How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs

## Abstract

Abstract Recent advancements in Large Language Models (LLMs) have led to the development of Video Large Multi-modal Models (Video-LMMs) that can handle a wide range of video understanding tasks. These models have the potential to be deployed in real-world applications such as robotics, AI assistants, medical surgery, and autonomous vehicles.
The widespread adoption of Video-LMMs in our daily lives underscores the importance of ensuring and evaluating their robust performance in mirroring human-like reasoning and interaction capabilities in complex, real-world contexts.
However, existing benchmarks for Video-LMMs primarily focus on general video comprehension abilities and neglect assessing their reasoning capabilities over complex videos in the real-world context, and robustness of these models through the lens of user prompts as text queries.
In this paper, we present the Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES), a novel benchmark that comprehensively assesses the performance of Video-LMMs across 11 diverse real-world video dimensions. We evaluate 9 recent models, including both open-source and closed-source variants, and find that most of the Video-LMMs, especially open-source ones, struggle with robustness and reasoning when dealing with complex videos. Based on our analysis, we develop a training-free Dual-Step Contextual Prompting (DSCP) technique to enhance the performance of existing Video-LMMs. Our findings provide valuable insights for building the next generation of human-centric AI systems with advanced robustness and reasoning capabilities.
Our dataset and code are publicly available at: mbzuai-oryx.github.io/CVRR-Evaluation-Suite/ .

## 1 Introduction

Recently, Large Language Models (LLMs) Touvron et al. (); Zheng et al. (); Jiang et al. () have demonstrated impressive reasoning and planning capabilities while simultaneously handling a wide range of NLP tasks Wei et al. (); Brown et al. ().
Consequently, their integration with the vision modality, specifically for video understanding tasks, has given rise to Video Large Multi-modal Models (Video-LMMs) Li et al. (). These models act as visual chatbots that accept both text and video as input and handle a diverse set of tasks, including video comprehension Maaz et al. (), detailed video understanding Lin et al. (), and action grounding Zhang et al. (). As these models directly capture video data, they hold substantial potential for deployment in real-world applications such as robotics, surveillance, medical surgery, and autonomous vehicles.

However, as these models assume an expanding role in our everyday lives, assessing their performance in comprehending complex videos and demonstrating reliable reasoning and robustness capabilities across diverse real-world contexts becomes essential. Video-LMMs with such capabilities will be more effective when integrated into our daily lives for solving perception tasks and will be a promising step towards building human-centric AI-assistive systems.

Several attempts in literature have been made to benchmark Video-LMMs. SEED-Bench Li et al. () curated a MCQ-based benchmarking dataset including 3 evaluation dimensions for videos. Similarly, MV-Bench Li et al. () constructed the Video-LMM benchmark and assembled 20 challenging video tasks for evaluating the spatial and temporal understanding of these models. While these methods aim at benchmarking Video-LMMs, they predominantly evaluate video and/or temporal comprehension abilities and overlook the complex reasoning aspects of Video-LMMs for real-world context, and their robustness towards user input text queries; both of which are crucial to ensure their responsible engagement with humans in various real-world situations in the wild. While some studies have explored similar areas such as hallucinations in image-based LLMs Liu et al. (); Qian et al. (), no such comprehensive study exists for the case of Video-LMMs.

**Table 1: Comparison of CVRR-ES with existing benchmarks for video QA. The CVRR-ES benchmark represents an initial effort to assess Video-LMMs in the context of their applicability and suitability in real-world applications.**
| Benchmark | Textual | Complex | In the wild | Contextual | Multiple | Temporal Order |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Robustness | Reasoning | (OOD) | Dependency | Actions | & Fine-grained |  |  |
| MSVD-QA Xu et al. () | \usym2717 | \usym2717 | \usym2717 | \usym2717 | \usym2717 | \usym2717 |  |
| MSRVTT-QA Xu et al. () | \usym2717 | \usym2717 | \usym2717 | \usym2717 | \usym2717 | \usym2717 |  |
| TGIF-QA Jang et al. () | \usym2717 | \usym2713 | \usym2717 | \usym2717 | \usym2713 | \usym2713 |  |
| Activity Net-QA Yu et al. () | \usym2717 | \usym2717 | \usym2717 | \usym2717 | \usym2717 | \usym2713 |  |
| VideoChat-GPT Maaz et al. () | \usym2717 | \usym2717 | \usym2717 | \usym2713 | \usym2717 | \usym2713 |  |
| MVBench Li et al. () | \usym2717 | \usym2713 | \usym2717 | \usym2717 | \usym2713 | \usym2713 |  |
| SEED-Bench Li et al. () | \usym2717 | \usym2717 | \usym2717 | \usym2717 | \usym2713 | \usym2713 |  |
| CVRR-ES (ours) | \usym2713 | \usym2713 | \usym2713 | \usym2713 | \usym2713 | \usym2713 |  |

Figure: Figure 1: Left: CVRR-ES comprises of 11 diverse complex video evaluation dimensions encompassing a variety of complex, real-world contexts. Right: Overall performance of Video-LMMs on the CVRR-ES benchmark. Results for each Video-LMM are averaged across 11 video dimensions.
Refer to caption: x1.png

Figure: Figure 2: We observe that most Video-LMMs struggle to reason over complex videos (rows 1-3) and exhibit weak robustness and rectification capabilities when prompted to generate answers for user questions that can sometimes be confusing (row 4). The QA pairs in Comprehensive Video Reasoning and Robustness Evaluation Suite (CVRR-ES) benchmark assess the performance of Video-LMMs beyond general video comprehension.
Refer to caption: x3.png

Motivated by the wide-scale applications of Video-LMMs and the lack of world-centric complex video benchmarking efforts, we present a new benchmark, Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES), to comprehensively assess the performance of Video-LMMs.
As shown in Tab. 1, CVRR-ES evaluates Video-LMMs on key aspects of robustness and reasoning in videos, encompassing video domains that more accurately test models in real-world scenarios such as videos having contextual dependency and in-the-wild aspects.
CVRR-ES is an open-ended video QA benchmark comprising 11 real-world video category dimensions (Fig. 1, left) that encompass diverse evaluation aspects. These dimensions span from context-dependent (e.g., social, emotional, etc.) categories to ones that often take place in the wild such as videos containing physically anomalous activities. We comprehensively evaluate a representative set of 9 recent Video-LMMs (Fig. 1, right) including both open-source and closed-source models on the CVRR-ES benchmark using a LLM-assisted automatic evaluation framework Maaz et al. (); Cai et al. ().

The performance of Video-LMMs on the CVRR-ES benchmark reveals that these models struggle to correctly comprehend complex videos indicating their weak reasoning and lack of robustness to the textual user queries (Fig. 2). For instance, state-of-the-art Video-LLaVA Lin et al. () achieves only 15.92% performance averaged across 11 video dimensions of CVRR-ES. In contrast, closed-source models including GPT4V(vision) OpenAI () and Gemini-Vision-Pro Google () exhibit relatively stronger performance but still lag behind the performance of humans. Using CVRR-ES benchmark, we extensively perform quantitative and qualitative analysis formulating important insights into these Video-LMMs based on their failure cases and individual performances across the diverse video dimensions.

Based on our analysis, we observe that standard prompting of Video-LMMs struggles in steering their focus for complex video understanding. Additionally, their limitations in reasoning and robust video understanding of real-world scenarios are dominantly driven by the quality of textual inputs (i.e., user questions). Based on these insights, we develop a training-free Dual-Step Contextual Prompting (DSCP) technique, which effectively steers the model’s behavior during inference to elicit video-specific reasoning and improved robustness within Video-LMMs. With DSCP, Video-LMMs show substantial improvements on our benchmark, suggesting the potential of prompting techniques for Video-LMMs. Our main contributions can be summarised as follows:

- •
We present the Complex Video Robustness and Reasoning Evaluation suite (CVRR-ES), a Video Question Answering benchmark designed to assess the reasoning and robustness capabilities of Video-LMMs across 11 diverse world-centric complex video dimensions.
- •
We comprehensively evaluate both open-source and closed-source Video-LMMs on the CVRR-ES benchmark and find that most models exhibit weak performance, highlighting their limited reasoning in complex videos and lack of robustness towards user text queries.
- •
We conduct extensive analysis and formulate important conclusions about Video-LMMs based on their failure cases and performance on the CVRR-ES benchmark. Our findings provide valuable insights for building the next generation of human-centric AI systems with improved robustness and reasoning capabilities.
- •
To improve Video-LMMs’ reasoning and robustness abilities, we formulate a model-agnostic and training-free prompting technique that effectively enhances their performance.

