EgoNight: Towards Egocentric Vision Understanding at Night with a Challenging Benchmark
Authors: Deheng Zhang
Yuqian Fu
Runyi Yang
Yang Miao
Tianwen Qian
Xu Zheng
Guolei Sun
Ajad Chhatkuli
Xuanjing Huang
Yu-Gang Jiang
Luc Van Gool
Danda Pani Paudel
Venue: ICLR 2026 Poster (2026)
Keywords: Egocentric vision; Benchmark; MLLMs; VQA
Primary area: datasets and benchmarks

Abstract: Most existing benchmarks for egocentric vision understanding focus primarily on daytime scenarios, overlooking the low-light conditions that are inevitable in real-world applications. To investigate this gap, we present EgoNight, the first comprehensive benchmark for nighttime egocentric vision, with visual question answering (VQA) as the core task. A key feature of EgoNight is the introduction of day–night aligned videos, which enhance night annotation quality using the daytime data and reveal clear performance gaps between lighting conditions. To achieve this, we collect both synthetic videos rendered by Blender and real-world recordings, ensuring that scenes and actions are visually and temporally aligned. Leveraging these paired videos, we construct EgoNight-VQA, supported by a novel day-augmented night auto-labeling engine and refinement through extensive human verification. Each QA pair is double-checked by annotators for reliability. In total, EgoNight-VQA contains 3658 QA pairs across 90 videos, spanning 12 diverse QA types, with more than 300 hours of human work. Evaluations of the state-of-the-art multimodal large language models (MLLMs) reveal substantial performance drops when transferring from day to night, underscoring the challenges of reasoning under low-light conditions. Beyond VQA, EgoNight also introduces two auxiliary tasks, day–night correspondence retrieval and egocentric depth estimation at night, that further explore the boundaries of existing models. We believe EgoNight-VQA provides a strong foundation for advancing application-driven egocentric vision research and for developing models that generalize across illumination domains.  The code and data can be found in https://github.com/dehezhang2/EgoNight

Published as a conference paper at ICLR 2026
EGONIGHT: TOWARDS EGOCENTRIC VISION UNDERSTANDING AT NIGHT WITH A CHALLENGING BENCHMARK
Deheng Zhang"; Yuqian Fu Runyi Yang', Yang Miao', Tianwen Qian', Xu Zheng 1.3 Guolei Sun', Ajad Chhatkuli Xuanjing Huang". Yu-Gang Jiang", Luc Van Gool', Danda Pani Paudel' INSAIT, Sofia University "St. Kliment Ohridski" "East China Normal University
"HKUST(GZ) Nankai University
Fuuan University
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Figure Overview of the Nighight. EgoNight integrates diverse video sources spanning synthetic environments, real-world indoor and outdoor scenes, recorded under both daytime and nighttime conditions, with spatial and temporal aligmment. consists of three benchmarks: (i) egocentric VQA as the primary focus, (ii) day-night correspondence re- trieval, and (iii) egocentric depth estimation, all targeting the challenges of low-light egocentric vision. The day- alignment (illustrated on the right with VQA examples) enables rigorous analysis of illumination gaps in MLLMs.
ABSTRACT
Most existing benchmarks for understanding egocentric vision focus primarily on daytime scenarios, overlooking the low-light conditions that are inevitable in real-world applications. To investigate this gap, we present EgoNight the first comprehensive benchmark for nighttime egocentric vision, with visual question answering (VQA) as the core task. A key feature of EgoNight is the introduction of day-night aligned videos, which enhance night annotation quality using the daytime data and re- veal clear performance gaps between lighting conditions. To achieve this, we collect both synthetic videos rendered by Blender and real-world recordings, ensuring that scenes and actions are visually supported by
and temporally aligned. Leveraging these paired videos, we construct EgoNight- a novel augmentedt night auto-labeling engine and refinement through extensive human verifica- tion. Each QA pair is double-checked by annotators for reliability. In total, EgoNight- VQA contains 3658 QA pairs across 90 videos. spanning 12 diverse QA types, with more than 300 hours of hu- man work. Evaluations of the state-of-the-an multimodal large language models (MLLMs) reveal substantial performance drops when transferring from day to night, underscoring the challenges of reasoning under low-light conditions. Beyond VQA, EgoNight also introduces two auxiliary tasks, day-night correspondence retrieval and egocentric depth estimation at night, that further explore the
means equal contribution; ř denotes corresponding author.

