Exposing the Limits of Video-Text Models through Contrast Sets (2022)

Abstract: Recent video-text models can retrieve relevant videos based on text with a high accuracy, but to what extent do they comprehend the semantics of the text? Can they discriminate between similar entities and actions? To answer this, we propose an evaluation framework that probes video-text models with hard negatives. We automatically build contrast sets, where true textual descriptions are manipulated in ways that change their semantics while maintaining plausibility. Specifically, we leverage a pre-trained language model and a set of heuristics to create verb and person entity focused contrast sets. We apply these in the multiple choice video to-text classification setting. We test the robustness of recent methods on the proposed automatic contrast sets, and compare them to additionally collected human-generated counterparts, to assess their effectiveness. We see that model performance suffers across all methods, erasing the gap between recent CLIP-based methods vs. the earlier methods.

## introduction relating video and text modalities is one of the important goals in vision and language. video is a complex signal where people and objects act and interact with each other through space and time. thus correctly associating a textual description and a video requires understanding of entities, their actions and much more, making it a hard problem. one of the popular ways of training and evaluating video-text models is via cross-modal matching. often the task is formulated as a retrieval problem, where the goal is to select the correct match among many (e.g. thousand) candidates, and distractors are picked randomly @xcite . another way is via multiple-choice prediction, where the goal is to pick the true match out of several (e.g. 5) candidates @xcite . the latter allows figure 1 : samples of our video-to-text tasks on the msr-vtt @xcite and lsmdc dataset @xcite . a hard negative option is added by manipulating verb (top) and entity (bottom) in the ground truth sentence. two sota methods mmt @xcite and clip4clip @xcite incorrectly choose the manipulated sentence (option b) in both these cases. for more controlled choice of negatives, which are typically selected from other videos. commonly, the retrieval setting is used during training to avoid capturing any specific multiple-choice patterns or biases, while both are used for evaluation. recent methods that leverage the large-scale clip model @xcite show significant improvement in cross-modal matching, specifically, in the retrieval setting @xcite . they outperform the prior state-of-the-art methods, often based on the multimodal transformer design @xcite @xcite . however, we know that often model performance is "over-estimated" due to the lack of challenging samples in evaluation. for instance, @xcite show that model performance on several nlp tasks and one image-text task is much lower on contrast sets, which are test samples with small perturbation done by human experts in a way that changes the gold label. in this work, we are investigating whether the video-text models also struggle in an evaluation framework that probes them with hard negatives. instead of using human-designed contrast sets that are not easily scalable, we propose an automated pipeline that can generate contrast sets via verb and human entity manipulation. our manipulations are carefully designed to preserve fluency but change semantics of the textual descriptions, making them invalid for a given video. we focus on entities and verbs to evaluate if the model can truly understand "who did what" in a video. inspired by @xcite , we leverage a generative t5 language model @xcite to manipulate the verb phrase and use heuristics to swap person entities. note that our pipeline does not require a trained video-text model in the loop. we apply our automatic manipulations to two popular video-text benchmarks, msr-vtt @xcite and lsmdc @xcite . we additionally collect human generated contrast sets to compare with our automatic ones. to make sure that our automatic negatives are of high quality, we also confirm that humans can successfully select the correct description for a given video with our hard negatives. finally, we benchmark several video-text models on our contrast sets. we find that all methods degrade in performance with the introduction of hard negatives in the multiplechoice setting (figure 1 ). this includes the recent clip-based works that demonstrated large gains in the retrieval setting. this shows that all methods have difficulty discriminating between entities and verbs when the remaining context is unchanged. we observe that model performance drops especially on cases such as verb antonym swaps, where fine-grained action understanding is important. ## related work defending and generating adversarial examples @xcite have been mostly explored in nlp since the reign of pre-trained language models (lms) @xcite . li et al. (2020); garg and ramakrishnan (2020); morris et al. (2020) show that substituting words in a sentence with masked lms @xcite can successfully mislead the classification and entailment model predictions to be incorrect. template-based @xcite and manually crafted @xcite perturbations on evaluation datasets have also been studied for textual entailment. @xcite have curated a list of checklists to reveal bugs present in nlp models. language-based adversarial examples can be collected to study the robustness of vision-language models as well. @xcite intro-duces foil-coco dataset to evaluate the visionlanguage model's decision when associating images with both correct and "foil" captions. akula et al. (2020) measure the robustness of visual referring expression models by checking if grounding is performed correctly after word manipulation. hendricks and @xcite show that visionlanguage transformers are worse at verb understanding than nouns. new versions of the vqa dataset @xcite are proposed to study robustness of vqa models @xcite . @xcite automatically generate contrast sets from scene graphs to probe compositional consistency of vqa models. our work is different in that we use pre-trained lms to introduce perturbations and evaluate robustness of video-language models. ## designing contrast sets in this section we present our approach to automatically constructing text-based contrast sets for videolanguage tasks. suppose we are given a video v i and description s i . contrast sets ĉi = { ŝ1 , ..., ŝi } are designed such that ŝi is semantically inconsistent with v i and yet models incorrectly select ŝi over s i in a video-to-text multiple-choice setting. while there are different ways to create valid ĉi , we investigate manipulating 1) person entities and 2) verb phrases in the original descriptions. qualitative examples of ĉ are shown in ## conclusion we present a pipeline to build automatic contrast sets for video and language tasks, focused on manipulating person entities and verb phrases. we show that models struggle on contrast sets compared to random negatives, and stronger retrieval models do not show better robustness to hard negatives. for verb contrast sets, we find that model performance is strongly correlated with semantic proximity, unlike humans. we leave it as future work to use automatic contrast sets in training to improve model robustness, and designing contrast sets for different concepts/parts of speech.