## 2 Related Works

Video Large Multi-modal models (Video-LMMs).
Video-LMMs Lin et al. (); Li et al. (); Zhang et al. () are advanced visual chatbots capable of performing a wide range of video understanding tasks, including video comprehension and captioning, video question-answering, and action grounding. These models accept both video and textual inputs and generate textual responses. From an architectural perspective, Video-LMMs typically combine pre-trained vision backbones Radford et al. (); Fang et al. (); Wang et al. () with large language models Touvron et al. (); Zheng et al. () using connector modules such as MLP adapters, Q-former Dai et al. (), and gated attention Alayrac et al. (). VideoChat Li et al. () and VideoChat-GPT Li et al. () presented initial open-source efforts in this direction and were trained with two stages of alignment and video-instruction following objectives. Recently, more advanced Video-LMMs have emerged in the field, with some models focusing on improving model architectures Li et al. (), expanding to new tasks Munasinghe et al. (), and enabling support for long videos Song et al. (); Ren et al. (). In this work, we aim to develop a comprehensive benchmarking evaluation framework to assess the reasoning and robustness capabilities of Video-LMMs and develop a training-free prompting technique to improve their performance on these fronts.

Benchmarking Video-LMMs.
With the growing number of Video-LMMs emerging in the research community, several works have presented evaluation frameworks to assess and quantify these models for benchmarking and analysis purposes. SEED-Bench Li et al. () evaluates the visual capabilities in both image and Video-LMMs across 12 unique dimensions. MV-Bench Li et al. () curates 20 challenging video tasks to evaluate spatial and temporal understanding of Video-LMMs. Video-ChatGPT Maaz et al. () develops a quantitative evaluation framework to assess model understanding across five aspects of general video comprehension, such as the correctness and consistency of model captions. While these evaluation frameworks provide effective insights, their assessments do not extend beyond general video-comprehension metrics to more advanced aspects of reasoning and robustness, particularly for real-world context cases. In contrast, our work focuses on providing a complex video reasoning and robustness benchmark across 11 diverse real-world-centric evaluation types and offers a more thorough assessment of Video-LMMs in practical applications.

Training-free Prompting Techniques. Steering model behavior at inference time using prompting has become a common paradigm in the NLP domain. Prompting Wei et al. (); Wang et al. () refers to the set of instructions given as a prefix to the language model to better align model responses with human intent without the need for task-specific fine-tuning. Prompting techniques can be as simple as a single sentence (e.g., "Let’s think step by step") such as zero-shot chain of thought Wei et al. () prompting, to more detailed techniques such as combining chain-of-thought prompting with few-shot learning Brown et al. () and self-consistency chain of thought prompting Wang et al. (). Surprisingly, training-free prompting techniques for Video Large Multi-modal Models (Video-LMMs) have been minimally explored.
In this work, we develop a dual-step prompting technique based on principled prompt instructions specifically designed to steer the model’s behavior for improved reasoning and robustness over complex videos.

## 3 Complex Video Reasoning and Robustness Evaluation Suite

As Video-LMMs are touching new real-world applications, it is essential to ensure that they robustly handle the user inputs, comprehend the visual world, and exhibit human-like reasoning capabilities.
In this work, our goal is to establish a comprehensive benchmark that specifically assess the robustness and reasoning capabilities of Video-LMMs in a variety of complex and contextual videos covering diverse scenarios.
To this end, we present Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES). We first provide a holistic overview of CVRR-ES benchmark below and detail the video evaluation dimensions in Sec. 3.1. Subsequently, we present the CVRR-ES creation process in Sec. 3.2. We provide details on the dataset quality and human evaluation in Appendix B.

Figure: Figure 3: CVRR-ES Benchmark Statistics. Left: Frequency distribution of the type of questions. Right: Illustration of the most frequent keywords in the answer-set of CVRR-ES benchmark.
Refer to caption: extracted/5585526/figures/combined.png

Overview of CVRR-ES Benchmark.
CVRR-ES encompasses evaluation dimensions that cover diverse video categories related to real-world scenarios, ranging from context-dependent (e.g., social, emotional) categories to video types that often take place in the wild (e.g., anomalous activities).
Specifically, we have compiled 11 video evaluation dimensions and curated 2,400 high-quality open-ended question-answer (QA) pairs, spanning 217 high-quality videos. The average video duration is 22.3 seconds, with maximum and minimum durations of 183 and 2 seconds, respectively. In Fig. 3 (left), we quantify the distribution of different question types present in our benchmark. This diverse set of questions aims to comprehensively capture the model’s answering capabilities based on reasoning and robustness criteria. We show the word cloud plot based on the frequency of key words in the answer set of CVRR-ES in Fig. 3 (right). The frequent words correspond to objects and attributes with which Video-LMMs could most likely interact when deployed in practical scenarios.

### 3.1 CVRR-ES Video Category definitions.

To assess the robustness and reasoning capabilities of Video-LMMs in the CVRR-ES benchmark, we carefully curate 11 diverse benchmark evaluation categories. As shown in Fig. 1 (left), these categories encompass a wide range of real-world complex and contextual videos within each category.
Below, we define each video evaluation dimension of the CVRR-ES benchmark in detail.

1) Multiple actions in a single video.
This category includes videos that contain multiple activities within a single video. The number of activities varies from 2 to 4 in these videos, mostly featuring humans performing multiple activities. We curate QA pairs in this category aiming to identify whether the model can reason over challenging questions concerning multiple actions and understand the interrelation between different actions within a video.

2) Fine-grained action understanding.
We gather video samples with fine-grained actions. These actions encompass various
fine-grained
activities performed by humans, including pushing, opening, closing, spreading, sitting, etc. This category presents a challenge to the model’s comprehension of subtle and fine-grained actions through carefully crafted questions.

3) Partial actions.
Based on our observations that Video-LMMs predominantly generate content that may be contextually relevant and likely to co-occur with the depicted scene in the video, we compile videos featuring actions that have a high probability of being followed by subsequent actions but are not executed in the video. For instance, an action such as cracking an egg in a kitchen setting often anticipates the subsequent action of frying/cooking the egg.

4) Time order understanding.
Accurately recognizing the temporal sequence of activities in videos is crucial for distinguishing between atomic actions, such as pushing and pulling. We collect videos of fine-grained actions occurring in a particular temporal direction and curate challenging questions.

5) Non-existent actions with existent scene depictions.
This category examines the model’s robustness and reasoning behavior in scenarios where we introduce non-existent activities into the video without altering the physical and spatial scenes or environmental details in it.

6) Non-existent actions with non-existent scene depictions.
In this evaluation category, we make the QA task more challenging by creating questions that include both non-existent activities and non-existent scene comprehension. Non-existent scene comprehension involves changing the objects, attributes of objects, and background scene description. This evaluates the model’s reliability to correct misleading questions and avoid generating imaginary content.

7) Continuity and object instance count.
This category contains videos (both real and simulations) designed to test the models’ ability to accurately recognize the number of instances of objects, people, etc., and distinguish between existing objects and new ones introduced in the same video scene.

8) Unusual and physically anomalous activities.
This category consists of videos with unconventional activities and physical phenomena that seemingly defy the laws of physics. We meticulously collect relevant videos from various sources on the internet, focusing on capturing unusual activities such as a person floating in the air or driving a motorbike on a running river. We believe that assessing Video-LMMs in such scenarios is crucial, as it allows us to determine whether they can generalize to understand actions in out-of-distribution videos that can occur in practical situations.

