Deep Multimodal Feature Encoding for Video Ordering

True understanding of videos comes from a joint analysis of all its modalities: the video frames, the audio track, and any accompanying text such as closed captions. We present a way to learn a compact multimodal feature representation that encodes all these modalities. Our model parameters are learned through a proxy task of inferring the temporal ordering of a set of unordered videos in a timeline. To this end, we create a new multimodal dataset for temporal ordering that consists of approximately 30K scenes (2-6 clips per scene) based on the "Large Scale Movie Description Challenge". We analyze and evaluate the individual and joint modalities on three challenging tasks: (i) inferring the temporal ordering of a set of videos; and (ii) action recognition. We demonstrate empirically that multimodal representations are indeed complementary, and can play a key role in improving the performance of many applications.Comment: IEEE International Conference on Computer Vision (ICCV) Workshop on Large Scale Holistic Video Understanding. The datasets and code are available at https://github.com/vivoutlaw/tcb

Test of Time: Instilling Video-Language Models with a Sense of Time

Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language models to have a sense of time. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We establish that seven existing video-language models struggle to understand even such simple temporal relations. We then question whether it is feasible to equip these foundational models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We conduct a zero-shot evaluation of the adapted models on six datasets for three downstream tasks which require varying degrees of time awareness. We observe encouraging performance gains especially when the task needs higher time awareness. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without the need for data and compute-intense training from scratch.Comment: Accepted for publication at CVPR 2023. Project page: https://bpiyush.github.io/testoftime-website/index.htm

Eye vs. AI: Human Gaze and Model Attention in Video Memorability

Understanding the factors that determine video memorability has important applications in areas such as educational technology and advertising. Towards this goal, we investigate the semantic and temporal attention mechanisms underlying video memorability. We propose a Transformer-based model with spatio-temporal attention that matches SoTA performance on video memorability prediction on a large naturalistic video dataset. More importantly, the self-attention patterns show us where the model looks to predict memorability. We compare model attention against human gaze fixation density maps collected through a small-scale eye-tracking experiment where humans perform a video memory task. Quantitative saliency metrics show that the model attention and human gaze follow similar patterns. Furthermore, while panoptic segmentation confirms that the model and humans attend more to thing classes, stuff classes that receive increased/decreased attention tend to have higher memorability scores. We also observe that the model assigns greater importance to the initial frames, mimicking temporal attention patterns found in humans

Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays

Real-world application of chest X-ray abnormality classification requires dealing with several challenges: (i) limited training data; (ii) training and evaluation sets that are derived from different domains; and (iii) classes that appear during training may have partial overlap with classes of interest during evaluation. To address these challenges, we present an integrated framework called Generalized Cross-Domain Multi-Label Few-Shot Learning (GenCDML-FSL). The framework supports overlap in classes during training and evaluation, cross-domain transfer, adopts meta-learning to learn using few training samples, and assumes each chest X-ray image is either normal or associated with one or more abnormalities. Furthermore, we propose Generalized Episodic Training (GenET), a training strategy that equips models to operate with multiple challenges observed in the GenCDML-FSL scenario. Comparisons with well-established methods such as transfer learning, hybrid transfer learning, and multi-label meta-learning on multiple datasets show the superiority of our approach.Comment: 17 page