Recognizing Human-Object Interactions in Videos

Understanding human actions that involve interacting with objects is very important due to the wide range of real-world applications, such as security surveillance and healthcare. In this thesis, three different approaches are presented for addressing the problem of human-object interactions (HOIs) recognition in videos. Firstly, we propose a hierarchical framework for analyzing human-object interactions in a video sequence. The framework comprises Long Short-Term Memory (LSTM) networks that capture human motion and temporal object information independently. These pieces of information are then combined through a bilinear layer and fed into a global deep LSTM to learn high-level information about HOIs. To concentrate on the key components of human and object temporal information, the proposed approach incorporates an attention mechanism into LSTMs. Secondly, we aim to achieve a holistic understanding of human-object interactions (HOIs) by exploiting both their local and global contexts through knowledge distillation. The local context graphs are used to learn the relationship between humans and objects at the frame level by capturing their co-occurrence at a specific time step. On the other hand, the global relation graph is constructed based on the video-level of human and object interactions, identifying their long-term relations throughout a video sequence. We investigate how knowledge from these context graphs can be distilled to their counterparts to improve HOI recognition. Lastly, we propose the Spatio-Temporal Interaction Transformer-based (STIT) network to reason about spatio-temporal changes of humans and objects. Specifically, the spatial transformers learn the local context of humans and objects at specific frame times. The temporal transformer then learns the relations at a higher level between spatial context representations at different time steps, capturing long-term dependencies across frames. We further investigate multiple hierarchy designs for learning human interactions. The effectiveness of each of the proposed methods mentioned above is evaluated using various video action datasets that include human-object interactions, such as Charades, CAD-120, and Something-Something V1

DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving

Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that has yet to be thoroughly explored in existing research. To address this gap, we present DAMO-StreamNet, an optimized framework that combines recent advances from the YOLO series with a comprehensive analysis of spatial and temporal perception mechanisms, delivering a cutting-edge solution. The key innovations of DAMO-StreamNet are: (1) A robust neck structure incorporating deformable convolution, enhancing the receptive field and feature alignment capabilities. (2) A dual-branch structure that integrates short-path semantic features and long-path temporal features, improving motion state prediction accuracy. (3) Logits-level distillation for efficient optimization, aligning the logits of teacher and student networks in semantic space. (4) A real-time forecasting mechanism that updates support frame features with the current frame, ensuring seamless streaming perception during inference. Our experiments demonstrate that DAMO-StreamNet surpasses existing state-of-the-art methods, achieving 37.8% (normal size (600, 960)) and 43.3% (large size (1200, 1920)) sAP without using extra data. This work not only sets a new benchmark for real-time perception but also provides valuable insights for future research. Additionally, DAMO-StreamNet can be applied to various autonomous systems, such as drones and robots, paving the way for real-time perception. The code is available at https://github.com/zhiqic/DAMO-StreamNet

The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised Framework

In the context of label-efficient learning on video data, the distillation method and the structural design of the teacher-student architecture have a significant impact on knowledge distillation. However, the relationship between these factors has been overlooked in previous research. To address this gap, we propose a new weakly supervised learning framework for knowledge distillation in video classification that is designed to improve the efficiency and accuracy of the student model. Our approach leverages the concept of substage-based learning to distill knowledge based on the combination of student substages and the correlation of corresponding substages. We also employ the progressive cascade training method to address the accuracy loss caused by the large capacity gap between the teacher and the student. Additionally, we propose a pseudo-label optimization strategy to improve the initial data label. To optimize the loss functions of different distillation substages during the training process, we introduce a new loss method based on feature distribution. We conduct extensive experiments on both real and simulated data sets, demonstrating that our proposed approach outperforms existing distillation methods in terms of knowledge distillation for video classification tasks. Our proposed substage-based distillation approach has the potential to inform future research on label-efficient learning for video data

Dual Semantic Fusion Network for Video Object Detection

Video object detection is a tough task due to the deteriorated quality of video sequences captured under complex environments. Currently, this area is dominated by a series of feature enhancement based methods, which distill beneficial semantic information from multiple frames and generate enhanced features through fusing the distilled information. However, the distillation and fusion operations are usually performed at either frame level or instance level with external guidance using additional information, such as optical flow and feature memory. In this work, we propose a dual semantic fusion network (abbreviated as DSFNet) to fully exploit both frame-level and instance-level semantics in a unified fusion framework without external guidance. Moreover, we introduce a geometric similarity measure into the fusion process to alleviate the influence of information distortion caused by noise. As a result, the proposed DSFNet can generate more robust features through the multi-granularity fusion and avoid being affected by the instability of external guidance. To evaluate the proposed DSFNet, we conduct extensive experiments on the ImageNet VID dataset. Notably, the proposed dual semantic fusion network achieves, to the best of our knowledge, the best performance of 84.1\% mAP among the current state-of-the-art video object detectors with ResNet-101 and 85.4\% mAP with ResNeXt-101 without using any post-processing steps.Comment: 9 pages,6 figure

Effect of oil palm empty fruit bunches (OPEFB) fibers to the compressive strength and water absorption of concrete

Growing popularity based on environmentally-friendly, low cost and lightweight building materials in the construction industry has led to a need to examine how these characteristics can be achieved and at the same time giving the benefit to the environment and maintain the material requirements based on the standards required. Recycling of waste generated from industrial and agricultural activities as measures of building materials is not only a viable solution to the problem of pollution but also to produce an economic design of building