9) Interpretation of social context.
In the real world, human actions are often influenced by social context in their surroundings. For instance, a person might be helping an elderly individual cross the road. This category evaluates Video-LMMs on such scenarios to determine their ability to accurately infer the rationale behind actions based on the depicted social context. We gather diverse videos from the internet and create challenging questions that encompass the social context dimension.

10) Understanding of emotional context.
Similar to social context, humans can accurately understand and interpret each other’s actions by considering the emotional context. For example, a person being emotionally moved and crying in a gathering could be a happy moment if it is one stemming from success/joy. We collect videos and curate challenging reasoning questions aimed at recognizing the nature of actions solely based on emotional context for evaluating Video-LMMs.

11) Interpretation of visual context.
This dimension focuses on assessing the model’s reasoning abilities to recognize the actions by leveraging the overall visual contextual cues in the video. We curate specific videos containing actions where activity identification and reasoning require visual contextual cues. For example, to identify the number of people present based on the presence of shadows, one must utilize the visual context from the shadows to reason about the question.

Qualitative Examples. Fig. 2 shows examples of collected videos for the CVRR-ES benchmark. The curated videos are carefully selected to be diverse and contain rich spatio-temporal content, aligned with the proposed video evaluation dimensions.

### 3.2 Building CVRR-ES Benchmark

After defining the video evaluation dimensions, we now proceed toward building the CVRR-ES benchmark which consists of three stages. We present each stage in detail below.

Stage 1: Data collection and Annotation.
We first collect high-quality videos and annotate each video using human assistance.
To ensure that each evaluation dimension captures the relevant attributes and information, we meticulously select videos that are representative of specific characteristics associated with that dimension.
Across the 11 dimensions, 214 unique videos are selected for the benchmark with around 20 videos per evaluation category.
Around 60% of these videos are collected from public academic datasets. To introduce diversity in the benchmark distribution, we incorporate video samples from multiple academic datasets including Something-Something-v2 Goyal et al. (), CATER Girdhar and Ramanan (), Charades Sigurdsson et al. (), ActivityNet Caba Heilbron et al. (), HMDB51 Kuehne et al. (), YFCC100M Thomee et al. (). The remaining 40% of videos are collected from the internet.

Following the video collection process, two experienced human annotators are assigned to generate captions for each video. For videos where initial captions or metadata are available from academic datasets,
the captions are generated by the annotators based on them.
For videos collected from the internet, captions are entirely generated by human annotators.
To ensure consistency and high quality, we provide annotation instructions to annotators, who generate captions accordingly. Personalized annotation guidelines are used for each video category. Refer to additional details in Appendix B.

Stage 2: Question-Answer Generation.
The first challenge is to select an evaluation setting to assess Video-LMMs. Humans typically engage in free-form conversation to interact with each other in day-to-day life. Inspired by this, we aim to simulate a similar style of interaction with Video-LMMs by curating open-ended QA pairs to evaluate these models for robustness and reasoning.
We feed detailed ground-truth video captions to GPT-3.5 LLM, which are utilized to generate open-ended questions covering both reasoning and robustness aspects.

Reasoning QA pairs: With Video-LMMs beginning to interact more directly with humans in our lives, it’s crucial to validate the reasoning abilities of Video-LMMs for more reliable Human-AI interaction. When evaluating the reasoning capabilities of Video-LMMs, we aim to determine whether these models can understand the input video not only by analyzing spatial content but also by grasping the underlying rationale behind the occurring activities and their relationships with the surrounding context. This involves creating questions that go beyond simple video comprehension and scene description and require the model to engage in complex logical inference, contextual understanding, and reasoning about counterfactual and hypothetical scenarios.

Robustness QA pairs: In addition to evaluating the reasoning capabilities of LLMs, it is important to assess Video-LMMs to ensure their robust and responsible performance in real-world scenarios. In the context of Video-LMMs, robustness can be evaluated from both visual (video input) and textual interfaces. Our focus in this work lies on textual interface robustness by particularly testing the model’s comprehension when posed with misleading or confusing questions. This scenario mirrors realistic situations where users, based on their expertise levels, may pose irrelevant, misleading, or confusing questions. It is crucial for models to demonstrate reliability and robustness in handling such queries and avoid generating unreal or hallucinated content for input videos.

We curate specific prompts for each evaluation dimension to instruct LLM in generating QA pairs. Example prompts used as an instruction to LLMs for curating QA pairs for robustness and reasoning aspects are provided in Fig. 14 in the Appendix D.

Stage 3: QA Pairs Filtration.
After generating QA pairs, a manual filtration step is employed, with human assistance to verify each generated QA pair. Approximately 30% of the QA pairs generated by GPT-3.5 are found to be noisy, containing questions that are unrelated to the video evaluation dimensions or unanswerable based on the provided ground-truth captions. Additionally, many questions contain answers within the question itself. Therefore, an exhaustive filtering process is conducted which involves QA rectification and removing those samples which are not relevant to the video or evaluation type. This process results in a final set of 2400 high-quality QA pairs for the CVRR-ES benchmark. Examples of QA pairs are shown in Tab. 4 in the Appendix.

Stage 4: Evaluation Procedure.
Previous methods in the literature Maaz et al. (); Cai et al. (); Liu et al. (); Qian et al. () have explored using LLM models as judges for quantifying results in open-ended QA benchmarks. We adopt a similar approach and instruct LLMs to act as teachers to assess the correctness of predicted responses from Video-LMMs compared to ground-truth answers. We generate open-ended predictions from Video-LMMs by providing video-question pairs as inputs and then present the model predictions and their corresponding ground-truth responses to the LLM Judge alongside the evaluation prompt. The Judge determines whether the prediction is correct or incorrect through a binary judgment, assigns a score from 1 to 5 representing the quality of the prediction, and provides a reasoning to explain its decision. Our ablative analysis in the Appendix. D demonstrates that reasoning-constrained LLM-based evaluation aligns well with human-based judgment. The evaluation prompt is shown in Fig. 13 in the Appendix D.

## 4 Dual-Step Contextual Prompting for Video-LMMs.

Given their wide-scale potential in practical downstream applications, new Video-LMMs are frequently introduced by the research community. Despite the availability of numerous Video-LMMs, the majority of them are trained using only positive examples and video-conversational templates that are primarily limited to tasks such as video-captioning and video question answering. This leads to highly over-affirmative behavior and a lack of self-rectification abilities in these models (Sec. 5.4).

Figure: Figure 4: Principled prompt instructions in our DSCP method for improving reasoning and robustness in Video-LMMs.
Refer to caption: x4.png

Additionally, the templates have minimal focus on enhancing reasoning and robustness capabilities through reasoning-based instruction-tuning pairs, resulting in weak performance of such models against robustness and reasoning QA evaluations in the CVRR-ES benchmark. Furthermore, curating reasoning-based instruction fine-tuning datasets requires meticulous data curation steps, and retraining these models is computationally expensive Li et al. (); Ren et al. ().

Alternatively, training-free prompting techniques in NLP literature have shown effectiveness in eliciting reasoning abilities in LLMs such as chain of thought and self-consistency prompting Wei et al. (); Wang et al. ().
Inspired by these approaches, we introduce a prompting technique called Dual Step Contextual Prompting (DSCP), which aims to steer Video-LMM focus for enhanced reasoning while simultaneously encouraging the models to provide robust and grounded answers. DSCP is a two-step prompting method that 1) ensures that the model comprehends the video while reasoning over crucial aspects of complex video understanding such as contextual information and decoding the complex relationships between objects and motions, etc., and 2) encourages robustness by generating the response against the question while conditioning both on video and the context retrieved in the first step. Below we discuss each step of DSCP in detail.

Step 1: Reasoning over the video.
We first guide Video-LMMs using principled prompts to interpret video content from a reasoning perspective. As shown in Fig. 4 (in blue), we formulate ten principled reasoning-based instructions for prompting, $P_{\texttt{reason}}$, which directs Video-LMMs to not only comprehend the general video content but also steers them to reason over the rationale behind occurring activities and their relationships with the surrounding context. These prompt instructions include specific considerations like contextual priors, the temporal order of actions, instance count, and attributes. Additionally, the prompting technique incorporates instructions to ensure conciseness and factuality, aiming to mitigate hallucinations.

