Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutional-neural-network, which promote the efficiency for creating models. Unsupervised solutions such as feature average pooling, as a simple label-independent parameter-free based method, has limited ability to represent the video. While the supervised methods, like RNN, can greatly improve the recognition accuracy. However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased. In this paper, we proposes a novel video classification method based on a deep convolutional graph neural network(DCGN). The proposed method utilize the characteristics of the hierarchical structure of the video, and performed multi-level feature extraction on the video frame sequence through the graph network, obtained a video representation re ecting the event semantics hierarchically. We test our model on YouTube-8M Large-Scale Video Understanding dataset, and the result outperforms RNN based benchmarks.Comment: ECCV 201

9-Ethyl-9H-carbazole-3-carbaldehyde

The title mol­ecule, C15H13NO, approximates a planar conformation except for the alkyl chain (ethyl group) bonded to the N atom with a maximum deviation from the least-squares plane through the 15 planar atoms of 0.120 (2) Å for the O atom. The distance of the formyl O atom from the plane of the carbazole ring is 0.227 (2) Å. The N—C bond lengths in the central ring are significantly different, reflecting the electron-withdrawing properties of the aldehyde group. As a consequence, charge transfer may occur from the carbazole N atom to the substituted benzene ring

Impairment of pulmonary function and changes in the right cardiac structure of pneumoconiotic coal workers in China

Introduction Information on the changes of pulmonary function and the right cardiac structure in patients with coal worker’s pneumoconiosis in China is very scarce. This study was performed to clarify the changes of pulmonary function and right cardiac structure in patients with coal worker’s pneumoconiosis in China. Material and methods Pulmonary function, pulmonary artery systolic pressure, and the right cardiac structure were evaluated by spirometry and color Doppler echocardiography. Results The pulmonary artery systolic pressure of patients with coal worker’s pneumoconiosis was increased with disease severity. Patients with coal worker’s pneumoconiosis also exhibited an impaired pulmonary function and altered right cardiac structure compared with control subjects. A significant linear correlation of the variables of pulmonary ventilation and diffusion function with the indicators of the right cardiac structure was found in patients with coal worker’s pneumoconiosis in China. Conclusions This study elucidated a deterioration of pulmonary function and right cardiac structure in patients with coal worker’s pneumoconiosis in China

Thermal conductivity of deformed carbon nanotubes

We investigate the thermal conductivity of four types of deformed carbon nanotubes by using the nonequilibrium molecular dynamics method. It is reported that various deformations have different influence on the thermal properties of carbon nanotubes. For the bending carbon nanotubes, the thermal conductivity is independent on the bending angle. However, the thermal conductivity increases lightly with XY-distortion and decreases rapidly with Z-distortion. The thermal conductivity does not change with the screw ratio before the breaking of carbon nanotubes but decreases sharply after the critical screw ratio.Comment: 6figure

Model Inversion Attack via Dynamic Memory Learning

Model Inversion (MI) attacks aim to recover the private training data from the target model, which has raised security concerns about the deployment of DNNs in practice. Recent advances in generative adversarial models have rendered them particularly effective in MI attacks, primarily due to their ability to generate high-fidelity and perceptually realistic images that closely resemble the target data. In this work, we propose a novel Dynamic Memory Model Inversion Attack (DMMIA) to leverage historically learned knowledge, which interacts with samples (during the training) to induce diverse generations. DMMIA constructs two types of prototypes to inject the information about historically learned knowledge: Intra-class Multicentric Representation (IMR) representing target-related concepts by multiple learnable prototypes, and Inter-class Discriminative Representation (IDR) characterizing the memorized samples as learned prototypes to capture more privacy-related information. As a result, our DMMIA has a more informative representation, which brings more diverse and discriminative generated results. Experiments on multiple benchmarks show that DMMIA performs better than state-of-the-art MI attack methods

Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training

Developing a practically-robust automatic speech recognition (ASR) is challenging since the model should not only maintain the original performance on clean samples, but also achieve consistent efficacy under small volume perturbations and large domain shifts. To address this problem, we propose a novel WavAugment Guided Phoneme Adversarial Training (wapat). wapat use adversarial examples in phoneme space as augmentation to make the model invariant to minor fluctuations in phoneme representation and preserve the performance on clean samples. In addition, wapat utilizes the phoneme representation of augmented samples to guide the generation of adversaries, which helps to find more stable and diverse gradient-directions, resulting in improved generalization. Extensive experiments demonstrate the effectiveness of wapat on End-to-end Speech Challenge Benchmark (ESB). Notably, SpeechLM-wapat outperforms the original model by 6.28% WER reduction on ESB, achieving the new state-of-the-art

COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts

Practical object detection application can lose its effectiveness on image inputs with natural distribution shifts. This problem leads the research community to pay more attention on the robustness of detectors under Out-Of-Distribution (OOD) inputs. Existing works construct datasets to benchmark the detector's OOD robustness for a specific application scenario, e.g., Autonomous Driving. However, these datasets lack universality and are hard to benchmark general detectors built on common tasks such as COCO. To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts. COCO-O has a large distribution gap with training data and results in a significant 55.7% relative performance drop on a Faster R-CNN detector. We leverage COCO-O to conduct experiments on more than 100 modern object detectors to investigate if their improvements are credible or just over-fitting to the COCO test set. Unfortunately, most classic detectors in early years do not exhibit strong OOD generalization. We further study the robustness effect on recent breakthroughs of detector's architecture design, augmentation and pre-training techniques. Some empirical findings are revealed: 1) Compared with detection head or neck, backbone is the most important part for robustness; 2) An end-to-end detection transformer design brings no enhancement, and may even reduce robustness; 3) Large-scale foundation models have made a great leap on robust object detection. We hope our COCO-O could provide a rich testbed for robustness study of object detection. The dataset will be available at \url{https://github.com/alibaba/easyrobust/tree/main/benchmarks/coco_o}.Comment: To appear in ICCV2023, https://github.com/alibaba/easyrobust/tree/main/benchmarks/coco_

technical report for projects TIGA and SEAL

repor