61 accepted papers
User satisfaction is closely related to enterprises, as it not only directly reflects users' subjective evaluation of service quality or products, but also affects customer loyalty and long-term business revenue. Monitoring and understanding user emotions during interactions helps predict and improv…
Vision–Language–Action (VLA) models have shown strong performance in robotic manipulation, but often struggle in long-horizon or out-of-distribution scenarios due to the lack of explicit mechanisms for multimodal reasoning and anticipating how the world will evolve under action. Recent works introdu…
As Large Language Models (LLMs) integrate into writing workflows, precise governance requires distinguishing ''how AI participated'' rather than merely ''whether AI was used.'' Traditional binary detection often misclassifies ``AI-polished'' content as generated, creating fairness risks. We propose …
Recent advances in large language models (LLMs) have spurred interest in using these models to coordinate multi-agent robot systems. However, existing approaches often fail to handle dynamic and complex environments effectively. We present A2C-Collab, an <underline xmlns:mml="http://www.w3.org/1998/
Spoken Language Understanding (SLU) consists of two sub-tasks: intent detection (ID) and slot filling (SF). Given its broad range of real-world applications, enhancing SLU for practical deployment is increasingly critical. Profile-based SLU addresses ambiguous user utterances by incorporating contex
The widespread adoption of Low-Rank Adaptation (LoRA) for efficient fine-tuning of large language models has created demand for scalable parameter generation methods that can synthesize adaptation weights directly from task descriptions, avoiding costly task-specific training. We present LoRAGen, a…
Glass surface ubiquitous in both daily life and professional environments presents a potential threat to vision-based systems, such as robot and drone navigation. To solve this challenge, most recent studies have shown significant interest in Video Glass Surface Detection (VGSD). We observe that obj
We present Mixture-of-Length (MoL), an approach for Question Answering (QA) with context that aims to improve the balance between reasoning quality and response efficiency. Our method introduces a principled difficulty assessment based on information-theoretic principles and a dual-objective reward…
The prefill stage of long-context Retrieval-Augmented Generation (RAG) is severely bottlenecked by computational overhead. To mitigate this, recent methods assemble pre-calculated KV caches of retrieved RAG documents (by a *user query*) and reprocess selected tokens to recover cross-attention betwee…
Large Language Models (LLM) have gained significant attention. KV cache, stored to avoid quadratic complexity of attention, becomes a bottleneck due to the demands for long-context. Sparse attention (SA) has been proposed to address this by only selecting critical KVs for attention, which ma…
Collaborative perception enables connected vehicles to share information, overcoming occlusions and extending the limited sensing range inherent in single-agent (non-collaborative) systems. Existing vision-only methods for 3D semantic occupancy prediction commonly rely on dense 3D voxels, which incu
Unsupervised graph anomaly detection aims to identify nodes that deviate from typical behaviors in graphs. Existing approaches can be briefly categorized into two main groups, namely, reconstruction-based approaches that detect anomalies through reconstruction errors, and contrastive learning-based…
Singing melody extraction from polyphonic music is a complex but important task in music information retrieval. Harmonic relationships have been shown to be crucial in this task, but most existing models based on Convolutional Neural Networks (CNNs) struggle to capture long-range harmonic dependenci…
Multilingual spoken language understanding (SLU) involves intent detection (ID) and slot filling (SF) across multiple languages. The inherent linguistic diversity presents significant challenges in achieving performance comparable to traditional SLU. Recent studies have attempted to improve multilin…
Speaker verification performance significantly degrades when there exists a language mismatch between training and evaluation. Domain Adversarial Training (DAT) has shown to be effective in mitigating this gap by incorporating adversarial training with domain information (language id). Inspired by r…
Soft robots, compared to regular rigid robots, as their multiple segments with soft materials bring flexibility and compliance, have the advantages of safe interaction and dexterous operation in the environment. However, due to its characteristics of high dimensional, nonlinearity, time-varying natu
Open-vocabulary Video Instance Segmentation (OpenVIS) can simultaneously detect, segment, and track arbitrary object categories in a video, without being constrained to categories seen during training. In this work, we propose InstFormer, a carefully designed framework for the OpenVIS task that achi…
Significant advancements have been made in text-based speech editing (TSE) for clear speech, but effectively editing the noise-contaminated speech remains a challenge. Background noise degrades the quality of generated speech, and edited speech that fails to maintain noise context consistency often…
Robotic navigation plays a pivotal role in a wide range of real-world applications. While traditional navigation systems focus on efficiency and obstacle avoidance, their inability to model complex human behaviors in shared spaces has underscored the growing need for socially aware navigation. In th
Empathy detection (ED) and empathy intent classification (EIC) aim to identify the empathy direction expressed in user utterances and the underlying empathy intent behind them. Previous studies show that facilitating information transfer between tasks can enhance model performance. However, the inte…