Figure: Figure 5: Qualitative results of DSCP prompting method. Using our DSCP approach, Video-LMMs demonstrate enhanced robustness and reasoning capabilities over complex videos.
Refer to caption: x5.png

Given a Video-LMM $\mathcal{F}$ and input video $\mathcal{V}$, we retrieve contextual reasoning information $I_{\texttt{context}}$ by providing principled reasoning prompt $P_{\texttt{reason}}$ along with the video to the LMM, $I_{\texttt{context}}=\mathcal{F}(P_{\texttt{reason}}|\mathcal{V}).$
The contextual information is utilized in the second step of DSCP to generate a more grounded response to the user question.

Step 2: Context conditioned question answering.
As discussed earlier, Video-LMMs are primarily trained with positive examples to answer questions, with limited emphasis on reasoning and robustness aspects. Consequently, enabling direct interaction of Video-LMMs with users in real-world scenarios can result in undesired responses when the user question is confusing and deceiving due to their extreme over-affirmative behavior. To address these challenges, we propose incorporating an additional inference step in Video-LMMs before answering the user’s question. We note that Video-LMMs often possess factual knowledge about the video content but may become distracted and produce hallucinations when prompted with confusing or misleading questions (more details in Appendix C). Specifically, we devise a prompting method that conditions the model to first comprehend the video in detail without attending to the user question, thereby eliminating the influence of the question. The complex video comprehension information refers to $I_{\texttt{context}}$ formulated in step 1.

Subsequently, we pose the user question in the second step using prompt $P_{\texttt{user}}$ which combines user question and the contextual reasoning information (Fig. 4, in green) while conditioning the model on both the video and the contextual reasoning information $I_{\texttt{context}}$. Concretely, $\texttt{Final response}=\mathcal{F}(P_{\texttt{user}}|\mathcal{V})$, where $P_{\texttt{user}}=[\texttt{question};I_{\texttt{context}}]$.

Intuitively, the factual content generated in the first step will guide the model towards a robust response in the second step to produce factual and correct responses, even in the presence of noisy/misleading user questions. We illustrate the qualitative results of the DSCP method in Fig. 5. This approach leads to responses that are better grounded with the actual video content and are robust against potential lesser-quality user queries. As we will later show, the DSCP technique effectively enhances the performance of Video-LMMs on the CVRR-ES benchmark.

## 5 Evaluation Experiments on CVRR-ES.

**Table 2: Evaluation results of Video LLMs across various video-evaluation categories on the CVRR-ES benchmark. We present results for both open-source and closed-source models, alongside human evaluation results which serves as the upper bound on the benchmark.**
| Benchmark Category | Video-LLaMA-2 | VideoChat | Video-ChatGPT | Video-LLaVA | MovieChat | LLaMA-VID | TimeChat | Gemini-V Pro | GPT4V | Human |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Multiple Actions in | 16.98 | 23.90 | 27.67 | 15.72 | 12.58 | 17.92 | 28.30 | 43.08 | 57.55 | 93.40 |
| single video. |  |  |  |  |  |  |  |  |  |  |
| Fine-grained action | 29.57 | 33.48 | 26.96 | 25.22 | 23.48 | 26.09 | 39.13 | 51.61 | 77.39 | 95.65 |
| understanding. |  |  |  |  |  |  |  |  |  |  |
| Partial | 24.76 | 33.01 | 22.82 | 13.59 | 21.36 | 14.56 | 49.51 | 67.48 | 73.79 | 98.54 |
| actions. |  |  |  |  |  |  |  |  |  |  |
| Time order | 16.45 | 31.58 | 27.63 | 21.05 | 16.45 | 19.74 | 34.21 | 45.39 | 57.89 | 97.37 |
| understanding. |  |  |  |  |  |  |  |  |  |  |
| Non-existent actions with | 10.14 | 15.22 | 23.19 | 5.07 | 5.07 | 2.90 | 23.19 | 57.25 | 71.01 | 97.10 |
| existent scene. |  |  |  |  |  |  |  |  |  |  |
| Non-existent actions with | 13.19 | 14.58 | 17.36 | 3.47 | 11.81 | 6.94 | 13.89 | 49.64 | 75.00 | 100.00 |
| non-existent scene. |  |  |  |  |  |  |  |  |  |  |
| Continuity and Object | 28.25 | 24.29 | 28.41 | 21.47 | 19.77 | 24.86 | 34.46 | 36.16 | 62.71 | 96.49 |
| instance Count. |  |  |  |  |  |  |  |  |  |  |
| Unusual and Physically | 18.95 | 18.42 | 18.95 | 15.79 | 17.89 | 16.32 | 27.37 | 60.00 | 74.74 | 96.84 |
| Anomalous activities. |  |  |  |  |  |  |  |  |  |  |
| Interpretation of | 25.00 | 31.07 | 32.50 | 18.93 | 17.14 | 13.93 | 39.29 | 64.29 | 79.64 | 97.51 |
| social context. |  |  |  |  |  |  |  |  |  |  |
| Understanding of | 21.92 | 23.63 | 21.23 | 15.07 | 13.70 | 14.73 | 27.40 | 47.26 | 66.44 | 95.55 |
| emotional context. |  |  |  |  |  |  |  |  |  |  |
| Interpretation of | 32.60 | 34.43 | 27.84 | 19.78 | 21.25 | 23.08 | 45.05 | 63.00 | 82.42 | 94.87 |
| visual context. |  |  |  |  |  |  |  |  |  |  |
| Average | 21.62 | 25.78 | 24.96 | 15.92 | 16.41 | 16.46 | 32.89 | 53.20 | 70.78 | 96.67 |

Video-LMMs. Both open-source and closed-source models are selected for the evaluation. Among the open-source models, we evaluate 7 recent Video-LMMs, including Video-LLaVA Lin et al. (), TimeChat Ren et al. (), MovieChat Song et al. (), LLaMA-ViD Li et al. (), VideoChat Li et al. () Video-ChatGPT Maaz et al. (), and Video-LLaMA-2 Zhang et al. (). For evaluating closed-source models, we use Gemini-Pro-Vision Google () and GPT-4V(vision) OpenAI (). Refer to the Appendix A for implementation details.

### 5.1 Main Experiments on CVRR-ES.

In Tab. 2, we present the evaluation results of Video-LMMs on the 11 dimension categories of the CVRR-ES benchmark. Below, we present several key findings.

Open Source Video-LMMs struggles on CVRR-ES benchmark. All open-source LMMs show inferior performance across the different evaluation dimensions of CVRR-ES. Interestingly, some of the earlier developed open-source Video-LMMs, like Video-LLaMA, VideoChat, and Video-ChatGPT, exhibit higher performance compared to more recent models such as Video-LLaVA, MovieChat, and LLaMA-VID. Overall, TimeChat achieves the highest performance of 32.89% averaged across the 11 evaluation dimensions among open-source LMMs, followed by VideoChat with a score of 25.78%.

Humans rank highest in CVRR-ES benchmark. Human studies achieve the highest performance on the CVRR-ES benchmark, with over 95% accuracy across all evaluation dimensions. Furthermore, these results suggest that the CVRR-ES QA pairs are answerable and suitable for benchmarking.

Closed source models perform competitively on CVRR-ES. As shown in Tab. 2, both Gemini and GPT4V surpass the performance of open-source models and achieve high gains across all evaluation dimensions. The competitive results of GPT4V and Gemini on complex video evaluation dimensions such as partial actions, non-existent action/scene depiction, and context-dependent categories show that these models have a more sophisticated understanding of the complex visual contents of videos and have strong capabilities to rectify misleading and confusing user questions. Overall, GTP4V improves over Gemini by 17.58% and provides an average accuracy of 70.78% on CVRR-ES.

