An Efficient and Streaming Audio Visual Active Speaker Detection System

• URL: http://arxiv.org/abs/2409.09018v1
• Date: Fri, 13 Sep 2024 17:45:53 GMT
• Title: An Efficient and Streaming Audio Visual Active Speaker Detection System
• Authors: Arnav Kundu, Yanzi Jin, Mohammad Sekhavat, Max Horton, Danny Tormoen, Devang Naik,
• Abstract summary: We present two scenarios that address the key challenges posed by real-time constraints. First, we introduce a method to limit the number of future context frames utilized by the ASD model. Second, we propose a more stringent constraint that limits the total number of past frames the model can access during inference.
• Score: 2.4515389321702132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper delves into the challenging task of Active Speaker Detection (ASD), where the system needs to determine in real-time whether a person is speaking or not in a series of video frames. While previous works have made significant strides in improving network architectures and learning effective representations for ASD, a critical gap exists in the exploration of real-time system deployment. Existing models often suffer from high latency and memory usage, rendering them impractical for immediate applications. To bridge this gap, we present two scenarios that address the key challenges posed by real-time constraints. First, we introduce a method to limit the number of future context frames utilized by the ASD model. By doing so, we alleviate the need for processing the entire sequence of future frames before a decision is made, significantly reducing latency. Second, we propose a more stringent constraint that limits the total number of past frames the model can access during inference. This tackles the persistent memory issues associated with running streaming ASD systems. Beyond these theoretical frameworks, we conduct extensive experiments to validate our approach. Our results demonstrate that constrained transformer models can achieve performance comparable to or even better than state-of-the-art recurrent models, such as uni-directional GRUs, with a significantly reduced number of context frames. Moreover, we shed light on the temporal memory requirements of ASD systems, revealing that larger past context has a more profound impact on accuracy than future context. When profiling on a CPU we find that our efficient architecture is memory bound by the amount of past context it can use and that the compute cost is negligible as compared to the memory cost.

Related papers

• FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution [68.77813885751308]
  State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information.<n>We propose an efficient and scalable solution inspired by the structural and statistical similarities between hyperspectral images (HSI) and video data.<n>Our approach introduces a compression-driven dimensionality reduction strategy that reduces computational complexity, accelerates inference, and enhances the extraction of temporal information across frames.
  arXiv  Detail & Related papers  (2025-06-13T07:59:52Z)
• Log-Augmented Generation: Scaling Test-Time Reasoning with Reusable Computation [80.69067017594709]
  Large language models (LLMs) and their agentic counterparts struggle to retain reasoning from previous tasks.<n>We propose a novel framework, log-augmented generation (LAG) that directly reuses prior computation and reasoning from past logs at test time.<n>Our method significantly outperforms standard agentic systems that do not utilize logs.
  arXiv  Detail & Related papers  (2025-05-20T14:14:38Z)
• FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge [60.000984252907195]
  Auto-regressive (AR) models have recently shown promise in visual generation tasks due to their superior sampling efficiency.<n>Video generation requires a substantially larger number of tokens to produce coherent temporal frames, resulting in significant overhead during the decoding phase.<n>We propose the textbfFastCar framework to accelerate the decode phase for the AR video generation by exploring the temporal redundancy.
  arXiv  Detail & Related papers  (2025-05-17T05:00:39Z)
• PRESERVE: Prefetching Model Weights and KV-Cache in Distributed LLM Serving [2.7309692684728613]
  Large language models (LLMs) are typically served from clusters of GPU/NPUs that consist of large number of devices.<n>Prior work addressed this issue by overlapping communication with compute, but has severe limitations due to the data dependencies between these operations.<n>We propose PRESERVE, a novel framework that prefetches model weights and KV-cache from off-chip memory to the on-chip cache of AI accelerators.
  arXiv  Detail & Related papers  (2025-01-14T15:14:10Z)
• Faster Diffusion Action Segmentation [9.868244939496678]
  Temporal Action Classification (TAS) is an essential task in video analysis, aiming to segment and classify continuous frames into distinct action segments. Recent advances in diffusion models have demonstrated substantial success in TAS tasks due to their stable training process and high-quality generation capabilities. We propose EffiDiffAct, an efficient and high-performance TAS algorithm.
  arXiv  Detail & Related papers  (2024-08-04T13:23:18Z)
• Temporal Feature Matters: A Framework for Diffusion Model Quantization [105.3033493564844]
  Diffusion models rely on the time-step for the multi-round denoising. We introduce a novel quantization framework that includes three strategies. This framework preserves most of the temporal information and ensures high-quality end-to-end generation.
  arXiv  Detail & Related papers  (2024-07-28T17:46:15Z)
• Temporal Sentence Grounding in Streaming Videos [60.67022943824329]
  This paper aims to tackle a novel task - Temporal Sentence Grounding in Streaming Videos (TSGSV) The goal of TSGSV is to evaluate the relevance between a video stream and a given sentence query. We propose two novel methods: (1) a TwinNet structure that enables the model to learn about upcoming events; and (2) a language-guided feature compressor that eliminates redundant visual frames.
  arXiv  Detail & Related papers  (2023-08-14T12:30:58Z)
• An Efficiency Study for SPLADE Models [5.725475501578801]
  In this paper, we focus on improving the efficiency of the SPLADE model. We propose several techniques including L1 regularization for queries, a separation of document/ encoders, a FLOPS-regularized middle-training, and the use of faster query encoders.
  arXiv  Detail & Related papers  (2022-07-08T11:42:05Z)
• Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation [49.17930380106643]
  We propose a novel framework to speed up any architecture with skip-connections for real-time vision tasks. Specifically, at the arrival of each frame, we transform the features from the previous frame to reuse them at specific spatial bins. We then perform partial computation of the backbone network on the regions of the current frame that captures temporal differences between the current and previous frame.
  arXiv  Detail & Related papers  (2022-06-20T07:20:02Z)
• Fast Online Video Super-Resolution with Deformable Attention Pyramid [172.16491820970646]
  Video super-resolution (VSR) has many applications that pose strict causal, real-time, and latency constraints, including video streaming and TV. We propose a recurrent VSR architecture based on a deformable attention pyramid (DAP)
  arXiv  Detail & Related papers  (2022-02-03T17:49:04Z)
• Dissecting User-Perceived Latency of On-Device E2E Speech Recognition [34.645194215436966]
  We show that factors affecting token emission latency, and endpointing behavior significantly impact on user-perceived latency (UPL) We achieve the best trade-off between latency and word error rate when performing ASR jointly with endpointing, and using the recently proposed alignment regularization.
  arXiv  Detail & Related papers  (2021-04-06T00:55:11Z)
• Temporal Memory Relation Network for Workflow Recognition from Surgical Video [53.20825496640025]
  We propose a novel end-to-end temporal memory relation network (TMNet) for relating long-range and multi-scale temporal patterns. We have extensively validated our approach on two benchmark surgical video datasets.
  arXiv  Detail & Related papers  (2021-03-30T13:20:26Z)
• Memformer: A Memory-Augmented Transformer for Sequence Modeling [55.780849185884996]
  We present Memformer, an efficient neural network for sequence modeling. Our model achieves linear time complexity and constant memory space complexity when processing long sequences.
  arXiv  Detail & Related papers  (2020-10-14T09:03:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.