Policy learning focuses on devising strategies for agents in embodied artificial intelligence systems to perform optimal actions based on their perceived states. One of the key challenges in policy learning involves handling complex, long-horizon tasks that require managing extensive sequences of ac…
Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models. As training data plays an increasingly crucial role in the modern development of large-scale AI models, data attribution has found broad applications in imp…
Instruction following is one of the fundamental capabilities of large language models (LLMs). As the ability of LLMs is constantly improving, they have been increasingly applied to deal with complex human instructions in real-world scenarios. Therefore, how to evaluate the ability of complex instruc…
Spatial cognition refers to the ability to gain knowledge about their surroundings and utilize this information to identify their location, acquire resources, and navigate their way back to familiar places. People with blindness and low vision (pBLV) face significant challenges with spatial cognitio…
Large-scale pre-trained neural language model has facilitated to achieve the state-of-the-art performance on Dialogue State Tracking (DST) tasks. One of the existing works models the semantic correlation between the dialogue context and (domain, slot) pair encoded by BERT and make the prediction. De…
Multi-intent spoken language understanding consists of two typical subtasks: multi-intent detection and slot filling. Existing approach suffers from two limitations: (1) It fails to explicitly model the information transfer between slots associated within the same intent clause; (2) Using a co-occur…
Efficiently processing time series data streams in real-time on resource-constrained devices offers significant advantages in terms of enhanced computational energy efficiency and reduced time-related risks. We introduce an innovative streaming time series classification network that utilizes attent…
Dialogue state tracking (DST) is a crucial component of task-oriented dialogue systems, as it aims to accurately track the user’s goals throughout the dialogue history. However, DST models struggle with new domains due to limited annotated data, leading to poor performance. To solve this key challen…
Fairness is a critical concern in deep learning especially in healthcare where these models influence diagnoses and treatment decisions. Although fairness has been investigated in the vision-only domain the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of med…
"Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have introduced new methodologies and datasets in medical AI. However, the fairness issue under the setting of domain transfer i…
Zero-Shot Object Goal Navigation (ZS-OGN) enables robots to navigate toward objects of unseen categories without prior training. Traditional approaches often leverage categorical semantic information for navigation guidance, which struggles when only partial objects are observed or detailed and func…
"Video Anomaly Detection (VAD) has been extensively studied under the settings of One-Class Classification (OCC) and Weakly-Supervised learning (WS), which however both require laborious human-annotated normal/abnormal labels. In this paper, we study Unsupervised VAD (UVAD) that does not depend on a…
Incorporating frame-level phonetic information during the extraction of speaker embeddings has been shown to enhance the performance of speaker verification systems. However, previous studies have primarily relied on phonetic information obtained from pre-trained models of monolingual automatic spee…
To infer a diffusion network based on observations from historical diffusion processes, existing approaches assume that observation data contain exact occurrence time of each node infection, or at least the eventual infection statuses of nodes in each diffusion process. They determine potential infl…
3D scene semantic segmentation plays a crucial role in robotics by enabling robots to understand and interpret their environment in a detailed and context-aware manner, facilitating tasks such as navigation, object manipulation, and interaction within complex spaces. A preponderance of methodology p…
Deep neural network (DNN) applications have significantly progressed in voice activity detection (VAD). Most current DNN-based VAD methods ignore the rich audio information in the phase domain. Therefore, applying this auxiliary information rationally and coping with low signal-to-noise ratio (SNR)…
Ranking aggregation (RA), the process of aggregating multiple rankings derived from multiple search strategies, has been proved effective in person re-identification (re-ID) because of a single re-ID method can not always achieve consistent superiority for different scenarios. Existing RA research m…
We propose a deep neural network with spectrogram matching and mutual attention (SMMA-Net) for audio clue-based target speaker extraction (TSE). To effectively use the auxiliary speech, we proposed spectrogram matching (SM) strategy and mutual attention (MA) block. We conducted all experiments on th…
Current methods for large-scale point cloud scene semantic segmentation rely on manually annotated dense point-wise labels, which are costly, labor-intensive, and prone to errors. Consequently, gathering point cloud scenes with billions of labeled points is impractical in real-world scenarios. In th…
Although Deep Neural Networks (DNNs) have demonstrated excellent performance, they are vulnerable to adversarial patches that introduce perceptible and localized perturbations to the input. Generating adversarial patches on images has received much attention, while adversarial patches on videos have…
Low-resource speech recognition has been long-suffering from insufficient training data. In this paper, we propose an approach that leverages neighboring languages to improve low-resource scenario performance, founded on the hypothesis that similar linguistic units in neighboring languages exhibit c…