### 5.2 Effectiveness of DSCP method for improving Video-LMMs performance

Figure: Figure 6: Video-LMMs with DSCP technique effectively improves their performance (gains are shown in green) on CVRR-ES benchmark.
Refer to caption: x6.png

We next integrate DSCP technique with Video-LMMs and present results on the CVRR-ES benchmark in Fig. 6. The results indicate that DSCP improves the model’s performance compared with models that use standard prompting (i.e., using only the question itself). These results suggest that prompting techniques in Video-LMMs can better guide models for improved reasoning and robustness. With DSCP, initially low-performing Video-LMMs such as Video-LLaVa, MovieChat, and LLaMA-Vid show much better relative gains and become competitive with other models. The highest relative gain of 184% is achieved by LLaMA-ViD, which moves from 7th place in the leaderboard to 2nd among the open-source models after utilizing DSCP prompting. We observe similar overall positive trends of using DSCP with closed-source model Gemini, which improves on the benchmark by an absolute overall gain of 5.02%. We provide more detailed results comparisons in Appendix C.

**Table 3: Prompting methods. DSCP stage 1 uses only the principled instructions designed in step 1, while DSCP (Both stages) uses the complete dual-step prompting technique.**
| Prompting Method | VideoChat | Video-LLaVA | MovieChat | LLaMA-VID | TimeChat |
| --- | --- | --- | --- | --- | --- |
| Standard prompting | 25.78 | 15.92 | 16.41 | 16.46 | 32.89 |
| Chain of Thought (CoT) prompting | 22.44 | 25.87 | 15.89 | 29.68 | 39.57 |
| DSCP (Stage 1) | 38.07 | 32.12 | 28.05 | 25.13 | 33.04 |
| DSCP (Both stages) | 47.92 | 37.93 | 35.87 | 46.85 | 39.45 |

### 5.3 Different prompting techniques.

We study the contribution of each step of DSCP and compare it with chain-of-thought prompting Wei et al. (). The results for the top 5 performing Video-LMMs are shown in Tab. 3. Chain-of-thought prompting improves over the standard prompting technique in 3 out of 5 Video-LMMs, suggesting that prompting techniques from NLP literature can effectively guide multi-modal Video-LMMs to enhance reasoning and robustness. Next, we ablate on the first step of DSCP prompting, which uses the principled instructions of DSCP step 1 as a prefix alongside the actual user question. Using the first step prompting technique of DSCP substantially improves model performance on all Video-LMMs, suggesting the effectiveness of the principled prompt instructions designed specifically for Video models. DSCP with both steps, which integrates an additional thinking step in the prompting step, further improves the results and provides the highest results on 4 out of 5 Video-LMMs.

### 5.4 Main findings and Qualitative Results

Based on the results of Video-LMMs on CVRR-ES, we draw key findings and show qualitative results. These insights can serve as valuable guidance for developing the next generation of Video-LMMs, aiming to make them more robust and reliable when deployed in real-world applications.

Models excelling at standard VQA benchmarks struggle on CVRR-ES benchmark.
Our analysis in Sec. 5.1 reveals that the latest open-source Video-LMMs, such as Video-LLaVA, MovieChat, and LLaMA-VID, perform less effectively on the CVRR-ES benchmark compared to Video-LMMs that were introduced earlier in the community, such as VideoChat and Video-ChatGPT. Interestingly, the same recent models demonstrate superior performance on general video comprehension benchmarks. This discrepancy suggests that current VQA benchmarks, like ActivityNet-QA Yu et al. () and MSRVTT Xu et al. (), do not adequately correlate with the complex video reasoning and robustness scenarios highlighted in our benchmark. Consequently, this also indicates that most newer Video-LMMs are heavily trained to excel on the general video comprehension benchmarks while reducing their generalizability, reasoning, and robustness capabilities.

Over-affirmative behavior of open-source Video-LMMs. Another important observation about open-source models is their tendency to exhibit excessively positive and affirmative responses. As shown in Fig. 8, open-source Video-LMMs consistently respond with "Yes" even when faced with confusing questions that describe non-existent actions and objects. This highlights the vulnerability of these models when interacting with users in real-world scenarios.
In our CVRR-ES benchmark, open-source models are particularly vulnerable to our evaluation dimensions of "Non-existent actions with the existent scene" and "Non-existent actions with the non-existent scene" compared to closed-source models. These models lack negation and self-rectification capabilities, especially when users provide misleading or confusing questions. We conjecture that such behavior arises due to the absence of negative instruction tuning pairs during the training of Video-LMMs.

Tendency towards activity completion. Most open-source Video-LMMs have shown weak performance on the evaluation dimension of partial actions in CVRR-ES, which contains videos focusing on incomplete or atomic actions. To further analyze the models’ behavior, we show qualitative results on such videos in Fig. 8. It can be observed that most open-source models tend to complete actions, even when only part of the action is provided in the video. For instance, Video-LLaVA struggles to reason over the video and describes the man as kicking the soccer ball, while the action in the video stops at the point of the man placing his foot beside the ball. We observe similar behavior in other Video-LMMs. Upon examining the fine-tuning strategies Maaz et al. (); Liu et al. (), we find that almost all models are trained on end-to-end actions-based instruction-tuning data, causing them to generate complete action descriptions at inference. This tendency highlights the vulnerability of Video-LMMs after deployment, as real-world scenarios often involve atomic, sub-atomic, and general actions alike. To improve the performance of Video-LMMs, it is crucial to incorporate diverse action types during training, including partial and incomplete actions.

Weak Generalization to extreme OOD videos. The evaluation dimension of unusual and physically anomalous activities in CVRR-ES resembles extreme out-of-distribution video examples. With the exception of GPT4V and Gemini, Video-LMMs struggle with this dimension, indicating weak generalizability towards OOD videos containing the coexistence of unusual objects and activities that are extremely rare in typical videos. For instance, Video-LLaVA in Fig. 10 describes a person falling on the street, while the video actually shows the person performing an optical illusion. To be responsibly deployed in real-world applications, where OOD actions occur more frequently, Video-LMMs need to be trained to perform more robustly on OOD samples. This may involve incorporating diverse and atypical examples in the training data to improve the model’s ability to handle unusual situations.

Limited understanding of temporal order in complex videos.
The CVRR-ES benchmark results show that Video-LMMs perform relatively better on the fine-grained action dimension compared to the time-order understanding dimension. While these models can accurately identify fine-grained actions, they struggle with comprehending the correct temporal order of these actions within a video. This limitation can lead to misinterpretations of the underlying information depending on temporal order. We present failure cases of this dimension in Fig. 10. For building more advanced world-centric Video-LMMs, it is crucial to enhance their ability to process and interpret event sequences accurately.

Video-LMMs struggles in understanding the emotional and social context.
For more reliable interaction between Video-LMMs and humans in practical scenarios, these models should comprehend the spatio-temporal scenes with social and contextual reasoning capabilities similar to humans. The lower performance of Video-LMMs on social and emotional contextual dimensions in CVRR-ES highlights their limitations and lack of understanding of scenes based on contextual cues. For instance, as shown in Fig. 11 (bottom row), GPT-4V struggles to comprehend a scene where a worker is attempting to prevent shoes from getting wet due to the rain by moving them under the shade. Instead, GPT-4V provides a response that contradicts the social cues present in the video.

## 6 Conclusion

Given the expanding role of Video-LMMs in practical world-centric applications, it is vital to ensure that these models perform robustly and exhibit human-like reasoning and interaction capabilities across various complex and real-world contexts. In this work, we present the CVRR-ES benchmark for Video-LMMs, aiming to evaluate Video-LMMs on these very fronts. Through extensive evaluations, we find that Video-LMMs, especially open-source ones, exhibit limited robustness and reasoning capabilities over complex videos involving real-world contexts. Based on our analysis, we formulate a training-free prompting technique that effectively improves the performance of Video-LMMs across various evaluation dimensions of the CVRR-ES benchmark. Furthermore, we analyze and investigate the failure cases of Video-LMMs on the CVRR-ES benchmark and deduce several important findings. We hope that the CVRR-ES benchmark, accompanied by our extensive analysis, will contribute towards building the next generation of advanced world-centric video understanding models.