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Published as a conference paper at ICLR 2026
boundaries of existing models. We believe EgoNight- VQA provides a strong foundation for advanc- ing application-drivee egocentric vision research and for developing models that generalize across illumination domains The code and data can be found in hhtpss//giihbboooomdddd/////////
1 INTRODUCTION
With the rapid development of wearable devices, egocentric vision understanding has become increasingly important. Unlike third-person vision, egocentric perception naturally aligns with the way humans perceive, understand, and interact with the world. A robust egocentric vision system can not only serve as an intelligent assistant in daily activities Yang et al. (2025) but also play crucial role in embodied Al and robotic learning [i al. (2025a); Kareer et al. 2025). Beyond these general applications, egocentric vision holds unique potential for assisting specific user groups such as people who are blind or visually impaired Xiao et al. (2025), or physically disabled Zhhagg et al. (2023a), enabling technologies that enhance navigation, accessibility, and real-time scene understanding.
Significant efforts have been made to advance egocentric vision understanding, including the construction of large- scale ego-centric datasets such as SPIC-KITCHENS Damen et al. (2020), Ego4D Grauman et al. (2022). and Ego-
Exo4D Graumann et al. (2024); the design of diverse and challenging benchmarks such as EgoTaskQA Jia et al. (2022).
EgoSchema Mangalam et al. (2023), and EeooTempo Plizzari et al. (2025): and the development of egocentric mul- timodal large language models (MLLMs) such as Ego Pramanick et al. (2023), EgoGPT Yang et al. (2025). and Exo2Ego Zhang et al. (225a)) Despite these advances, almost all prior works focus daytime scenarios with favorable lighting. In contrast, real-world egocentric systems, for example, intelligent personal assistants for naviga- tion, must operate at night, under low light, uneven illumination, and severely limited visibility. This motivates us to investigate egocentric vision at night, focusing on complex scene understanding and reasoning tasks.
A central challenge in constructing such a benchmark lies in obtaining suitable video sources that capture the character- isties of nighttime environments and developing annotation methods that ensure high labeling quality. To address this, we place particular emphasis on day-night aligned videos, which not only allow us to leverage daytime data to annotate nighttime videos, but also enable rigorous performance comparisons across day and night lighting conditions. How- ever, in practice, collecting perfectly aligned night pairs in the real world is highly non-trivial. To overcome this, we leverage Blender Iraci (2013), where scene layouts, camera trajectories, and lighting can be precisely controlled, enabling the synthesis of the desired videos. This produces EgoNight-Synthetic,, a collection of 50 ideally aligned egocentric pairs spanning diverse and complex indoor scenarios with varying illumination levels. To complement synthetic data with real-world evidence, we design a video-guiee guided recording protocol to construct EgoNight which contains 20 pairs of real-world egocentric videos with spatially and temporally aligned day-night counterparts. These videos cover realistic use cases (e.g.. "Where did I put my keys?", "How much is the item saw in the gro- cery shop?"), spanning both indoor and outdoor environments under diverse illumination sources such as streetlights, flashlights, and candles. Finally, we incorporate 20 nighttime videos from the Oxford Day-and-Night dataset WWang] et al. (2025b), termed Night-OOoo,,,,, which serve as an additional testbed despite lacking day-night alignment. Together, these three video sources constitute our EgoNight dataset, which is the first egocentric dataset providing day-night aligned correspondences, as mainly summarized in Fig.
The videos in EgoNight pave the way for constructing challenging benchmarks to evaluate the capabilities of existing models. Among many egocentric tasks, we focus on the egocentric video question answering, a flagship task that best reflects high-level understanding in egocentric vision. Specifically, to comprehensively evaluate model abilities, we first propose a diverse set of QA types, spanning well-studied tasks (c.g., object recognition, spatial reasoning. action recognition, counting, text recognition) as well as several underexplored dimensions (e.g., temporal scene sequence understanding, navigation, lighting recognition, and non--comonn-see paired and unpaired QA types, depending To construct the benchmark at scale, we then develop a novel three-stage day- augmented auto-labeling pipeline that leverages daytime videos assist in generating question- pairs for nighttime clips, followed by extensive human verification to ensure accuracy and reliability. Building EgoNight and annotating VQA required over 300 hours of human effort, with each QA pair verified by least one expert annotator. This process results in the high- quality EgoNight-VQA dataset, comprising 3,658 QA pairs. Beyond VQA, we introduce two auxiliary tasks with dedicated testbeds: diy-night correspondence retrieval, which evaluates mossmilluminttion matching, and egocentric depth estimation at night, which is crucial for navigation and interaction in embodied AI. These two tasks further broaden the benchmark and expose new challenges for existing models.
reasoning). These are further organized into whether day- night counterparts share the same questions and answers.
