This work explores the challenges of leveraging large-scale vision language models, such as CLIP, for visual relationship prediction (VRP), a task vital in understanding the relations between objects in a scene based on both image features and text descriptors. Despite its potential, we find that CLIP’s language priors are restrictive in effectively differentiating between various predicates for VRP. Towards this, we present CREPE (CLIP Representation Enhanced Predicate Estimation), which utilizes learnable prompts and a unique contrastive training strategy to derive reliable CLIP representations suited for VRP. CREPE can be seamlessly integrated into any VRP method. Our evaluations on the Visual Genome benchmark illustrate that using representations from CREPE significantly enhances the performance of vanilla VRP methods, such as UVTransE and VCTree. This enhancement is notable as CREPE can be seamlessly integrated into any VRP method, even without the need for additional calibration techniques, showcasing its efficacy as a powerful solution to VRP. CREPE’s performance on the Unrel benchmark reveals strong generalization to diverse and previously unseen predicate occurrences, despite lacking explicit training on such examples.

The problem of adapting models from a source domain using data from any target domain of interest has gained prominence, thanks to the brittle generalization in deep neural networks. While several test-time adaptation techniques have emerged, they typically rely on synthetic data augmentations in cases of limited target data availability. In this paper, we consider the challenging setting of single-shot adaptation and explore the design of augmentation strategies. We argue that augmentations utilized by existing methods are insufficient to handle large distribution shifts, and hence propose a new approach SiSTA (Single-Shot Target Augmentations), which first fine-tunes a generative model from the source domain using a single-shot target, and then employs novel sampling strategies for curating synthetic target data. Using experiments with a state-of-the-art domain adaptation method, we find that SiSTA produces improvements as high as 20% over existing baselines under challenging shifts in face attribute detection, and that it performs competitively to oracle models obtained by training on a larger target dataset. Our codes can be accessed at github.com/kowshikthopalli/SISTA.

In this paper, we address the problem of adapting models from a source domain to a target domain, a task that has become increasingly important due to the brittle generalization of deep neural networks. While several test-time adaptation techniques have emerged, they typically rely on synthetic toolbox data augmentations in cases of limited target data availability. We consider the challenging setting of single-shot adaptation and explore the design of augmentation strategies. We argue that augmentations utilized by existing methods are insufficient to handle large distribution shifts, and hence propose a new approach SiSTA, which first fine-tunes a generative model from the source domain using a single-shot target, and then employs novel sampling strategies for curating synthetic target data. Using experiments on a variety of benchmarks, distribution shifts and image corruptions, we find that SiSTA produces significantly improved generalization over existing baselines in face attribute detection and multi-class object recognition. Furthermore, SiSTA performs competitively to models obtained by training on larger target datasets. Our codes can be accessed at https://github.com/Rakshith-2905/SiSTA

Model agnostic meta-learning algorithms aim to infer priors from several observed tasks that can then be used to adapt to a new task with few examples. Given the inherent diversity of tasks arising in existing benchmarks, recent methods have resorted to task-specific adaptation of the prior. Our goal is to improve generalization of meta learners when the task distribution contains challenging distribution shifts and semantic disparities. To this end, we introduce CAML (Contrastive Knowledge-Augmented Meta Learning), a knowledge-enhanced few-shot learning approach that evolves a knowledge graph to encode historical experience, and employs a contrastive distillation strategy to leverage the encoded knowledge for task-aware modulation of the base learner. In addition to the standard few-shot task adaptation, we also consider the more challenging multi-domain task adaptation and few-shot dataset generalization settings in our evaluation with standard benchmarks. Our empirical study shows that CAML (i) enables simple task encoding schemes; (ii) eliminates the need for knowledge extraction at inference time; and most importantly, (iii) effectively aggregates historical experience thus leading to improved performance in both multi-domain adaptation and dataset generalization.

Inverting an image onto the latent space of pre-trained generators, e.g., StyleGAN-v2, has emerged as a popular strategy to leverage strong image priors for ill-posed restoration. Several studies have showed that this approach is effective at inverting images similar to the data used for training. However, with out-of-distribution (OOD) data that the generator has not been exposed to, existing inversion techniques produce sub-optimal results. In this paper, we propose SPHInX (StyleGAN with Projection Heads for Inverting X), an approach for accurately embedding OOD images onto the StyleGAN latent space. SPHInX optimizes a style projection head using a novel training strategy that imposes a vicinal regularization in the StyleGAN latent space. To further enhance OOD inversion, SPHInX can additionally optimize a content projection head and noise variables in every layer. Our empirical studies on a suite of OOD data show that, in addition to producing higher quality reconstructions over the state-of-the-art inversion techniques, SPHInX is effective for ill-posed restoration tasks while offering semantic editing capabilities.