37 accepted papers
In agent theory, epistemic trust is used to infer beliefs, for example by filtering out the information the agent receives from untrustworthy agents. Moreover, trust itself can be inferred from other information. We introduce a simple information filtering architecture that clearly distinguishes the
Large language models (LLMs) generate increasingly human-like text, raising concerns about misinformation and authenticity. Detecting AI-generated text remains challenging: existing methods often underperform, especially on short texts, require probability access unavailable in real-world black-box…
Human cognition is compositional, and one can parse a visual scene into independent concepts and the corresponding concept-changing rules. By contrast, many vision-language systems process images holistically, with limited support for explicit decomposition. And previous methods of decomposing conce…
Cloth-changing person re-identification (CC-ReID) aims to identify individuals across non-overlapping cameras despite clothing variations. Existing methods are often constrained by two primary limitations: approaches using auxiliary modalities typically rely on a single specific cue, limiting their
Large Vision-Language Models (LVLMs) often hallucinate when visual evidence conflicts with world knowledge, i.e., in counterfactual scenarios. We propose Envision-Attend-Respond (EnAR), a training-free framework that leverages visual priors to steer the model's attention toward counterfactual elemen
Control in high-dimensional action spaces remains a fundamental challenge in reinforcement learning (RL), primarily due to inefficient exploration of the action space. While recent methods attempt to guide exploration, they often fall short of achieving the agility and coordination exhibited in biol
Place recognition (PR) is a key component of simultaneous localization and mapping (SLAM) in autonomous vehicles and robotics. By efficiently matching descriptors generated from the current scene with a prebuilt reference database, existing PR methods enable accurate vehicle re-localization. However
Recent advances in Speech Large Language Models (Speech LLMs) have paved the way for unified architectures across diverse speech understanding tasks. However, prevailing alignment paradigms rely heavily on large-scale audio-text paired data and computationally intensive training, yet often exhibit l…
LLM-based agents are becoming increasingly capable, yet their safety lags behind. This creates a gap between what agents can do and should do. This gap widens as agents engage in multi-turn interactions and employ diverse tools, introducing new risks overlooked by existing benchmarks. To systematica…
We tackle the task of long-form music generation, particularly the challenging \textbf{lyrics-to-song} problem, by introducing \textbf{YuE (乐)}, a family of open-source music generation foundation models. Specifically, YuE scales to trillions of tokens and generates up to five minutes of music while…
Robotic graspers are essential for enhancing the efficiency and versatility of robots in grasping tasks. In this paper, we propose a novel inflatable deployable origami grasper with a rigid-flexible coupling structure. The proposed grasper can achieve multiple deployment configurations under a singl
Audio-Visual Active Speaker Detection(ASD) is the task of identifying, at any given moment, who is actively speaking in a multi-person scene by using audio and visual cues. Current main stream ASD methods separately encode audio and facial features, then adopt post-feature fusion approach where the…
Existing video captioning methods merely provide shallow or simplistic representations of object behaviors, resulting in superficial and ambiguous descriptions. However, object behavior is dynamic and complex. To comprehensively capture the essence of object behavior, we propose a dynamic action sem…
Speech restoration aims at restoring full-band speech with high quality and intelligibility, considering a diverse set of distortions. MaskSR is a recently proposed generative model for this task. As other models of its kind, MaskSR attains high quality but, as we show, intelligibility can be substa…
Language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text data, which are labor-intensive. We introduce a parallel-data-free training scheme,…
Low-light scenarios pose a challenge for object detection due to noise and low brightness. The degraded feature maps are the main factors affecting detection performance. To improve the quality of low-light feature maps, we propose double adaptive filters (DAF) and dark feature pyramid network (DFPN…
In recent years, there has been a growing interest in designing small-footprint yet effective Connectionist Temporal Classification based keyword spotting (CTC-KWS) systems. They are typically deployed on low-resource computing platforms, where limitations on model size and computational capacity cr…
Federated unlearning (FU) aims to remove a participant's data contributions from a trained federated learning (FL) model, ensuring privacy and regulatory compliance. Traditional FU methods often depend on auxiliary storage on either the client or server side or require direct access to the data targ…
Zero-shot stance detection (ZSSD) is a challenging task that requires classifying stances towards unseen targets without large, well-curated training datasets. Existing data augmentation methods for ZSSD often suffer from semantic inconsistencies, hindering their effectiveness. To address these limi…
