Abstract

Visual Question Answering (VQA) with multiple choice questions enables a vision-centric evaluation of Multimodal Large Language Models (MLLMs). Although it reliably checks the existence of specific visual abilities, it is easier for the model to select an answer from multiple choices (VQA evaluation) than to generate the answer itself. In this work, we offer a novel perspective: we evaluate how well an MLLM understands a specific visual concept by its ability to uniquely describe two extremely similar images that differ only in the targeted visual concept. Specifically, we assess the ability of MLLMs to capture specific points of visual differences using self-retrieval, i.e., by retrieving the target image using its generated caption against the other image in the pair serving as the distractor. We curate 247 highly similar image pairs as part of the D3 benchmark. For each image pair, the model is prompted to: (1) Detect a specific visual difference, and (2) Describe the target image uniquely such that it (3) Discriminates the target image from the distractor. Self-retrieval within D3 enables whitebox evaluation across six different visual patterns, revealing that current models struggle to independently discern fine-grained visual differences, with open-source models failing to outperform random guess.

Abstract

Deep learning models are capable of complex auditory processing tasks such as keyword spotting, genre classification, and audio captioning, yet remain opaque. While several works have explored interpretability of neural networks for computer vision and natural language processing, the audio modality has been largely ignored. In this paper, we study the behavior of the audio CNN encoder used in the contrastively trained language-audio model, CLAP. In the domain of music and human speech sounds, we localize and identify the layers of the network that perform well on tasks of varying complexity, sometimes even outperforming the model's final outputs. Digging deeper, we also localize specific dataset classes to neuron clusters within a layer and analyze a cluster’s contribution to the model’s discriminability for that class. To perform these analyses, we propose an automated framework that can leverage a small dataset of a few thousand samples to evaluate and score neuron clusters for their role in classification. Our findings provide insights into the hierarchical nature of representations in audio CNNs, paving the way for improved interpretability of audio model.

Abstract

A method for identifying a soundtrack for a digital book by automatically aligning the digital book and the soundtrack with a movie using a movie adaptation technique is provided. The method includes (i) receiving media content through a user device that includes the digital book, the soundtrack, and the movie,(ii) segmenting (a) the chapters of the digital book into paragraph segments,(b) the movie into scene boundaries, and (c) tracks of the soundtrack into cohesive track segments,(iii) aligning the scene boundaries with the paragraph segments to generate aligned paragraph segments,(iv) aligning a background soundtracks of the movie with the cohesive track segments of the soundtrack using a majority key and a minority key of the cohesive track segments and the background soundtracks to generate aligned cohesive track segments and (v) aligning the aligned paragraph segments with the aligned cohesive

Abstract

The limited generalization of coreference resolution (CR) models has been a major bottleneck in the task’s broad application. Prior work has identified annotation differences, especially for mention detection, as one of the main reasons for the generalization gap and proposed using additional annotated target domain data. Rather than relying on this additional annotation, we propose an alternative referential task, Major Entity Identification (MEI), where we: (a) assume the target entities to be specified in the input, and (b) limit the task to only the frequent entities. Through extensive experiments, we demonstrate that MEI models generalize well across domains on multiple datasets with supervised models and LLM-based few-shot prompting. Additionally, MEI fits the classification framework, which enables the use of robust and intuitive classification-based metrics. Finally, MEI is also of practical use as it allows a user to search for all mentions of a particular entity or a group of entities of interest.

Abstract

Characters are an important aspect of any storyline and identifying and including them in descriptions is necessary for story understanding. While previous work has largely ignored identity and generated captions with someone (anonymized names), recent work formulates id-aware captioning as a fill-in-the-blanks (FITB) task, where, given a caption with blanks, the goal is to predict person id labels. However, to predict captions with ids, a two-stage approach is required: first predict captions with someone, then fill in identities. In this work, we present a new single stage approach that can seamlessly switch between id-aware caption generation or FITB when given a caption with blanks. Our model, Movie-Identity Captioner (MICap), uses a shared auto-regressive decoder that benefits from training with FITB and full-caption generation objectives, while the encoder can benefit from or disregard captions with blanks as input. Another challenge with id-aware captioning is the lack of a metric to capture subtle differences between person ids. To this end, we introduce iSPICE, a caption evaluation metric that focuses on identity tuples created through intermediate scene graphs. We evaluate MICap on Large-Scale Movie Description Challenge (LSMDC), where we show a 4.2% improvement in FITB accuracy, and a 1-2% bump in classic captioning metrics.

Abstract

We introduce multimodal story summarization by leveraging TV episode recaps – short video sequences interweaving key visual moments and dialog from previous episodes to bring viewers up to speed. We propose PlotSnap, a dataset featuring two crime thriller TV shows with rich recaps, and long-form content over 40 minutes per episode. Recap shots are mapped to corresponding sub-stories that serve as labels for story summarization. We propose a hierarchical model TaleSumm that (i) processes entire episodes by creating compact shot and dialog representations, and (ii) predicts importance scores for each video shot and dialog utterance by enabling interactions between local story groups. Unlike traditional summarization tasks, our method extracts multiple plot points from long-form videos. We present a thorough evaluation on this task, including promising cross-series generalization. TaleSumm shows good results on video summarization benchmarks.

Abstract

Movie story analysis requires understanding characters’ emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions. By leveraging annotations from the MovieGraphs dataset [72], we aim to predict classic emotions (e.g. happy, angry) and other mental states (e.g. honest, helpful). We conduct experiments on the most frequently occurring 10 and 25 labels, and a mapping that clusters 181 labels to 26. Ablation studies and comparison against adapted stateof-the-art emotion recognition approaches shows the effectiveness of EmoTx. Analyzing EmoTx’s self-attention scores reveals that expressive emotions often look at character tokens while other mental states rely on video and dialog cues.

Abstract

Commonsense question-answering (QA) methods combine the power of pre-trained Language Models (LM) with the reasoning provided by Knowledge Graphs (KG). A typical approach collects nodes relevant to the QA pair from a KG to form a Working Graph (WG) followed by reasoning using Graph Neural Networks (GNNs). This faces two major challenges: (i) it is difficult to capture all the information from the QA in the WG, and (ii) the WG contains some irrelevant nodes from the KG. To address these, we propose GrapeQA with two simple improvements on the WG: (i) Prominent Entities for Graph Augmentation identifies relevant text chunks from the QA pair and augments the WG with corresponding latent representations from the LM, and (ii) ContextAware Node Pruning removes nodes that are less relevant to the QA pair. We evaluate our results on OpenBookQA, CommonsenseQA and MedQA-USMLE and see that GrapeQA shows consistent improvements over its LM + KG predecessor (QA-GNN in particular) and large improvements on OpenBookQA.

Abstract

Modelling and understanding time remains a challenge in contemporary video understanding models. Time also appears in language through temporal relations. Video-language models can benefit from having a sense of time, especially since language provides an interface for generalization. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We construct a simple synthetic dataset to measure such temporal understanding in video-language models and find that six existing models struggle to understand even such simple relations. We then posit whether it is feasible to equip these foundation models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on postpretraining on a small amount of video-text data. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without needing data- and compute-intense training from scratch. Project page:

Abstract

Modeling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language models to have a sense of time. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We establish that six existing video-language models struggle to understand even such simple temporal relations. We then question whether it is feasible to equip these foundational models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We conduct a zero-shot evaluation of the adapted models on six datasets for three downstream tasks which require a varying degree of time awareness. We observe encouraging performance gains especially when the task needs higher time awareness. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without the need for data and compute-intense training from scratch.