Social Media Ready Caption Generation for Brands

Social media advertisements are key for brand marketing, aiming to attract consumers with captivating captions and pictures or logos. While previous research has focused on generating captions for general images, incorporating brand personalities into social media captioning remains unexplored. Brand personalities are shown to be affecting consumers' behaviours and social interactions and thus are proven to be a key aspect of marketing strategies. Current open-source multimodal LLMs are not directly suited for this task. Hence, we propose a pipeline solution to assist brands in creating engaging social media captions that align with the image and the brand personalities. Our architecture is based on two parts: a the first part contains an image captioning model that takes in an image that the brand wants to post online and gives a plain English caption; b the second part takes in the generated caption along with the target brand personality and outputs a catchy personality-aligned social media caption. Along with brand personality, our system also gives users the flexibility to provide hashtags, Instagram handles, URLs, and named entities they want the caption to contain, making the captions more semantically related to the social media handles. Comparative evaluations against various baselines demonstrate the effectiveness of our approach, both qualitatively and quantitatively.

An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis

We consider the problem of constraining diffusion model outputs with a user-supplied reference image. Our key objective is to extract multiple attributes (e.g., color, object, layout, style) from this single reference image, and then generate new samples with them. One line of existing work proposes to invert the reference images into a single textual conditioning vector, enabling generation of new samples with this learned token. These methods, however, do not learn multiple tokens that are necessary to condition model outputs on the multiple attributes noted above. Another line of techniques expand the inversion space to learn multiple embeddings but they do this only along the layer dimension (e.g., one per layer of the DDPM model) or the timestep dimension (one for a set of timesteps in the denoising process), leading to suboptimal attribute disentanglement. To address the aforementioned gaps, the first contribution of this paper is an extensive analysis to determine which attributes are captured in which dimension of the denoising process. As noted above, we consider both the time-step dimension (in reverse denoising) as well as the DDPM model layer dimension. We observe that often a subset of these attributes are captured in the same set of model layers and/or across same denoising timesteps. For instance, color and style are captured across same U-Net layers, whereas layout and color are captured across same timestep stages. Consequently, an inversion process that is designed only for the time-step dimension or the layer dimension is insufficient to disentangle all attributes. This leads to our second contribution where we design a new multi-attribute inversion algorithm, MATTE, with associated disentanglement-enhancing regularization losses, that operates across both dimensions and explicitly leads to four disentangled tokens (color, style, layout, and object).

Iterative Multi-granular Image Editing using Diffusion Models

Recent advances in text-guided image synthesis has dramatically changed how creative professionals generate artistic and aesthetically pleasing visual assets. To fully support such creative endeavors, the process should possess the ability to: 1) iteratively edit the generations and 2) control the spatial reach of desired changes (global, local or anything in between). We formalize this pragmatic problem setting as Iterative Multi-granular Editing. While there has been substantial progress with diffusion-based models for image synthesis and editing, they are all one shot (i.e., no iterative editing capabilities) and do not naturally yield multi-granular control (i.e., covering the full spectrum of local-to-global edits). To overcome these drawbacks, we propose EMILIE: Iterative Multi-granular Image Editor. EMILIE introduces a novel latent iteration strategy, which re-purposes a pre-trained diffusion model to facilitate iterative editing. This is complemented by a gradient control operation for multi-granular control. We introduce a new benchmark dataset to evaluate our newly proposed setting. We conduct exhaustive quantitatively and qualitatively evaluation against recent state-of-the-art approaches adapted to our task, to being out the mettle of EMILIE. We hope our work would attract attention to this newly identified, pragmatic problem setting.

Learning with Multi-modal Gradient Attention for Explainable Composed Image Retrieval

We consider the problem of composed image retrieval that takes an input query consisting of an image and a modification text indicating the desired changes to be made on the image and retrieves images that match these changes. Current state-of-the-art techniques that address this problem use global features for the retrieval, resulting in incorrect localization of the regions of interest to be modified because of the global nature of the features, more so in cases of real-world, in-the-wild images. Since modifier texts usually correspond to specific local changes in an image, it is critical that models learn local features to be able to both localize and retrieve better. To this end, our key novelty is a new gradient-attention-based learning objective that explicitly forces the model to focus on the local regions of interest being modified in each retrieval step. We achieve this by first proposing a new visual image attention computation technique, which we call multi-modal gradient attention (MMGrad) that is explicitly conditioned on the modifier text. We next demonstrate how MMGrad can be incorporated into an end-to-end model training strategy with a new learning objective that explicitly forces these MMGrad attention maps to highlight the correct local regions corresponding to the modifier text. By training retrieval models with this new loss function, we show improved grounding by means of better visual attention maps, leading to better explainability of the models as well as competitive quantitative retrieval performance on standard benchmark datasets.

Contextual Prompt Learning for Vision-Language Understanding

Recent advances in multimodal learning has resulted in powerful vision-language models, whose representations are generalizable across a variety of downstream tasks. Recently, their generalizability has been further extended by incorporating trainable prompts, borrowed from the natural language processing literature. While such prompt learning techniques have shown impressive results, we identify that these prompts are trained based on global image features which limits itself in two aspects: First, by using global features, these prompts could be focusing less on the discriminative foreground image, resulting in poor generalization to various out-of-distribution test cases. Second, existing work weights all prompts equally whereas our intuition is that these prompts are more specific to the type of the image. We address these issues with as part of our proposed Contextual Prompt Learning (CoPL) framework, capable of aligning the prompts to the localized features of the image. Our key innovations over earlier works include using local image features as part of the prompt learning process, and more crucially, learning to weight these prompts based on local features that are appropriate for the task at hand. This gives us dynamic prompts that are both aligned to local image features as well as aware of local contextual relationships. Our extensive set of experiments on a variety of standard and few-shot datasets show that our method produces substantially improved performance when compared to the current state of the art methods. We also demonstrate both few-shot and out-of-distribution performance to establish the utility of learning dynamic prompts that are aligned to local image features.

