Cell segmentation in histopathological images plays a crucial role in understanding, diagnosing, and treating many diseases. However, data annotation for this is expensive since there can be a large number of cells per image, and expert pathologists are needed for labelling images. Instead, our paper focuses on using weak supervision -- annotation from related tasks -- to induce a segmenter. Recent foundation models, such as Segment Anything (SAM), can use prompts to leverage additional supervision during inference. SAM has performed remarkably well in natural image segmentation tasks; however, its applicability to cell segmentation has not been explored. In response, we investigate guiding the prompting procedure in SAM for weakly supervised cell segmentation when only bounding box supervision is available. We develop two workflows: (1) an object detector's output as a test-time prompt to SAM (D-SAM), and (2) SAM as pseudo mask generator over training data to train a standalone segmentation model (SAM-S). On finding that both workflows have some complementary strengths, we develop an integer programming-based approach to reconcile the two sets of segmentation masks, achieving yet higher performance. We experiment on three publicly available cell segmentation datasets namely, ConSep, MoNuSeg, and TNBC, and find that all SAM-based solutions hugely outperform existing weakly supervised image segmentation models, obtaining 9-15 pt Dice gains.
We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj. 3MASSIV comprises of 50k short videos (20 seconds average duration) and 100K unlabeled videos in 11 different languages and captures popular short video trends like pranks, fails, romance, comedy expressed via unique audio-visual formats like self-shot videos, reaction videos, lip-synching, self-sung songs, etc. 3MASSIV presents an opportunity for multimodal and multilingual semantic understanding on these unique videos by annotating them for concepts, affective states, media types, and audio language. We present a thorough analysis of 3MASSIV and highlight the variety and unique aspects of our dataset compared to other contemporary popular datasets with strong baselines. We also show how the social media content in 3MASSIV is dynamic and temporal in nature, which can be used for semantic understanding tasks and cross-lingual analysis.
In this paper, given a small bag of images, each containing a common but latent predicate, we are interested in localizing visual subject-object pairs connected via the common predicate in each of the images. We refer to this novel problem as visual relationship co-localization or VRC as an abbreviation. VRC is a challenging task, even more so than the well-studied object co-localization task. This becomes further challenging when using just a few images, the model has to learn to co-localize visual subject-object pairs connected via unseen predicates. To solve VRC, we propose an optimization framework to select a common visual relationship in each image of the bag. The goal of the optimization framework is to find the optimal solution by learning visual relationship similarity across images in a few-shot setting. To obtain robust visual relationship representation, we utilize a simple yet effective technique that learns relationship embedding as a translation vector from visual subject to visual object in a shared space. Further, to learn visual relationship similarity, we utilize a proven meta-learning technique commonly used for few-shot classification tasks. Finally, to tackle the combinatorial complexity challenge arising from an exponential number of feasible solutions, we use a greedy approximation inference algorithm that selects approximately the best solution. We extensively evaluate our proposed framework on variations of bag sizes obtained from two challenging public datasets, namely VrR-VG and VG-150, and achieve impressive visual co-localization performance.