Figure: Figure 7: Over affirmative behaviour. Most open-source Video-LMMs exhibit overly affirmative behavior by consistently agreeing with user questions, even when the questions are confusing or inaccurate.
Refer to caption: x7.png

Figure: Figure 9: Weak generalization on OOD videos. Open-source Video-LMMs struggle to correctly reason over videos containing rare and unusual actions.
Refer to caption: x9.png

Figure: Figure 11: Limited contextual understanding. Most Video-LMMs exhibit a weak understanding of complex videos that contain emotional (e.g., an angry player in the top video) and social cues (e.g., a person saving shoes from getting wet due to rain in the bottom video).
Refer to caption: x11.png

## Appendix A Implementation details

For open-source models, we follow their default best inference settings and hyperparameters. To evaluate Gemini and GPT-4V, we utilize their official APIs. Full videos are directly passed to Gemini Vision-Pro, as its API (using Google Cloud vertexai framework) inherently supports video inputs. However, as GPT-4V does not inherently support videos, we uniformly sample 8 frames for each video which are passed into GPT API along with user questions. For each model under evaluation, we generate responses to the questions independently and without retaining the chat history. For the evaluation results of Video-LMMs on the CVRR-ES QA pairs, we utilize GPT-3.5 as a judge in all of our experiments.

## Appendix B Additional details on CVRR-ES Benchmark.

More details on annotation process.
Expert human annotators are assigned to annotate the videos of the CVRR-ES benchmark. To ensure consistency and high quality, we provide annotation instructions to annotators, who generate captions accordingly. For instance, when annotating videos for the category of non-existent actions with non-existent scene depictions, annotators are instructed to include information about all actions and attribute information about objects. This ensures that each caption provides sufficient information to be effectively used in the next stage of the QA generation process.
To verify the quality and correctness of video captions, we perform two separate iterations of verification and rectification (if applicable) of each video caption curated in the previous iteration.

Question-Answer generation process.
We use LLM assisted question-answer generation process, to curate question-answer pairs using ground-truth video captions in the CVRR-ES benchmark. An illustration of this process is shown in Fig. 14.

Quality of QA pairs.
We present examples of QA pairs from the CVRR-ES benchmark in Table 4. Our QA pairs are of high quality and aim to comprehensively test the understanding of Video-LMMs against reasoning and robustness criteria across multiple evaluation dimensions. To quantitatively assess the quality of the benchmark, we establish a quality assessment procedure similar to the one in Gandhi et al. (). We randomly sample 1120 QA pairs, which encompass all videos of the CVRR-ES benchmark, and request human experts to evaluate the quality of each QA pair by answering the following questions: (1) "Does the QA pair correctly represent the evaluation dimension category under which it falls?" (possible answers: "Yes", "No") (2) Can the question be correctly answered given only the video content? (possible answers: "Agree", "Disagree") and (3) Is the corresponding paired ground-truth answer correct? (which will be used during evaluation as ground truth) (possible answers: "Yes", "No"). On average, the answer of experts for the first question was "Yes" for 98.84% of the times. For the second and third questions, the averaged answer was "Agree" and "Yes" for 100% and 99.91% of the times, respectively.

**Table 4: Examples of the question-answer pairs in the CVRR-ES benchmark for various complex video evaluation dimensions.**
| Evaluation Dimensions | Sample Question-Answer pairs |
| --- | --- |
| 1. Multiple actions in<br>a single video | Q. Does the person stand up to welcome the cat or remain seated throughout their interaction?<br>A. The person remains seated throughout their interaction with the cat.<br>Q. What is the next action performed by the person after using the laptop?<br>A. The action directly after using the laptop is placing a bag in the refrigerator. |
| 2. Fine-grained action<br>understanding | Q. At any point in the video, does the man use the thread to sew fabric?<br>A. No, the man uses the thread to create loops and demonstrate tying a knot; there is no depiction of sewing fabric.<br>Q. What action is performed by the person’s hands in the video?<br>A. The person’s hands are shown plugging a black USB charging cable into the charging port. |
| 3. Partial actions | Q. What is happening in the video?<br>A. The video shows the door of a red car and a person’s hand reaching to the handle of the car …<br>Q. Does the video include a moment where the snack is replaced to its original position on the right?<br>A. No, the video concentrates on the initial action of moving the snack from the right to the left, without … |
| 4. Time order understanding | Q. Is the video showing the activity of taking out liquid from the soda can?<br>A. No, the video does not show the activity of taking out the liquid from the soda can. The video shows …<br>Q. Is the person running in clockwise direction or anticlockwise direction on the race track?<br>A. The person is running in anticlockwise direction in the video. |
| 5. Non-existent actions with<br>existent scene<br>depictions | Q. After going through the bag, does the person meticulously clean the area around the sink?<br>A. No, the person does not clean the area around the sink after going through the bag. The video focuses …<br>Q. What is the reaction of the audience when the keynote speaker delivers his speech?<br>A. The scene does not include a moment where a keynote speaker is delivering a speech … |
| 6. Non-existent actions with<br>non-existent scene<br>depictions | Q. How do the children interact with the flowers in the video?<br>A. There are no children interacting with the flowers depicted in the video. The footage is committed to displaying …<br>Q.What is the reaction of the child playing in the corner when the dog runs past?<br>A. There is no child playing in the corner or any reaction to the dog runing past … |
| 7. Continuity and Object<br>Instance Count | Q. How many unique sunglasses appear throughout the video?<br>A. As there are 4 persons in the car wearing the sunglasses, the number of unique sunglasses is 4.<br>Q. Did the attire of both men remain the same upon re-entering the frame the second time?<br>A. No, the attire of both men did not remain the same upon re-entering … |
| 8. Unusual and Physically<br>Anomalous activities | Q. Is the person showcasing walking or running movements to reach an elevated position in the video?<br>A. No, the person did not walk or run; they ascended and floated in the air through what …<br>Q. How the person is able to fly over the water?<br>A. The person is using a flyboard system attached to his shoes using which he is flying over the water. |
| 9. Interpretation of<br>social context | Q. What was the response of the crowd when the girl landed the water bottle vertically?<br>A. the crowd applauded to showcase appreciation for her perseverance and success.<br>Q. What is the primary reason the boy touches the ashes before placing his hand on the goat?<br>A. The boy uses the ashes to warm the goat, indicating his primary motive is care and providing warmth. |
| 10. Understanding of<br>emotional context | Q. Identify if the emotional context of the video is negative, based on the described actions and reactions?<br>A. The emotional context of the video is not negative; it is overwhelmingly positive. The indicators of happiness, …<br>Q. Identify the nature of the interaction between the two individuals. Is it professional, hostile, or friendly?<br>A. The interaction is friendly. This is evidenced by the warm hug and the handshake, … |
| 11. Interpretation of<br>visual context | Q. Does the person in the video undergo a real physical transformation?<br>A. No, … They simply remove a rubber mask that made them look like a man, revealing that they are actually a woman.<br>Q. Identify the unusual behavior depicted between a predator and its usual prey in the video.<br>A. A cat plays and sleeps with chicks instead of hunting them. This showcases an unusual peace … |

Human Evaluation.
To verify that the QA pairs in the CVRR-ES benchmark are reasonably answerable and to establish a benchmark for human performance, we conduct a human evaluation. Two human experts (authors) are instructed to watch the video corresponding to each question and provide a free-form answer. The predictions of the human experts for all QA pairs are assessed using an LLM-assisted evaluation. Individual final scores are averaged to mitigate potential bias from a single human evaluator. The results of the human evaluation are presented in the main paper in the experiments section (Sec. 5.1).

## Appendix C Further Analysis and Additional Results for DSCP Method.

### C.1 Discussion on DSCP method.

We note that Video-LMMs are often able to correctly comprehend the video content and produce factual responses. However, they are extremely sensitive to user textual prompt inputs and exhibit highly over-affirmative behavior. These attributes of Video-LMMs can lead to hallucinations and wrong output responses, especially when the user asks reasoning-based, low-quality, confusing, or misleading questions.

Our Dual-Step Contextual Prompting technique aims to address these limitations of Video-LMMs by explicitly delineating the contextual reasoning information retrieval from the user question answering using a two-step prompting technique. This strategy effectively eliminates the influence of the question, leading to more grounded overall responses.