Our extensive experiments across three video sources, three tasks, and 10 state-of-the-art multimodal large language models reveal that nearly all models (including closed-source models such as GPT and Gemini) struggle on this chal- lenging benchmark, with clear and consistent performance gap between day and night. This highlights the unsolved
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challenges of egocentric vision at nighttime and calls for more robust models that generalize across illumination con- ditions. Besides, we highlight that our newly introduced QA types, covering lighting recognition/dnnamie, scene se- quence reasoning, navigation, and non- non-common-senss reasoning, are substantially more challenging than well-studied categories, revealing fresh difficulties for MILMMs. We further prove synthetic data is highly correlated with real data and effectively boosts real-world performance through fine-tuning. Our pilot studies further show that fine-tuning specialized subset of data improves model performance through adapting vision encoder into low-light domain and aligning the language model the uncertain features during night.
Our main contributions are threefold: i) EgoNiggh Dataset: We present the first egocentric dataset that systematically addresses nighttime conditions, featuring night aligned videos from symthetic SegoNight-Synthetie real-worke (FgoNiigttSSffia) and existing (FgoNoigt-Oxfford) sources. ii) Benchmark Suite: We build a comprehensive bench- mark centered egocentric VQA with diverse QA types and 3658 fully human-verified QA pairs, complemented by egocentric depth estimation at night and day-night correspondence retrieval tasks, iii) Empirical Insights: Extensive evaluations reveal clear day-night performance gaps, underscoring illumination robustness as a key challenge; our newly proposed QA types are also validated to pose practical difficulties for current MLLMs.
2 RELATED WORKS
2.1 EGOCENTRIC DATASETS AND VQA BENCHMARKS
A series of large-scat scale egocentric datasets, such as EPIC-KITCHENS Damen et al. (222)) Ego4D Grauman et al.) (2022). Ego-Exo4D Grauman et al. (2024), and EgoExoLearn Huang et al. (2024), have laid the foundation for wide range of tasks, including action recognition Sudhakaran et al. (211). object detection Ren & Gul (2010), pose estimation Luo et al. (2021), video generation Liu et al. (2021) l). Ego-Exo correspondence Fuu et al. (2025 y Pan et al.) (2025) and Ego-Exo translation Liu et al. (2024) Mahdi et al. (2025). Among these, we are particularly interested in egocentric visual question answering (VQA) Fan (2019), which provides a natural and human-like framework for comprehensively evaluating model performance through question-answer interactions. In recent years, several egocentric VQA benchmarks have been proposed, including Ego Fan (2019), EgoTaskQA Dia et al. (2022).
EgoSchema Mangalam et al.] (2023), Tgo Cheng et al. (2024). Temp Plizzari et al. (2025), EgoCross Li ct al. 2025b),, Egnd Xiao et al. (2025), EgoMemoria Ye et al. 2024), Hour Video Chandrasegaran et al. (2024).
Ego Yang et al. (025) with different focuses. However, nearly all of them are confined to daytime or well-lit
scenarios, leaving model performance in low-light or nighttime conditions largely unexplored. The Oxford Day-and-
Night dataset Wang et al. (2025b) is partial exception but was not designed for VQA and lacks day-night alignment. This makes EgoNight and EgoNight-VQS VQQ fundamentally distinct from prior benchmarks.
2.2 MLLMS FOR VIDEO UNDERSTANDING
The rapid development of multimodal large language models (MLLMM)) has substantially advanced the frontier of video understanding. Prominent open-source models include Qwen-VI Bai et al.] (2023), Intern [Chen et al.) (2024b). Vidco-LLaMA Zhang et al. (2023b), while closed-source commercial systems such as GPT-4V AAchiam et al. (023) and Gemini Comanici et al. (0025 demonstrate even stronger capabilities in video captioning, summarization, and open-ended visual question answer- ing. Building on these advances, egocentric MLLMs have emerged to adapt foundation models from exocentric to first-person perspectives. Representative examples include Ego Pramanick al. (2023) for improved video-language cross-modal fusion, EgoGPT Yang et al. (2025) fine-tuned with egocentric captioning and QA, MM- Ego Ye et al. (2024) with a memory mechanism for long videos, and Exo2Ego Zhang et al. (2025a) leveraging ex- ocentric data for egocentric generalization. These works highlight the potential of MLLMs as egocentric assistants. However, nearly all of them are developed and tested under well-lit daytime conditions, leaving their robustness in low-light or nighttime scenarios unexplored. Nevertheless, almost all existing MLLMs are developed and evaluated under well-lit daytime conditions, with little consideration of low-light or nighttime videos, leaving their robustness in low-light or nighttime scenarios unexplored.
