16 accepted papers
Existing 3D Visual Question Answering (3D-VQA) methods rely on generative paradigms, producing ambiguous descriptions that hinder deterministic decision-making. We introduce 3D Scene Assertion Verification, a task requiring models to verify natural language assertions in 3D scenes with strict binary…
Auto-regressive (AR) models, initially successful in language generation, have recently shown promise in visual generation tasks due to their superior sampling efficiency. Unlike image generation, video generation requires a substantially larger number of tokens to produce coherent temporal frames,…
Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text. Each quintuple comprises five elements: subject, object, aspect, opinion and preference. With the rise of Large Language Models (LLMs), existing work primarily focuses on en…
Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs. But it still requires professional knowledge to facilitate the expertise for some domain-specific tasks. In this paper, we investigate into knowledge-intensive calculation problems. We find that the ma…
Monocular Depth Estimation (MDE) has emerged as a pivotal task in computer vision, supporting numerous real-world applications. However, deploying accurate depth estimation models on resource-limited edge devices, especially Application-Specific Integrated Circuits (ASICs), is challenging due to the…
Precise and robust extrinsic parameter calibration is fundamental for LiDAR-camera multi-modal sensing applications. However, most existing methods assume that sensors have the same orientation, limiting their effectiveness in feature extraction and feature alignment from different angle of view in
Large Language Models (LLMs) could struggle to fully understand legal theories and perform complex legal reasoning tasks. In this study, we introduce a challenging task (confusing charge prediction) to better evaluate LLMs’ understanding of legal theories and reasoning capabilities. We also propose…
Visual Relation Extraction (VRE) is a powerful means of discovering relationships between entities within visually-rich documents. Existing methods often focus on manipulating entity features to find pairwise relations, yet neglect the more fundamental structural information that links disparate ent…
Prompt tuning is a parameter-efficient method, which learns soft prompts and conditions frozen language models to perform specific downstream tasks. Though effective, prompt tuning under few-shot settings on the one hand heavily relies on a good initialization of soft prompts. On the other hand, it…
In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics. In this work, we focus on topic-oriented dialogue summarization, which generates…
Automatic chat summarization can help people quickly grasp important information from numerous chat messages. Unlike conventional documents, chat logs usually have fragmented and evolving topics. In addition, these logs contain a quantity of elliptical and interrogative sentences, which make the cha…
Low-latency and low-power implementations of Convolutional Neural Network (CNN) are highly desired for budget-restricted scenarios. Pruning and Winograd algorithm are two representative approaches to reduce the computation complexity of CNNs. Coupling them is very attractive, but the Winograd transf…
Polar code is increasing its popularity recently for its capacity-achieving property for B-DMCs. However, when designing decoders for polar code, it has always been an inevitable concern for us to balance the decoding performance and the hardware consumption. In this paper, we propose an approximate…
Convolutional neural networks (CNNs) are widely used in various intelligent tasks. However, the huge computational complexity of CNNs makes it hard to be implemented in many real-time embedded devices. Various methods have been employed to reduce the model size of CNNs, where the Canonical Polyadic…
Polar codes are the first provably capacity-achieving forward error correction codes. To improve decoder throughput, the symbol-decision SC algorithm makes hard-decision for multiple bits at a time. In this paper, we prove that for polar codes, the symbol-decision SC algorithm is better than the bit…
Although the successive cancelation (SC) algorithm works well for very long polar codes, its error performance for shorter polar codes is much worse. Several SC based list decoding algorithms have been proposed to improve the error performances of both long and short polar codes. A significant step…