Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Praveen, R Gnana"'
Autor:
Praveen, R. Gnana, Alam, Jahangir
In video-based emotion recognition, audio and visual modalities are often expected to have a complementary relationship, which is widely explored using cross-attention. However, they may also exhibit weak complementary relationships, resulting in poo
Externí odkaz:
http://arxiv.org/abs/2403.19554
Autor:
Praveen, R. Gnana, Alam, Jahangir
Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (R
Externí odkaz:
http://arxiv.org/abs/2403.13659
Autor:
Praveen, R. Gnana, Alam, Jahangir
Although person or identity verification has been predominantly explored using individual modalities such as face and voice, audio-visual fusion has recently shown immense potential to outperform unimodal approaches. Audio and visual modalities are o
Externí odkaz:
http://arxiv.org/abs/2403.04661
Autor:
Praveen, R. Gnana, Alam, Jahangir
Person or identity verification has been recently gaining a lot of attention using audio-visual fusion as faces and voices share close associations with each other. Conventional approaches based on audio-visual fusion rely on score-level or early fea
Externí odkaz:
http://arxiv.org/abs/2403.04654
Autor:
Praveen, R. Gnana, Alam, Jahangir
Speaker verification has been widely explored using speech signals, which has shown significant improvement using deep models. Recently, there has been a surge in exploring faces and voices as they can offer more complementary and comprehensive infor
Externí odkaz:
http://arxiv.org/abs/2309.16569
In video-based emotion recognition (ER), it is important to effectively leverage the complementary relationship among audio (A) and visual (V) modalities, while retaining the intra-modal characteristics of individual modalities. In this paper, a recu
Externí odkaz:
http://arxiv.org/abs/2304.07958
Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention
Automatic emotion recognition (ER) has recently gained lot of interest due to its potential in many real-world applications. In this context, multimodal approaches have been shown to improve performance (over unimodal approaches) by combining diverse
Externí odkaz:
http://arxiv.org/abs/2209.09068
Autor:
Praveen, R. Gnana, de Melo, Wheidima Carneiro, Ullah, Nasib, Aslam, Haseeb, Zeeshan, Osama, Denorme, Théo, Pedersoli, Marco, Koerich, Alessandro, Bacon, Simon, Cardinal, Patrick, Granger, Eric
Multimodal emotion recognition has recently gained much attention since it can leverage diverse and complementary relationships over multiple modalities (e.g., audio, visual, biosignals, etc.), and can provide some robustness to noisy modalities. Mos
Externí odkaz:
http://arxiv.org/abs/2203.14779
Multimodal analysis has recently drawn much interest in affective computing, since it can improve the overall accuracy of emotion recognition over isolated uni-modal approaches. The most effective techniques for multimodal emotion recognition efficie
Externí odkaz:
http://arxiv.org/abs/2111.05222
Autor:
Kiran, Madhu, Praveen, R Gnana, Nguyen-Meidine, Le Thanh, Belharbi, Soufiane, Blais-Morin, Louis-Antoine, Granger, Eric
In real-world video surveillance applications, person re-identification (ReID) suffers from the effects of occlusions and detection errors. Despite recent advances, occlusions continue to corrupt the features extracted by state-of-art CNN backbones,
Externí odkaz:
http://arxiv.org/abs/2104.06524