors have achieved impressive results in the place recognition task. Nonetheless, due to the sparsity of point clouds, how to extract discriminative local features of point clouds to efficiently form a global descriptor is still a challenging problem.…
In this work, we propose a novel paradigm to design a hybrid thermal/EO (Electro-Optical or visible-light) camera, whose thermal and RGB frames are pixel-wisely aligned and temporally synchronized. Compared with the existing schemes, we innovate in three ways in order to make it more compact in dime…
The following message is relayed from an update made on IEEE Xplore. Notice of Violation of IEEE Publication Principles "Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution" by Hang Yang, Ming Zhu, Yan Niu, Yujing Guan, and Zhongbo Zhang in the Proceedings of t…
This paper presents a novel 3D modeling framework that reconstructs an indoor scene as a structured model from panorama RGBD images. A scene geometry is represented as a graph, where nodes correspond to structural elements such as rooms, walls, and objects. The approach devises a structure grammar t…