Assistant Professor Carl Vondrick, Didac Souris, and Ruoshi Liu developed a computer vision algorithm for predicting human interactions and body language in video, a capability that could have applications for assistive technology, autonomous vehicles, and collaborative robots.
Research from the department has been accepted to the 2021 Computer Vision and Pattern Recognition (CVPR) Conference. The annual event explores machine learning, artificial intelligence, and computer vision research and its applications.
Open-Vocabulary Object Detection Using Captions
Alireza Zareian Snap Inc. and Columbia University, Kevin Dela Rosa Snap Inc., Derek Hao Hu Snap Inc., Shih-Fu Chang Columbia University
Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. Accordingly, we establish a new state-of-the-art for scalable object detection.
Vx2Text: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs
Xudong Lin Columbia University, Gedas Bertasius Facebook AI, Jue Wang Facebook AI, Shih-Fu Chang Columbia University, Devi Parikh Facebook AI and Georgia Tech, Lorenzo Torresani Facebook AI and Dartmouth
We present Vx2Text, a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. In order to leverage transformer networks, which have been shown to be effective at modeling language, each modality is first converted into a set of language embeddings by a learnable tokenizer. This allows our approach to perform multimodal fusion in the language space, thus eliminating the need for ad-hoc cross-modal fusion modules. To address the non-differentiability of tokenization on continuous inputs (e.g., video or audio), we utilize a relaxation scheme that enables end-to-end training. Furthermore, unlike prior encoder-only models, our network includes an autoregressive decoder to generate open-ended text from the multimodal embeddings fused by the language encoder. This renders our approach fully generative and makes it directly applicable to different “video+x to text” problems without the need to design specialized network heads for each task. The proposed framework is not only conceptually simple but also remarkably effective: experiments demonstrate that our approach based on a single architecture outperforms the state-of-the-art on three video-based text-generation tasks—captioning, question answering, and audio-visual scene-aware dialog. Our code will be made publicly available.
Co-Grounding Networks With Semantic Attention for Referring Expression Comprehension in Videos
Sijie Song Wangxuan Institute of Computer Technology, Xudong Lin Columbia University, Jiaying Liu Wangxuan Institute of Computer Technology, Zongming Guo Wangxuan Institute of Computer Technology, Shih-Fu Chang Columbia University
In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics. Unlike previous methods which solve the problem in multiple stages (i.e., tracking, proposal-based matching), we tackle the problem from a novel perspective, co-grounding, with an elegant one-stage framework. We enhance the single-frame grounding accuracy by semantic attention learning and improve the cross-frame grounding consistency with co-grounding feature learning. Semantic attention learning explicitly parses referring cues in different attributes to reduce the ambiguity in the complex expression. Co-grounding feature learning boosts visual feature representations by integrating temporal correlation to reduce the ambiguity caused by scene dynamics. Experiment results demonstrate the superiority of our framework on the video grounding datasets VID and OTB in generating accurate and stable results across frames. Our model is also applicable to referring expression comprehension in images, illustrated by the improved performance on the RefCOCO dataset. Our project is available at https://sijiesong.github.io/co-grounding.
Seeing in Extra Darkness Using a Deep-Red Flash
Jinhui Xiong KAUST, Jian Wang Snap Research, Wolfgang Heidrich KAUST, Shree Nayar Snap Research and Columbia University
We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source. Our main observation is that in a dim environment, the human eye mainly uses rods for the perception of light, which are not sensitive to wavelengths longer than 620nm, yet the camera sensor still has a spectral response. We propose a novel modulation strategy when training a modern CNN model for guided image filtering, fusing a noisy RGB frame and a flash frame. This fusion network is further extended for video reconstruction. We have built a prototype with minor hardware adjustments and tested the new flash technique on a variety of static and dynamic scenes. The experimental results demonstrate that our method produces compelling reconstructions, even in extra dim conditions.
