We present a validation dataset of newly-collected kitchen-based egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations. Importantly, all annotations are grounded in 3D through digital twinning of the scene, fixtures, object locations, and primed with gaze. Footage is collected from unscripted recordings in diverse home environments, making HD-EPIC the first dataset collected in-the-wild but with detailed annotations matching those in controlled lab environments.We show the potential of our highly-detailed annotations through a challenging VQA benchmark of 26K questions assessing the capability to recognise recipes, ingredients, nutrition, fine-grained actions, 3D perception, object motion, and gaze direction. The powerful long-context Gemini Pro only achieves 37.6% on this benchmark, showcasing its difficulty and highlighting shortcomings in current VLMs. We additionally assess action recognition, sound recognition, and long-term video-object segmentation on HD-EPIC.HD-EPIC is 41 hours of video in 9 kitchens with digital twins of 413 kitchen fixtures, capturing 69 recipes, 59K fine-grained actions, 51K audio events, 20K object movements and 37K object masks lifted to 3D. On average, we have 263 annotations per minute of our unscripted videos.

The goal of this work is to generate step-by-step visual instructions in the form of a sequence of images, given an input image that provides the scene context and the sequence of textual instructions. This is a challenging problem as it requires generating multi-step image sequences to achieve a complex goal while being grounded in a specific environment. Part of the challenge stems from the lack of large-scale training data for this problem. The contribution of this work is thus three-fold. First, we introduce an automatic approach for collecting large step-by-step visual instruction training data from instructional videos. We apply this approach to one million videos and create a large-scale, high-quality dataset of 0.6M sequences of image-text pairs. Second, we develop and train ShowHowTo, a video diffusion model capable of generating step-by-step visual instructions consistent with the provided input image. Third, we evaluate the generated image sequences across three dimensions of accuracy (step, scene, and task) and show our model achieves state-of-the-art results on all of them. Our code, dataset, and trained models are publicly available.

Non-native speakers with limited vocabulary often struggle to name specific objects despite being able to visualize them, e.g., people outside Australia searching for ‘numbats.’ Further, users may want to search for such elusive objects with difficult-to-sketch interactions, e.g., “numbat digging in the ground.” In such common but complex situations, users desire a search interface that accepts composite multimodal queries comprising hand-drawn sketches of “difficult-to-name but easy-to-draw” objects and text describing “difficult-to-sketch but easy-to-verbalize” object's attributes or interaction with the scene. This novel problem statement distinctly differs from the previously well-researched TBIR (text-based image retrieval) and SBIR (sketch-based image retrieval) problems. To study this under-explored task, we curate a dataset, CSTBIR (Composite Sketch+Text Based Image Retrieval), consisting of ~2M queries and 108K natural scene images. Further, as a solution to this problem, we propose a pretrained multimodal transformer-based baseline, STNet (Sketch+Text Network), that uses a hand-drawn sketch to localize relevant objects in the natural scene image, and encodes the text and image to perform image retrieval. In addition to contrastive learning, we propose multiple training objectives that improve the performance of our model. Extensive experiments show that our proposed method outperforms several state-of-the-art retrieval methods for text-only, sketch-only, and composite query modalities. We make the dataset and code available at: https://vl2g.github.io/projects/cstbir.

We propose SLBERT (Speech and Language pre-training framework for BERT), an end-to-end trainable framework for learning joint representations of speech and language modalities. We enhance the well-known BERT architecture to provide a dual-stream multimodal architecture that processes both speech and language input. To enable effective information exchange between the two modalities, we introduce a novel attention fusion mechanism via AF-Blocks. To acquire robust contrastive representations for speech and language processing applications, we pre-train SLBERT on three auxiliary tasks: Masked Language Modeling, Masked Speech Modeling, and Speech-Language Matching. We evaluate our proposed model on two well-known multimodal tasks: intent classification and sentiment analysis. Our model achieves state-of-the-art results on both benchmarks while surpassing even larger baselines.

Table -to-text generation has been widely studied in the Natural Language Processing community in the recent years.We give a new perspective to this problem by incorporating signals from both tables as well as associated images to generate relevant text.While tables contain a structured list of facts, images are a rich source of unstructured visual information.For example, in the tourism domain, images can be used to infer knowledge such as the type of landmark (e.g., church), its architecture (e.g., Ancient Roman), and composition (e.g., white marble).Therefore, in this paper, we introduce the novel task of Vision-augmented Table -To-Text Generation (VISTOT), defined as follows: given a table and an associated image, produce a descriptive sentence conditioned on the multimodal input.For the task, we present a novel multimodal table-to-text dataset, WIKILAND-MARKS, covering 73,084 unique world landmarks.Further, we also present a competitive architecture, namely, VT3 that generates accurate sentences conditioned on the image and table pairs.Through extensive analyses and experiments, we show that visual cues from images are helpful in (i) inferring missing information from incomplete or sparse tables, and (ii) strengthening the importance of useful information from noisy tables for natural language generation.We make the code and data publicly available 1 .