Learning with Difference Attention for Visually Grounded Self-supervised Representations

Recent works in self-supervised learning have shown impressive results on single-object images, but they struggle to perform well on complex multi-object images as evidenced by their poor visual grounding. To demonstrate this concretely, we propose visual difference attention (VDA) to compute visual attention maps in an unsupervised fashion by comparing an image with its salient-regions-masked-out version. We use VDA to derive attention maps for state-of-the art SSL methods and show they do not highlight all salient regions in an image accurately, suggesting their inability to learn strong representations for downstream tasks like segmentation. Motivated by these limitations, we cast VDA as a differentiable operation and propose a new learning objective, Differentiable Difference Attention (DiDA) loss, which leads to substantial improvements in an SSL model's visually grounding to an image's salient regions.

A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis

While recent developments in text-to-image generative models have led to a suite of high-performing methods capable of producing creative imagery from free-form text, there are several limitations. By analyzing the cross-attention representations of these models, we notice two key issues. First, for text prompts that contain multiple concepts, there is a significant amount of pixel-space overlap (i.e., same spatial regions) among pairs of different concepts. This eventually leads to the model being unable to distinguish between the two concepts and one of them being ignored in the final generation. Next, while these models attempt to capture all such concepts during the beginning of denoising (e.g., first few steps) as evidenced by cross-attention maps, this knowledge is not retained by the end of denoising (e.g., last few steps). Such loss of knowledge eventually leads to inaccurate generation outputs. To address these issues, our key innovations include two test-time attention-based loss functions that substantially improve the performance of pretrained baseline text-to-image diffusion models. First, our attention segregation loss reduces the cross-attention overlap between attention maps of different concepts in the text prompt, thereby reducing the confusion/conflict among various concepts and the eventual capture of all concepts in the generated output. Next, our attention retention loss explicitly forces text-to-image diffusion models to retain cross-attention information for all concepts across all denoising time steps, thereby leading to reduced information loss and the preservation of all concepts in the generated output.

What to Read in a Contract? Party-Specific Summarization of Important Obligations, Entitlements, and Prohibitions in Legal Documents

Legal contracts, such as employment or lease agreements, are important documents as they govern the obligations and entitlements of the various contracting parties. However, these documents are typically long and written in legalese resulting in lots of manual hours spent in understanding them. In this paper, we address the task of summarizing legal contracts for each of the contracting parties, to enable faster reviewing and improved understanding of them. Specifically, we collect a dataset consisting of pairwise importance comparison annotations by legal experts for ~293K sentence pairs from lease agreements. We propose a novel extractive summarization system to automatically produce a summary consisting of the most important obligations, entitlements, and prohibitions in a contract. It consists of two modules: (1) a content categorize to identify sentences containing each of the categories (i.e., obligation, entitlement, and prohibition) for a party, and (2) an importance ranker to compare the importance among sentences of each category for a party to obtain a ranked list. The final summary is produced by selecting the most important sentences of a category for each of the parties. We demonstrate the effectiveness of our proposed system by comparing it against several text ranking baselines via automatic and human evaluation.

Agent-Specific Deontic Modality Detection in Legal Language

Legal documents are typically long and written in legalese, which makes it particularly difficult for laypeople to understand their rights and duties. While natural language understanding technologies can be valuable in supporting such understanding in the legal domain, the limited availability of datasets annotated for deontic modalities in the legal domain, due to the cost of hiring experts and privacy issues, is a bottleneck. To this end, we introduce, LEXDEMOD, a corpus of English contracts annotated with deontic modality expressed with respect to a contracting party or agent along with the modal triggers. We benchmark this dataset on two tasks: (i) agent-specific multi-label deontic modality classification, and (ii) agent-specific deontic modality and trigger span detection using Transformer-based (Vaswani et al., 2017) language models. Transfer learning experiments show that the linguistic diversity of modal expressions in LEXDEMOD generalizes reasonably from lease to employment and rental agreements. A small case study indicates that a model trained on LEXDEMOD can detect red flags with high recall. We believe our work offers a new research direction for deontic modality detection in the legal domain.

Entailment Relation Aware Paraphrase Generation

We introduce a new task of entailment relation aware paraphrase generation which aims at generating a paraphrase conforming to a given entailment relation (e.g. equivalent, forward entailing, or reverse entailing) with respect to a given input. We propose a reinforcement learning-based weakly-supervised paraphrasing system, ERAP, that can be trained using existing paraphrase and natural language inference (NLI) corpora without an explicit task-specific corpus. A combination of automated and human evaluations show that ERAP generates paraphrases conforming to the specified entailment relation and are of good quality as compared to the baselines and uncontrolled paraphrasing systems. Using ERAP for augmenting training data for downstream textual entailment task improves performance over an uncontrolled paraphrasing system, and introduces fewer training artifacts, indicating the benefit of explicit control during paraphrasing.