UC2: Universal Cross-Lingual Cross-Modal Vision-and-Language Pre-Training
Mingyang Zhou University of California, Davis, Luowei Zhou Microsoft Dynamics 365 AI Research, Shuohang Wang Microsoft Dynamics 365 AI Research, Yu Cheng Microsoft Dynamics 365 AI Research, Linjie Li Microsoft Dynamics 365 AI Research, Zhou Yu University of California, Davis and Columbia University, Jingjing Liu Microsoft Dynamics 365 AI Research
Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English languages, we introduce UC^2, the first machine translation-augmented framework for cross-lingual cross-modal representation learning. To tackle the scarcity problem of multilingual captions for image datasets, we first augment existing English-only datasets with other languages via machine translation (MT). Then we extend the standard Masked Language Modeling and Image-Text Matching training objectives to multilingual setting, where alignment between different languages is captured through shared visual context (eg. using image as pivot). To facilitate the learning of a joint embedding space of images and all languages of interest, we further propose two novel pre-training tasks, namely Maksed Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM), leveraging MT-enhanced translated data. Evaluation on multilingual image-text retrieval and multilingual visual question answering benchmarks demonstrates that our proposed framework achieves new state of the art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks.
Learning Goals From Failure
Dave Epstein Columbia University and Carl Vondrick Columbia University
We introduce a framework that predicts the goals behind observable human action in video. Motivated by evidence in developmental psychology, we leverage video of unintentional action to learn video representations of goals without direct supervision. Our approach models videos as contextual trajectories that represent both low-level motion and high-level action features. Experiments and visualizations show our trained model is able to predict the underlying goals in video of unintentional action. We also propose a method to “automatically correct” unintentional action by leveraging gradient signals of our model to adjust latent trajectories. Although the model is trained with minimal supervision, it is competitive with or outperforms baselines trained on large (supervised) datasets of successfully executed goals, showing that observing unintentional action is crucial to learning about goals in video.
Generative Interventions for Causal Learning
Chengzhi Mao Columbia University, Augustine Cha Columbia University, Amogh Gupta Columbia University, Hao Wang Rutgers University, Junfeng Yang Columbia University, Carl Vondrick Columbia University
We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally occurring spurious correlations, which cause them to fail on images outside of the training distribution. In this paper, we show that we can steer generative models to manufacture interventions on features caused by confounding factors. Experiments, visualizations, and theoretical results show this method learns robust representations more consistent with the underlying causal relationships. Our approach improves performance on multiple datasets demanding out-of-distribution generalization, and we demonstrate state-of-the-art performance generalizing from ImageNet to ObjectNet dataset.
Learning the Predictability of the Future
Didac Suris Columbia University, Ruoshi Liu Columbia University, Carl Vondrick Columbia University
We introduce a framework for learning from unlabeled video what is predictable in the future. Instead of committing up front to features to predict, our approach learns from data which features are predictable. Based on the observation that hyperbolic geometry naturally and compactly encodes hierarchical structure, we propose a predictive model in hyperbolic space. When the model is most confident, it will predict at a concrete level of the hierarchy, but when the model is not confident, it learns to automatically select a higher level of abstraction. Experiments on two established datasets show the key role of hierarchical representations for action prediction. Although our representation is trained with unlabeled video, visualizations show that action hierarchies emerge in the representation.
Linear Semantics in Generative Adversarial Networks
Jianjin Xu Columbia University, Changxi Zheng Columbia University
Generative Adversarial Networks (GANs) are able to generate high-quality images, but it remains difficult to explicitly specify the semantics of synthesized images. In this work, we aim to better understand the semantic representation of GANs, and thereby enable semantic control in GAN’s generation process. Interestingly, we find that a well-trained GAN encodes image semantics in its internal feature maps in a surprisingly simple way: a linear transformation of feature maps suffices to extract the generated image semantics. To verify this simplicity, we conduct extensive experiments on various GANs and datasets; and thanks to this simplicity, we are able to learn a semantic segmentation model for a trained GAN from a small number (e.g., 8) of labeled images. Last but not least, leveraging our finding, we propose two few-shot image editing approaches, namely Semantic-Conditional Sampling and Semantic Image Editing. Given a trained GAN and as few as eight semantic annotations, the user is able to generate diverse images subject to a user-provided semantic layout, and control the synthesized image semantics. We have made the code publicly available.
Assistant Professor Carl Vondrick has won the National Science Foundation’s (NSF) Faculty Early Career Development award for his proposal program to develop machine perception systems that robustly detect and track objects even when they disappear from sight, thereby enabling machines to build spatial awareness of their surroundings.