Multilingual automatic speech recognition (ASR) models with phones as modeling units have have improved greatly in low-resource and similar-language scenarios, which benefits from shared representation across languages. Meanwhile, subwords have demonstrated their effectiveness for monolingual end-to…
Current supervised speech enhancement methods based on deep learning typically utilize amplitude-based loss functions for optimization, such as Mean Absolute Error (MAE) or Mean Square Error (MSE) loss, which measures the difference between the amplitudes of the estimated and clean speech signals. H…
3D LiDAR place recognition plays a vital role in various robot applications' including robotic navigation, autonomous driving, and simultaneous localization and mapping. However, most previous studies evaluated their models on accumulated 2D scans instead of real-world 3D LiDAR scans with a larger n…
Diffusion model, as a new generative model which is very popular in image generation and audio synthesis, is rarely used in speech enhancement. In this paper, we use the diffusion model as a module for stochastic refinement. We propose SRTNet, a novel method for speech enhancement via Stochastic Ref…
Dialogue state error correction has recently been proposed to correct wrong slot values in predicted dialogue states, thereby mitigating the error propagation problem for dialogue state tracking (DST). These approaches, though effective, are heavily intertwined with specific DST models, limiting the…
Existing speech separation models based on deep learning typically generalize poorly due to domain mismatch. In this paper, we propose SpeakerAugment (SA), a data augmentation method for generalizable speech separation that aims to increase the diversity of speaker identity in training data, to miti…
To let the state-of-the-art end-to-end ASR model enjoy data efficiency, as well as much more unpaired text data by multi-modal training, one needs to address two problems: 1) the synchronicity of feature sampling rates between speech and language (aka text data); 2) the homogeneity of the learned re…
We present a novel method for 3D shape representation learning using multi-scale wavelet decomposition. Previous works often decompose 3D shapes into complementary components in spatial domain at a single scale. In this work, we study to decompose 3D shapes into sub-bands components in frequency dom…
Malicious applications of deepfakes (i.e., technologies generating target facial attributes or entire faces from facial images) have posed a huge threat to individuals' reputation and security. To mitigate these threats, recent studies have proposed adversarial watermarks to combat deepfake models,…
Recently proposed dialogue state tracking (DST) approaches predict the dialogue state of a target turn sequentially based on the previous dialogue state. During the training time, the ground-truth previous dialogue state is utilized as the historical context. However, only the previously predicted d…
The performance of current Scene Graph Generation models is severely hampered by some hard-to-distinguish predicates, e.g., "woman-on/standing on/walking on-beach" or "woman-near/looking at/in front of-child". While general SGG models are prone to predict head predicates and existing re-balancing st…
"Deep neural networks (DNNs) for point clouds have achieved superior performance on a range of 3D vision tasks. However, generalization to out-of-distribution 3D point clouds remains challenging for DNNs. Due to the expensive cost or even infeasibility of annotating large-scale point clouds, it is c…
Non-autoregressive end-to-end ASR framework might be potentially appropriate for code-switching recognition task thanks to its inherent property that present output token being independent of historical ones. However, it still under-performs the state-of-the-art autoregressive ASR frameworks. In thi…
Speech separation dataset typically consists of hard and non-hard samples, and the former is minority and latter majority. The data imbalance problem biases the model towards non-hard samples and weakens the generalization capability. Given that the average separation performance is sufficiently goo…
To reconstruct the topology of a diffusion network, existing approaches customarily demand not only eventual infection statuses of nodes, but also the exact times when infections occur. In real-world settings, such as the spread of epidemics, tracing the exact infection times is often infeasible; ev…
This work studies temporal reading comprehension (TRC), which reads a free-text passage and answers temporal ordering questions. Precise question understanding is critical for temporal reading comprehension. For example, the question “What happened before the victory” and “What happened after the vi…
To infer the structure of a diffusion network from observed diffusion results, existing approaches customarily assume that observed data are complete and contain the final infection status of each node, as well as precise timestamps of node infections. Due to high cost and uncertainties in the monit…
We pursue an interpretable pitch tracking model and a jointly trained tone model for Mandarin tone classification. For pitch tracking, present deep learning based pitch model structure seldom considers the Viterbi decoding commonly implemented in prevalent manually designed pitch tracking algorithms…
Procedural text understanding aims at tracking the states (e.g., create, move, destroy) and locations of the entities mentioned in a given paragraph. To effectively track the states and locations, it is essential to capture the rich semantic relations between entities, actions, and locations in the…
Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of light-weight structure, high efficiency and great interpretability. Especially when extended to complex vector space, they show…