Li et al. (2024). and GLM-V [oog al. (2025). )
2.3 CROSS-DOMAIN GENERALIZATION
Domain generalization Zhou et al. (022) is a long-standing challenge in computer vision, where models trained on one distribution must adapt to another. Shifts can arise from semantic drift, style changes, or variations in weather
and lighting. Many algorithms have been validated across tasks such as image classification Li et al. (017): Zhou
et al. (202T): Huang et al. (2023); Peng et al. (2024) 2026), object detection Fu et al. (2024): Li et al. (025c). action
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recognition Pan et al. (2020); Fu et al. (2020): Bian et al. (2011); Zou et al. (2021 2020). few-shot learning Guo et al. (2020); Fu et al. (2021) 2023); Zhang et al. (222c)), and autonomous driving Li et al. (2022 2023a). contrast, cross-domain transfer for MLL.ss, especially in video understanding, remains underexpiored, with only a few recent attempts (e.g., CL.-Cross VQA Zhang et al. (2025b), VQA-GEN [nnni et al. (2023), Super-CI.EVR [i et al. (2023c)) However, none of them are targeted for egocentric video, which is naturally different from exocentric videos in terms of recorded images, camera motion, and contained information. The most relevant effort to us is EgoCross Li et al. (2025b), egocentric VQA benchmark that moves beyond daily activities to evaluate model generalization across distinct long-tail, specialized domains such as surgery and industrial settings. In this paper, however, we investigate MLLMs from different perspective, robustness under nighttime conditions, common and ubiquitous scenario in daily life, yet previously overlooked dimension of domain generalization in egocentric video understanding.
3 EGONIGHT DATASET & BENCHMARKS
3.1 VIDEO SOURCE COLLECTION
Overview & Design Principles. EgoNight is built to systematically evaluate MLLMs under challenging nighttime conditions, which are critical for developing robust intelligent assistants. The collection of video sources follows four principles: ① Reflect real-world challenges, such as walking on dimly lit streets or navigating indoors during power outages; @ Involve natural camera movements and preferably capture actions and interactions with the environment to evaluate both static perception and dynamic understanding: @ Ensure diversity of scenarios, illumination, and task difficulties, spanning indoor, outdoor, office, and grocery settings, lighting from strectlights, flashlights, headlights, and candles, and task levels from easy (relatively clear), through medium (partially visible), to hard (barely visible), + Enable rigorous analysis through day-night paired videos, where scenes, trajectories, and actions remain consistent across conditions so that differences can be attributed solely to illumination. To meet these requirements, EgoNight integrates three complementary video sources, as illustrated in the upper part of Fig. 2 and detailed below.
Syoniihht-ynthhttic. To obtain perfectly aligned day-night video pairs, we used a simulation environment where every element can be precisely controlled, including the scene layout, camera path, and lighting. This ensures that the day and night videos match exactly at the pixel and frame level, with lighting being the only difference. We first employ Infinigen Raistrick al. (2023) to generate diverse indoor 3D scenes. Human annotators cleaned and refined these scenes, then simulated walking through the space at normal speed (1.2 m/s), recording the camera trajectory. We replayed the same trajectory under different lighting conditions. Daytime videos were rendered using Blender Iraci [raci[2013) (2013), and we adjusted the lighting to create the corresponding nighttime versions.
In total, SeaNiight-Sntheeti contains 50 pairs of egocentric videos, covering more than 100 environment assets. These include indoor scenes such as kitchens, bathrooms, and living rooms, populated with over 50 diverse object categories (e.g., windows, tables, beds, chairs, lamps, bookshelves, plates). We design multiple illumination setups, ranging from uniformly lit rooms to sparsely localized lighting, and incorporate three difficulty levels with a different range of motion blur, sensor noise, and illumination level. Besides RGB frames, Blender also allows us to generate ground-truth depth and normals (see Appendix Sec. Sec.A.I.. making EegoNight-Syntheti richer and more versatile.
EgoNiigt--Sofia. To include realistic human- envirommnn interactions missing from synthetic videos, we also recorded our own day-night paired videos. Capturing perfectly aligned real-world pairs is challenging, we de- signed a practical video-guided recording strategy with post-trimming for better alignment.
We first record daytime video with ego-wearer exploring an environment while viewing the live camera feed a phone screen. For the nighttime version, the same person, device, and viewpoint are kept unchanged. Instead of using live preview, the recorded daytime video is played back on the phone, serving as visual guidance to help the ego-wearer match walking speed, viewpoints, and actions. After brief practice, this approach proved more stable and reliable than methods like using landmarks memorized trajectories. Post-trimming is applied to further refine spatial and temporal consistency.
