Supervision and Data Dynamics in Vision Across Recognition and Generation Landscapes

Abstract

This thesis looks at visual perception through the lens of supervision and data dynamics across recognition and generation landscapes. Generative and discriminative modeling form important pillars in computer vision. Depending on the task techniques to better learn and utilize the data and labels can change. Through this work we investigate different tasks along this landscape focusing on different supervision strategies, highlighting pitfalls in current approaches and propose modified architectures and losses to utilize the data better under different settings.

On the recognition side we start by analyzing Vision Transformers (ViTs) through a comprehensive analysis under varied supervision paradigms. We look at a mix of explicit supervision, contrastive self-supervision, and reconstructive self-supervision by delving into attention mechanisms and learned representations. We then look at a more specific case of supervision geared towards object detection which is called sparse supervision where their are missing annotations. We propose to utilize self and semi-supervised techniques to solve this task. Finally, we also explore a discovery style framework with applications on GAN generated image detection. Unlike sparse supervision discussed earlier, this scenario handles the case where are test time we have an unknown number of new classes. We were the first work proposing this problem where instead of just identifying synthetic images, we also try to group them based on their generation source. The exploration of Generative Adversarial Networks (GANs) in an open-world scenario uncovers the intricacies of learning with limited supervision for discovery style problems.

On the generation side we delve into different supervision strategies involving decomposing and decoupling representations. In the first work we tackle the problem of paired Image-to-Image (I2I) translation by decomposing supervision into reconstruction and residuals and highlight issues with traditional training approaches. We then look at generating talking head videos through two different kinds of supervision, video and audio. For driving the generation using a video we look at decoupling representations for the task of few-shot talking-head synthesis where the supervision is provided using only a few samples (shots). For this task we factorize the representation into spatial and style components which helps the learning. To supervise the generation additionally through audio, we look at multimodal supervision for lip-synchronized talking head generation. For this we incorporate audio and video modalities to synthesize lifelike talking-heads which can work even in in-the-wild scenarios.

In the last part we showcase two works which link our experiences from generation and recognition where we explore generative modeling to improve recognition models. The first work here utilizes the advancements in diffusion based image generation models to improve recognition models. Given the high fidelity and control of generation which diffusion models have brought, we utilize synthetic data from these models and create a suitable pipeline to utilize this data effectively to improve detection and segmentation performance. As a follow up to our ViT analysis we also propose a new technique to utilize off the shelf pretrained ViTs and generate high resolution features using a learnt lightweight feature transform. These high resolution features are especially effective for dense tasks like correspondence, segmentation, detection and object discovery.