Ali Javidani

Ali Javidani, Babak Nadjar Araabi, Mohammad Amin Sadeghi

This paper introduces a novel approach that integrates graph theory into self-supervised representation learning. Traditional methods focus on intra-instance variations generated by applying augmentations. However, they often overlook important inter-instance relationships. While our method retains the intra-instance property, it further captures inter-instance relationships by constructing k-nearest neighbor (KNN) graphs for both teacher and student streams during pretraining. In these graphs, nodes represent samples along with their latent representations. Edges encode the similarity between instances. Following pretraining, a representation refinement phase is performed. In this phase, Graph Neural Networks (GNNs) propagate messages not only among immediate neighbors but also across multiple hops, thereby enabling broader contextual integration. Experimental results on CIFAR-10, ImageNet-100, and ImageNet-1K demonstrate accuracy improvements of 7.3%, 3.2%, and 1.0%, respectively, over state-of-the-art methods. These results highlight the effectiveness of the proposed graph based mechanism. The code is publicly available at https://github.com/alijavidani/SSL-GraphNNCLR.

Ali Javidani, Mohammad Amin Sadeghi, Babak Nadjar Araabi

Self-supervised visual representation learning traditionally focuses on image-level instance discrimination. Our study introduces an innovative, fine-grained dimension by integrating patch-level discrimination into these methodologies. This integration allows for the simultaneous analysis of local and global visual features, thereby enriching the quality of the learned representations. Initially, the original images undergo spatial augmentation. Subsequently, we employ a distinctive photometric patch-level augmentation, where each patch is individually augmented, independent from other patches within the same view. This approach generates a diverse training dataset with distinct color variations in each segment. The augmented images are then processed through a self-distillation learning framework, utilizing the Vision Transformer (ViT) as its backbone. The proposed method minimizes the representation distances across both image and patch levels to capture details from macro to micro perspectives. To this end, we present a simple yet effective patch-matching algorithm to find the corresponding patches across the augmented views. Thanks to the efficient structure of the patch-matching algorithm, our method reduces computational complexity compared to similar approaches. Consequently, we achieve an advanced understanding of the model without adding significant computational requirements. We have extensively pretrained our method on datasets of varied scales, such as Cifar10, ImageNet-100, and ImageNet-1K. It demonstrates superior performance over state-of-the-art self-supervised representation learning methods in image classification and downstream tasks, such as copy detection and image retrieval. The implementation of our method is accessible on GitHub.

Ali Javidani, Ahmad Mahmoudi-Aznaveh

In this paper, a novel video classification method is presented that aims to recognize different categories of third-person videos efficiently. Our motivation is to achieve a light model that could be trained with insufficient training data. With this intuition, the processing of the 3-dimensional video input is broken to 1D in temporal dimension on top of the 2D in spatial. The processes related to 2D spatial frames are being done by utilizing pre-trained networks with no training phase. The only step which involves training is to classify the 1D time series resulted from the description of the 2D signals. As a matter of fact, optical flow images are first calculated from consecutive frames and described by pre-trained CNN networks. Their dimension is then reduced using PCA. By stacking the description vectors beside each other, a multi-channel time series is created for each video. Each channel of the time series represents a specific feature and follows it over time. The main focus of the proposed method is to classify the obtained time series effectively. Towards this, the idea is to let the machine learn temporal features. This is done by training a multi-channel one dimensional Convolutional Neural Network (1D-CNN). The 1D-CNN learns the features along the only temporal dimension. Hence, the number of training parameters decreases significantly which would result in the trainability of the method on even smaller datasets. It is illustrated that the proposed method could reach the state-of-the-art results on two public datasets UCF11, jHMDB and competitive results on HMDB51.

Ali Javidani, Ahmad Mahmoudi-Aznaveh

In this paper, a new video classification methodology is proposed which can be applied in both first and third person videos. The main idea behind the proposed strategy is to capture complementary information of appearance and motion efficiently by performing two independent streams on the videos. The first stream is aimed to capture long-term motions from shorter ones by keeping track of how elements in optical flow images have changed over time. Optical flow images are described by pre-trained networks that have been trained on large scale image datasets. A set of multi-channel time series are obtained by aligning descriptions beside each other. For extracting motion features from these time series, PoT representation method plus a novel pooling operator is followed due to several advantages. The second stream is accomplished to extract appearance features which are vital in the case of video classification. The proposed method has been evaluated on both first and third-person datasets and results present that the proposed methodology reaches the state of the art successfully.