13 accepted papers
This paper investigates unsupervised multimodal domain adaptation for multimodal emotion recognition, which is a solution for data scarcity yet remains under studied. Due to the varying distribution discrepancies of different modalities between source and target domains, the primary challenge lies i…
As an indispensable ingredient of intelligence, commonsense reasoning is crucial for large language models (LLMs) in real-world scenarios. In this paper, we propose CORECODE, a dataset that contains abundant commonsense knowledge manually annotated on dyadic dialogues, to evaluate the commonsense re…
Emotion Recognition in conversations (ERC) involves an internal cognitive process that interprets emotional cues by using a collection of past emotional experiences. However, many existing methods struggle to decipher emotional cues in dialogues since they are insufficient in understanding the rich…
Multimodal emotion recognition (MER) is important for building humanoid chatbots and has gained increasing attention in recent years. Existing studies have proven that extracting better modality-specific representations, which keep both commonality and individuality information of different modaliti…
Graph-based multi-armed bandit algorithms utilize the relationship between users to select the best item to recommend for maximal reward, which is decided by items’ features and un-known users’ preferences. Therefore, the precise estimation of users’ preferences is fairly important and indispensable…
Humans can effortlessly modify various prosodic attributes, such as the placement of stress and the intensity of sentiment, to convey a specific emotion while maintaining consistent linguistic content. Motivated by this capability, we propose EmoAug, a novel style transfer model designed to enhance…
In this paper, we propose a least-effort adversarial attack against a gait-based identity recognition system (GIRS). Specifically, we leverage a bilevel optimization framework to characterize this leader-follower Stackelberg game between the attacker and gait recognizer to pursue the Nash equilibriu…
Multi-modal emotion recognition (MER) using speech and text has attracted extensive attention because of the easy availability of data for these two modalities. Recently, the self-surprised learning (SSL) pre-trained model has become the state-of-the-art (SOTA) method for the extraction of acoustic…
Multimodal learning is susceptible to modality missing, which poses a major obstacle for its practical applications and, thus, invigorates increasing research interest. In this paper, we investigate two challenging problems: 1) when modality missing exists in the training data, how to exploit the in…
Multi-modal emotion recognition has gained increasing attention in recent years due to its widespread applications and the advances in multi-modal learning approaches. However, previous studies primarily focus on developing models that exploit the unification of multiple modalities. In this paper, w…
Sampling of bandlimited (BL) graph signals is one fundamental problem in graph signal processing (GSP), whose underlying kernel is an irregular graph rather than regular 1-D time-series kernel in classical discrete signal processing (DSP). Though there were amounts of sampling objectives and algorit…
This paper proposes a new model, called hierarchical and multi-view dependency modelling network (HMVDM), for the task of emotion recognition in conversations (ERC). The modelling of conversational context plays an important role in ERC, especially for the multi-turn and multi-speaker conversations…
In this paper, we study an untouched problem in visible-infrared person re-identification (VI-ReID), namely, Twin Noise Labels (TNL) which refers to as noisy annotation and correspondence. In brief, on the one hand, it is inevitable to annotate some persons with the wrong identity due to the complex…