"Understanding the content of events occurring in the video and their inherent temporal logic is crucial for video-text retrieval. However, web-crawled pre-training datasets often lack sufficient event information, and the widely adopted video-level cross-modal contrastive learning also struggles to…
This paper introduces the HumTrans dataset, which is publicly available and primarily designed for humming melody transcription. The dataset can also serve as a foundation for downstream tasks such as humming melody based music generation. It consists of 500 musical compositions of different genres…
Any-to-any singing voice conversion (SVC) is confronted with the challenge of "timbre leakage" issue caused by inadequate disentanglement between the content and the speaker timbre. To address this issue, this study introduces NeuCoSVC, a novel neural concatenative SVC framework. It consists of a se…
Probabilistic linear discriminant analysis (PLDA) is commonly used in speaker verification systems to score the similarity of speaker embeddings. Recent studies improved the performance of PLDA in domain-matched conditions by diagonalizing its covariance. We suspect such a brutal pruning approach co…
The single-speaker singing voice synthesis (SVS) usually underperforms at pitch values that are out of the singer's vocal range or associated with limited training samples. Based on our previous work, this work proposes a melody-unsupervised multi-speaker pretraining method conducted on a multi-sing…
Single channel target speaker separation (TSS) aims at extracting a speaker’s voice from a mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep learning TSS framework consists of an upstream model that obtains enrollment speaker embeddings and a downstream model…
A leaderboard named Speech processing Universal PERformance Benchmark (SUPERB), which aims at benchmarking the performance of a shared self-supervised learning (SSL) speech model across various downstream speech tasks with minimal modification of architectures and a small amount of data, has fueled…
Automatic speaker verification (ASV) is one of the core technologies in biometric identification. With the ubiquitous usage of ASV systems in safety-critical applications, more and more malicious attackers attempt to launch adversarial attacks at ASV systems. In the midst of the arms race between at…
In this paper, the configuration transformation of Wheel-Legged Robot (WLR) is studied, which can enable the robot to change its multilinks configuration on Inverted Equilibrium Manifold (IEM), while keeping balance with a small location drift on the floor. First of all, the general form of dynamics…
Existing approaches for replay and synthetic speech detection still lack generalizability to unseen spoofing attacks. This work proposes to leverage a novel model structure, so-called Res2Net, to improve the anti-spoofing countermeasure’s generalizability. Res2Net mainly modifies the ResNet block to…
This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based speaker verification systems to adversarial attacks, and the transferability of adversarial samples crafted from GMM i-vector based systems to x-vector based systems. In detail, we formulate the GMM i-vector syst…
State-of-the-art text-to-speech (TTS) synthesis models can produce monolingual speech with high intelligibility and naturalness. However, when the models are applied to synthesize code-switched (CS) speech, the performance declines seriously. Conventionally, developing a CS TTS system requires multi…
Speech emotion recognition (SER) is a fundamental step towards fluent human-machine interaction. One challenging problem in SER is obtaining utterance-level feature representation for classification. Recent works on SER have made significant progress by using spectrogram features and introducing neu…
The performance of traditional hydraulic robots is often limited by their hoses across moving joints or connecting hydraulic drive units, which would reduce their mobility and impede their ability to operate in complex environment. In response to this deficiency, this paper introduces the WLR-II (th…
For mispronunciation detection and diagnosis (MDD), nowadays approaches generally treat the phonemes in correct and mispronunciations as the same despite the fact they may actually carry different characteristics. Furthermore, serious data imbalance issue between correct and mispronunciation in data…
Wheel-legged hybrid robot with multi-modal locomotion can efficiently adapt to different terrain environments, as well as realize rapid maneuver on flat ground. We have developed a novel hydraulic wheel-legged robot (WLR) combined with a humanoid structural design. This robot can assist to emergency…
Second language (L2) speech is often labelled with the native, phoneme categories. Hence, we often observe segments for which it is difficult, if not impossible, to decide on a categorical phoneme label. We refer to these segments as “non-categorical” phoneme units. Existing approaches to mispronunc…
Speech is bimodal in nature. There are close correlations between the acoustic speech signals and the visual gestures such as lip movements, facial expressions and head motions. For speech driven talking avatar, how to derive more representative acoustic features from which to predict more accurate…