Figure: Figure 12: Effect of different prompts on Video-LLaVA. Row 1: Video-LLaVA often provides factual and correct information about the input video when prompted with simple and clear questions. Row 2 & 3: However, the model struggles to remain factual when the question becomes reasoning-based, confusing, or misleading, mainly due to its over-affirmative behavior. Row 4 & 5: Our DSCP method utilizes contextual reasoning information in the first step prompting, independent of the user question, and uses it as conditioning information in the second step, leading to more grounded and factual responses to user questions.
Refer to caption: x12.png

In Fig. 12, we show the sensitivity of Video-LMMs to textual prompts and the impact of each step in the DSCP prompting technique. It can be observed that prompting the model with simple questions, such as ’Describe the video content’ or ’What is happening in the video?’ leads to correct responses. However, as the user asks a reasoning-based question or a tricky question, the model struggles to reason properly and hallucinates due to an over-affirmative response. Finally, we generate the response using the DSCP method. The first step independently retrieves contextual reasoning information using principled prompt instructions, followed by asking the user a question conditioned on both the factual information retrieved earlier and the input video. We observe that integrating both steps of DSCP prompting injects improved reasoning and self-rectification capabilities into Video-LMMs.

### C.2 Detailed comparison results.

In the main paper, we presented overall results comparisons between Video-LMMs utilizing the Dual-Step Contextual Prompting (DSCP) technique. Here, we show the per evaluation dimension performance of Video-LMMs when utilizing DSCP technique in Tab. 5. The results indicate that Video-LMMs with DSCP technique provide substantial performance improvements across various evaluation dimensions in the CVRR-ES benchmark.

While DSCP prompting reduces the performance for the evaluation dimension of time-order understanding for a few Video-LMMs such as VideoChat, Video-ChatGPT, and Gemini, the overall relative performance improvements are notable for the majority of the models. DSCP technique improves the performance of Video-LMMs across most evaluation dimensions. In particular, DSCP shows the highest gains for the evaluation dimensions of physically anomalous, contextual videos, fine-grained actions, and partial actions, demonstrating the model’s improved reasoning capabilities without any additional training.
For evaluation dimensions involving explicit misleading user questions, such as non-existent actions with non-existent scene depiction, DSCP substantially improves the model’s performance. For instance, VideoChat improves from 14.38% to 58.33% on the same evaluation dimension, corresponding to relative gains of over 300%. This suggests that DSCP prompting acts as an additional filter layer that guides the model towards robust and grounded behavior.

**Table 5: Video LMMs evaluation results using our Dual-Step Contextual Prompting (DSCP) Technique. Video LMMs with DSCP technique effectively improves their reasoning and robustness capabilities on complex video-evaluation dimensions in CVRR-ES. Absolute gains over the standard prompting are shown in green.**
| Benchmark Category | Video-LLaMA2 | VideoChat | Video-ChatGPT | Video-LLaVA | MovieChat | LLaMA-VID | TimeChat | Gemini-V Pro |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Multiple Actions in | 32.39 | 38.99 | 32.70 | 37.74 | 27.36 | 39.62 | 32.08 | 49.37 |
| single video. | (+15.41) | (+15.09) | (+5.03) | (+22.01) | (+14.78) | (+21.70) | (+3.77) | (+6.29) |
| Fine-grained action | 35.65 | 39.57 | 28.26 | 33.48 | 41.74 | 41.74 | 40.87 | 51.15 |
| understanding. | (+6.09) | (+6.09) | (+1.30) | (+8.26) | (+18.26) | (+15.65) | (+1.74) | (-0.46) |
| Partial | 39.32 | 50.49 | 34.95 | 47.57 | 33.98 | 52.91 | 55.34 | 61.17 |
| actions. | (+14.56) | (+17.48) | (+12.14) | (+33.98) | (+12.62) | (+38.35) | (+5.83) | (-6.31) |
| Time order | 28.29 | 28.95 | 23.68 | 30.26 | 23.68 | 31.58 | 32.24 | 43.42 |
| understanding. | (+11.84) | (-2.63) | (-3.95) | (+9.21) | (+7.24) | (+11.84) | (-1.97) | (-1.97) |
| Non-existent actions with | 39.86 | 65.94 | 31.16 | 47.10 | 39.13 | 51.45 | 30.43 | 68.12 |
| existent scene. | (+29.71) | (+50.72) | (+7.97) | (+42.03) | (+34.06) | (+48.55) | (+7.25) | (+10.87) |
| Non-existent actions with | 40.97 | 58.33 | 30.56 | 42.36 | 35.42 | 56.94 | 29.17 | 71.94 |
| non-existent scene. | (+27.78) | (+43.75) | (+13.19) | (+38.89) | (+23.61) | (+50.00) | (+15.28) | (+22.30) |
| Continuity and Object | 31.07 | 38.42 | 31.64 | 32.77 | 35.59 | 37.85 | 38.98 | 46.33 |
| instance Count. | (+2.82) | (+14.12) | (+3.23) | (+11.30) | (+15.82) | (+12.99) | (+4.52) | (+10.17) |
| Unusual and Physically | 38.95 | 50.00 | 33.16 | 31.58 | 40.53 | 40.53 | 37.89 | 65.26 |
| Anomalous activities. | (+20.00) | (+31.58) | (+14.21) | (+15.79) | (+22.63) | (+24.21) | (+10.53) | (+5.26) |
| Interpretation of | 47.50 | 58.21 | 48.93 | 43.93 | 44.29 | 64.29 | 52.86 | 72.14 |
| social context. | (+22.50) | (+27.14) | (+16.43) | (+25.00) | (+27.14) | (+50.36) | (+13.57) | (+7.86) |
| Understanding of | 35.27 | 41.10 | 30.14 | 24.66 | 32.88 | 37.67 | 33.56 | 50.68 |
| emotional context. | (+13.36) | (+17.47) | (+8.90) | (+9.59) | (+19.18) | (+22.95) | (+6.16) | (+3.42) |
| Interpretation of | 47.50 | 58.21 | 48.93 | 43.93 | 44.29 | 64.29 | 52.86 | 72.14 |
| visual context. | (+13.55) | (+22.71) | (+19.78) | (+26.01) | (+18.68) | (+37.73) | (+5.49) | (-2.20) |
| Average | 37.77 | 47.92 | 33.89 | 37.93 | 35.87 | 46.85 | 39.45 | 58.22 |
|  | (+16.15) | (+22.14) | (+8.93) | (+22.01) | (+19.46) | (+30.39) | (+6.56) | (+5.02) |

The overall performance improvements of Video-LMMs with DSCP suggest that prompting techniques can effectively steer the behavior of Video-LMMs for enhanced reasoning and robustness over videos. Although DSCP shows promising results, the net performance of Video-LMMs is still far from satisfactory, which demands more advanced techniques to further enhance their capabilities, especially for open-source models.

## Appendix D Ablation Studies.

Our CVRR-ES evaluation benchmark utilizes key design choices. In this section, we present several ablation studies to validate the effectiveness of these design choices.

Figure: Figure 13: Prompt used to instruct LLM as a judge for evaluating Video-LMM responses on CVRR-ES benchmark. We employ GPT-3.5 turbo as the choice of LLM. The system prompt is shown in blue while the main prompt is shown in green.
Refer to caption: x13.png

Alignment of LLM as the Judge with Human evaluators.

We utilize LLMs such as GPT-3.5 as a judge for evaluating Video-LMMs on the CVRR-ES benchmark. In this study, we compare how closely LLM accuracy scores align with human evaluations. We assign two expert human evaluators to independently evaluate human performance by manually evaluating and scoring each candidate’s answer.
We observe that the human evaluation results by LLM have an alignment percentage of 95.36%. This means that for 4.64% of QA pairs, there was a mismatch between LLM judgment and human judgment. The 95%+ alignment rate with GPT-3.5 is encouraging, and we conjecture that future LLMs will exhibit further alignment with human evaluations.

LLM Judgement improves by generating explanations.
Our default evaluation prompt as shown in Fig. 13 requires the Judge LLM to generate a correct/incorrect flag, an answer quality score (ranging from 0 to 5), and the rationale behind the quality score and the correct/incorrect flag. The alignment score with human evaluators for this instruction prompt is 95.36%. Previously, we utilized the LLM Judge instruction prompt based on prior works Maaz et al. (); Liu et al. (); Song et al. (), which do not request the model to provide the decision rationale. With their prompt, we observe that the Judge’s alignment with human evaluators is 89.63%. This suggests that requiring LLM Judge decisions to be accompanied by corresponding reasons yields more reliable evaluation results.

