Krishna Somandepalli
krishna.ai
Authored Publications
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VideoPoet: A Large Language Model for Zero-Shot Video Generation
Dan Kondratyuk
Xiuye Gu
Jonathan Huang
Grant Schindler
Rachel Hornung
Vighnesh Birodkar
Jimmy Yan
Ming-Chang Chiu
Hassan Akbari
Josh Dillon
Agrim Gupta
Meera Hahn
Anja Hauth
David Hendon
Alonso Martinez
Kihyuk Sohn
Xuan Yang
Huisheng Wang
Lu Jiang
ICML (2024)
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We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs -- including images, videos, text, and audio. The training protocol follows that of Large Language Models (LLMs), consisting of two stages: pretraining and task-specific adaptation. During pretraining, VideoPoet incorporates a mixture of multimodal generative objectives within an autoregressive Transformer framework. The pretrained LLM serves as a foundation that can be adapted for a range of video generation tasks. We present empirical results demonstrating the model's state-of-the-art capabilities in zero-shot video generation, specifically highlighting VideoPoet's ability to generate high-fidelity motions. Project page: http://sites.research.google/videopoet/
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Relational Affect in Dyadic Interactions
CHI Conference on Human Factors in Computing Systems (2024)
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Relational affect is the affective response (encompassing emotion, expression, feeling) that emerges from an interaction between two people. The case study presented here introduces the concept of relational affect through a human perceptual rating task. Forty-five raters watched short video clips of two people interacting and described their perceived emotion of the individuals and that of the overall interaction. Our qualitative analysis of the rater responses showed that raters used a variety of schemes to reason about emotion, including expressions, context, and perceived appraisal of the event. These reasoning schemes were notably different for perceived individual emotion and relational affect. Our findings show that the vocabulary use for relational affect is distinct from that of individual emotion and relational affect as a phenomenon deepens our understanding of social interactions and moves the field a step closer to realizing the goal of fluid interactions between people and technology.
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A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation
Bradley Kim
Alonso Martinez
Yu-Chuan Su
Agrim Gupta
Lu Jiang
Jacob Walker
Neural Information Processing Systems (NeurIPS) (2024) (to appear)
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Training diffusion models for audiovisual sequences allows for a range of generation tasks by learning conditional distributions of various input-output combinations of the two modalities. Nevertheless, this strategy often requires training a separate model for each task which is expensive. Here, we propose a novel training approach to effectively learn arbitrary conditional distributions in the audiovisual space. Our key contribution lies in how we parameterize the diffusion timestep in the forward diffusion process. Instead of the standard fixed diffusion timestep, we propose applying variable diffusion timesteps across the temporal dimension and across modalities of the inputs. This formulation offers flexibility to introduce variable noise levels for various portions of the input, hence the term mixture of noise levels. We propose a transformer-based audiovisual latent diffusion model and show that it can be trained in a task-agnostic fashion using our approach to enable a variety of audiovisual generation tasks at inference time. Experiments demonstrate the versatility of our method in tackling cross-modal and multimodal interpolation tasks in the audiovisual space. Notably, our proposed approach surpasses baselines in generating temporally and perceptually consistent samples conditioned on the input.
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LanSER: Language-Model Supported Speech Emotion Recognition
Taesik Gong
Josh Belanich
Brian Eoff
INTERSPEECH (2023)
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Speech emotion recognition (SER) studies typically rely on costly motion-labeled speech for training, making scaling methods to to large speech datasets and nuanced emotion taxonomies difficult. We present LanSER that enables one to use those unlabeled data by generating weak emotion labels via pre-trained large language models, which are then used for weakly-supervised learning. For weak label generation, we utilize a textual entailment approach that selects an emotion label with the highest entailment score, given a transcript extracted from speech via automatic speech recognition. Our experimental results show that models pre-trained on large datasets with this weak supervision outperform other baseline models on standard SER datasets when fine-tuned, and exhibit much greater label efficiency. Despite being pre-trained on labels derived only from text, we show that the resulting representations appear to model the prosodic content of speech.
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Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers
Josh Belanich
Brian Eoff
ICML Expressive Vocalizations Workshop & Competition (2022)
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This technical report presents the modeling approaches used in our submission to the ICML Expressive Vocalizations Workshop & Competition multitask track (ExVo-MultiTask). We first applied image classification models of various sizes on mel-spectrogram representations of the vocal bursts, as is standard in sound event detection literature. Results from these models show an increase of 21.24% over the baseline system with respect to the harmonic mean of the task metrics, and comprise our team's main submission to the MultiTask track. We then sought to characterize the headroom in the MultiTask track by applying a large pre-trained Conformer model that previously achieved state-of-the-art results on paralinguistic tasks like speech emotion recognition and mask detection. We additionally investigated the relationship between the sub-tasks of emotional expression, country of origin, and age prediction, and discovered that the best performing models are trained as single-task models, questioning whether the problem truly benefits from a multitask setting.
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Federated Learning for Affective Computing Tasks
Brian Eoff
Alan Cowen
Josh Belanich
IEEE (2022), pp. 1-8
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Federated learning mitigates the need to store user data in a central datastore for machine learning tasks, and is particularly beneficial when working with sensitive user data or tasks. Although successfully used for applications such as improving keyboard query suggestions, it is not studied systematically for modeling affective computing tasks which are often laden with
subjective labels and high variability across individuals/raters or even by the same participant. In this paper, we study the federated averaging algorithm FedAvg to model self-reported
emotional experience and perception labels on a variety of speech, video and text datasets. We identify two learning paradigms that commonly arise in affective computing tasks: modeling of selfreports (user-as-client), and modeling perceptual judgments such as labeling sentiment of online comments (rater-as-client). In the user-as-client setting, we show that FedAvg generally performs on-par with a non-federated model in classifying self-reports. In the rater-as-client setting, FedAvg consistently performed poorer than its non-federated counterpart. We found that the performance of FedAvg degraded for classes where the interrater agreement was moderate to low. To address this finding, we propose an algorithm FedRater that learns client-specific label distributions in federated settings. Our experimental results show that FedRater not only improves the overall classification performance compared to FedAvg but also provides insights for estimating proxies of inter-rater agreement in distributed settings.
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