Deep spiking neural networks (SNNs) tend to suffer from long inference latency. While existing encoding schemes are insufficient, this work applies a bit-level loosely coupled (BLC) SNN in which output bits are generated sequentially from the current input bit. Consequently, the time steps can be further reduced with minor accuracy loss. The BLC SNN is optimized for hardware implementation and configured based on quantized artificial neural networks (ANNs). A hybrid early termination (ET) scheme is employed to skip redundant computation cycles without accuracy degradation. A pipelined digital accelerator architecture is designed to implement the BLC SNN, in which line buffers are utilized to maximize data reuse. Simulation results show that the proposed hybrid ET scheme reduces computation cycles by 28.31% on LeNet-5. Simulated in SMIC 28 nm technology, the accelerator consumes$0.10~\mu $J/img at 500 MHz with 30.49 TSOPS/W and is adaptable to 8/4 time steps of inference.

Video-based movie genre classification has garnered considerable attention due to its various applications in recommendation systems. Prior work has typically addressed this task by adapting models from traditional video classification tasks, such as action recognition or event detection. However, these models often neglect language elements (e.g., narrations or conversations) present in videos, which can implicitly convey high-level semantics of movie genres, like storylines or background context. Additionally, existing approaches are primarily designed to encode the entire content of the input video, leading to inefficiencies in predicting movie genres. Movie genre prediction may require only a few shots1to accurately determine the genres, rendering a comprehensive understanding of the entire video unnecessary. To address these challenges, we propose a Movie genre Classification method based on Language augmentatIon and shot samPling (Movie-CLIP). MovieCLIP mainly consists of two parts: a language augmentation module to recognize language elements from the input audio, and a shot sampling module to select representative shots from the entire video. We evaluate our method on MovieNet and Condensed Movies datasets, achieving approximate 6 − 9% improvement in mean Average Precision (mAP) over the baselines. We also generalize Movie-CLIP to the scene boundary detection task, achieving 1.1% improvement in Average Precision (AP) over the state-of-the-art. We release our implementation at this http URL.

Abstract Accurate tracking of the 3D pose of animals from video recordings is critical for many behavioral studies, yet there is a dearth of publicly available datasets that the computer vision community could use for model development. We here introduce the Rodent3D dataset that records animals exploring their environment and/or interacting with each other with multiple cameras and modalities (RGB, depth, thermal infrared). Rodent3D consists of 200 min of multimodal video recordings from up to three thermal and three RGB-D synchronized cameras (approximately 4 million frames). For the task of optimizing estimates of pose sequences provided by existing pose estimation methods, we provide a baseline model called OptiPose. While deep-learned attention mechanisms have been used for pose estimation in the past, with OptiPose, we propose a different way by representing 3D poses as tokens for which deep-learned context models pay attention to both spatial and temporal keypoint patterns. Our experiments show how OptiPose is highly robust to noise and occlusion and can be used to optimize pose sequences provided by state-of-the-art models for animal pose estimation.

With the recent advances in communication technology and the availability of powerful desktop computers, networking has gained popularity and many applications are being moved onto the Internet. To ease the development of distributed applications, software support to facilitate coordination and communication is needed. The paper describes an object oriented system for structured design and development of distributed applications. The basic system consists of a set of multithreaded servers, one server for each site in the network, which provide some basic communication facilities. The system has been developed using JAVA as the programming language. We use the software support provided by this basic system to define commonly used patterns of interaction in distributed applications. We also identify several different techniques for systematic composition of patterns to develop different applications. We illustrate the use of our system by defining some patterns and using them to build a sample application.