Figure: Figure 14: An illustration of the QA pair generation process using LLMs for our CVRR-ES benchmark. Human-generated video captions are input to LLMs which are instructed to generate diverse QA pairs encompassing both textual robustness and reasoning dimensions.
Refer to caption: x14.png

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 6
presentation: 2
questions: Please see weakness.
soundness: 3
strengths: 1 Topic is good. Video understanding is an important problem in the multimodal research. 

2 Contribution is good. Instead of investigating the exsiting comprehension abilities, the authors propose to focus on reasoning capabilities over complex videos and robustness to user prompts. Both aspects are important parts for video understanding evaluation.
summary: In this paper, the authors present a Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES) for assessing the performance of Video-LMMs on 11 diverse real-world video dimensions. Moreover, by evaluating 11 recent Video-LMMs, they further develop a training-free Dual-Step Contextual Prompting (DSCP) technique to boost  enhance their performance on the proposed benchmark.
weaknesses: 1 The build-up of this benchmark is similar to MVBench. In Figure 1,  It is not correct that, MVbench does not contain "in the wild" and "contextual dependency".  Moreover, MVbench contains the reasoning QA. Please further make the clarification on the difference, the robustness to user prompt? More explanation should be added.

2 Dual Step Contextual Prompting is straightforward.

3 The firgures are way to small. They are not quite easy to see.

---

confidence: 4
contribution: 1
initial_rating: 3
presentation: 2
questions: What was the number of frames inputted for the open-source models during evaluation?
Could the authors provide a summary of the complex reasoning evaluation dimensions?  The 11 dimensions seem quite detailed; grouping some of them into broader, more general capabilities could make the benchmark easier to understand and more accessible for users, potentially aiding wider adoption.
soundness: 3
strengths: The benchmark’s 11 evaluation dimensions are challenging and effectively assess model capabilities in complex scenarios. 
The 10 principled instructions in DSCP significantly enhance the ability of Video LMMs to handle these complex tasks more effectively.
summary: This paper introduces a novel benchmark suite, the Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES), designed to comprehensively assess the robustness and reasoning capabilities of Video Large Language Multimodal Models (Video-LMMs) in complex video understanding and real-world contexts. The authors also propose a training-free Dual-Step Contextual Prompting (DSCP) method, which leverages a two-stage prompting strategy to enhance model reasoning and robustness. By providing contextual cues, DSCP helps guide models toward more accurate responses and reduces misinterpretations of confusing or misleading queries. Experimental results demonstrate that current Video-LMMs often display overly affirmative responses when handling misleading or negatively framed text queries. Additionally, the experiments reveal challenges in these models' understanding of emotional and social contexts, as well as incomplete actions. The DSCP technique substantially improves model performance across these challenging dimensions.
weaknesses: The contribution to the field of Video LLMs is somewhat limited. Much recent work in the community has focused on evaluating reasoning capabilities, with benchmarks like MVBench already addressing reasoning alongside a broader range of evaluation angles. Given this context, focusing primarily on complex reasoning as a contribution may have limited impact on advancing the field and may not fully meet the standards expected for ICLR.
Using GPT-3.5 as the evaluation LLM also appears somewhat outdated. 
Furthermore, achieving the complex reasoning that the paper discusses requires fine-grained understanding of video content, which typically demands inputting a substantial number of video frames into the model. With a few frames, handling complex reasoning tasks becomes challenging. However, the paper does not specify the number of frames input to the open-source models, and intuitively, increasing the number of frames could enhance Video LLMs’ understanding and reasoning over video content.
Lastly, while DSCP is presented as a novel method, it is relatively basic, relying on a two-stage text prompt that appears somewhat rough and may not constitute a substantial contribution.

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confidence: 5
contribution: 1
initial_rating: 3
presentation: 3
questions: - can you calculate the correlation between scores on this benchmark to existing ones (e.g., r^2) - so we can see how different the proposed benchmark is from existing ones?
- Has a multiple-choice alternative been tested, and if yes, why did you opt not to use this?
- how did you verify that this benchmark requries a 'higher' level of reasoning compared to existing benchmarks?
- does DSCP impact performance more on the proposed benchmark compared to existing benchmarks?
soundness: 3
strengths: - shows that correct prompting on video benchmarks can improve performance
- proposes a human-verified benchmark (not fully automatic pipeline), which focuses on CoT.
summary: The authors introduce a new video-lmm benchmark, aiming to target specifically reasoning vs perception. They also propose a CoT prompting technique, which was shown improves performance on benchmarks.
weaknesses: - lacks comparison to many popular benchmarks: Video-MME, PerceptionTest, MLVU, LongVideoBench, EgoSchema, TempCompass.
- note that some of these benchmarks have a higher overlap with the proposal
- Uses LLM as a judge. This makes evaluation expensive, especially at scale.

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confidence: 4
contribution: 1
initial_rating: 3
presentation: 2
questions: Please see the "Weaknesses".
soundness: 2
strengths: - The motivation and issues discussed in the paper are valuable. By constructing the CVRR-ES to assess the reasoning ability and robustness of existing Video-LLMs on real-world videos, the benchmark is very detailed in the design of video evaluation dimensions.

- The construction of a human baseline provides a good reference for the evaluation of Video-LLMs.
summary: This paper proposes a new complex video reasoning and robustness benchmark, CVRR-ES, to assess Video-LLMs. CVRR-ES includes 2,400 high-quality open-ended question-answer pairs, spanning 214 high-quality videos, covering 11 video evaluation dimensions. This work evaluates 11 models, including closed-source and open-source Video-LLMs and establishes a human baseline. It also introduces a training-free dual-step contextual prompting method, DSCP, to enhance the performance of Video-LLMs.
weaknesses: - I express skepticism about whether the number of videos in the benchmark can achieve a robust assessment. The CVRR-ES benchmark includes only 214 videos, with the shortest video being just 2 seconds. Upon reviewing several videos from the anonymous link, I noticed a significant proportion of short videos. I question whether such short videos can adequately cover 11 categories. Moreover, current work that focuses solely on designing Video-LLMs, without specifically constructing evaluation benchmarks, provides a much larger number of assessment videos than the 214 included in CVRR-ES, for example, Tarsier [1].

- As mentioned in the previous question, the distribution of videos of different lengths within the benchmark is crucial for the assessment of reasoning ability and robustness, and the paper does not provide relevant explanations. The authors should include a table showing the distribution of video lengths across the dataset, and explain how they ensured a balanced representation of different video lengths across the 11 categories.

- In the motivation, it is mentioned that the goal is to build human-centric AI systems. Does the paper's reflection on this point merely consist of providing a human baseline? I think that offering more fine-grained visual examples would be more helpful for human-AI comparisons.

- I think that the contribution of the DSCP is somewhat overstated and lacks novelty. Such prompt engineering-based methods have already been applied in many works for data generation, model evaluation, and other stages. The introduction and ablation experiments of this technology in the paper seem redundant.

- The discussion on DSCP occupies a significant portion of the experimental analysis. I think that the current analysis provided in the paper lacks insight and does not fully reflect the value of CVRR-ES, especially in terms of human-machine comparison.

- The phrase should be "there exist a few limitations" instead of "there exist few limitations" in line 520.

- The paper does not provide prompt templates for all the closed-source and open-source Video-LLMs used, which will influence the reproducibility.

The problems discussed in this paper are valuable, but the most crucial aspects of benchmark construction and evaluation are not entirely convincing. Instead, a significant amount of space is dedicated to introducing the DSCP method. I don't think it meets the acceptance standards of ICLR yet. I will consider modifying the score based on the feedback from other reviewers and the authors' responses.

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[1] Wang J, Yuan L, Zhang Y. Tarsier: Recipes for Training and Evaluating Large Video Description Models[J]. arXiv preprint arXiv:2407.00634, 2024.