Our real-world dataset, EgoNight-Sofia, contains 20 day-night paired videos recorded in Sofia, Bulgaria. Despite its modest size, it is a rare resource capturing diverse real-world everyday scenarios, including apartments, offices, grocery stores, streets, tourist spots, and outdoor fitness areas. The recordings include natural actions such as drinking water, locking doors, placing keys, charging devices, or checking price labels-leading to realistic VQA cases (e.g.. "Where did put my keys?", "How much was the drink?", "Did I turn left?"). Illumination sources include street lights, lamps, flashlights, and candles.
-xoNig Oxford Day-Night Wang et al. (2025b) is notable exception that also includes egocentric videos captured under both daytime and nighttime conditions. Although it was originally designed for 3D vision tasks such
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Figure 2: EgoNight construction and EgoNight-VQA annotation. EgoNight integrates ShoNiight-Syntetti EgoNight-Sofia, and BeooNight-xfoort sources. Annotation is achieved via a novel three-stage day-augmente Auto QA generation pipeline with 300+ hours of human refinement, resulting in over 3600 high-quality QA pairs.
as novel view synthesis, it offers illumination variations across five representative locations in Oxford. However, the day and night videos are not spatially or temporally aligned.
To enrich EgoNight with more realistic nighttime content, particularly for urban outdoor scenes, we manually select 20 nighttime segments to form EgoNight-Oxford, based on two criteria: (i) minimal overlap in trajectories and loca- tions, and (ii) genuinely low-light conditions. These segments serve as a complementary testbed for evaluating model generalization under illumination changes when paired alignment is unavailable.
For both EgoNight-Sofin and EgoNight-Oxfort human annotators categorize each video into easy, medium, or hard levels. Together, these sources provide EgoNight with balanced combination of precise alignment, natural dynamics, and broad real-world diversity.
3.2 EGONIGUTT- VQA BENCIIAARK RECONSTRUCTION
QA Task Taxonomy. To thoroughly assess models from multiple perspectives, we define a diverse taxonomy of 12 QA tasks. Some of these categories are well-studied and have been explored in previous egocentric VQA benchmarks, such as object/action/text recognition, counting, and spatial reasoning. Others are much less studied or newly proposed in EgoNiigh--VQA. including scene sequence and navigation (which require not only visual perception but also mem- ory and spatial reasoning), illumination recognition and illumination change (designed to test models' understanding of lighting concepts), and non-common-sent
reasoning (e.g., detecting abnormal cases such as door inserted into wall in the synthetic data). More detailed explanations of QA types can be found in the Appendix Sec. A.4.] We further organize these categories into paired and umpaired QA types, depending on whether the same questions can be consistently applied across day-night counterparts: 1) Paired QA Types. These cover contexts that remain unchanged across day and night, allowing the same QA pairs to be used for both videos and thus providing a clean testbed for measuring performance gaps. Specifically, we include: ① object recognition, @ text recognition, 3 spatial reasoning. & scene sequence, @ navigation, @ counting of static, ① action recognition, and non- con--ooooonneeen reasoning. 2) Unpaired QA Types. These include categories that are impractical to pair across day and night, are only meaning- ful in the nighttime condition. We consider: ① lighting recognition, @ lighting dynamic, @ dynamic detection, and ( counting of dynamic. We control QA clip duration by task type. For static or spatial tasks (e.g., object recognition, lighting recognition), we use short clips of 3 seconds to minimize redundancy; For dynamic or temporal tasks (e.g.. action recognition, navigation), the entire video is used to capture the complete context. Following recent works Pliz- zari et al. (2025); y; Xiao et al. (2025), we adopt the open- open-ended QA setting over the closed-form multiple-choice format, as it better reflects natural human- Al interactions.
A detailed summary of each QA type, including whether it is paired or unpaired, clip duration, and example questions, is provided in Fig. This taxonomy makes EgoNight-VOO VQA not only diverse and well-structured but also novel, introducing illumination reasoning and other challenges uniquely tied to nighttime egocentric vision.
Day-Augmented Auto QA Generation. Constructing large-scale QA pairs for nighttime videos is particularly chal- lenging due to low visibility, which makes direct annotation both time-consuning and error-prone. To address this, as illustrated in the lower part of Fig. 2 we design a novel three-stage day-augmented auto QA generation pipeline that leverages aligned daytime videos as strong prior for annotating their nighttime counterparts.
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Figure 3: QA types with examples. The first eight are paired types, where the same question applies to both day and night clips; the last four are umpaired, evaluated only at night. QA Types have various durations, with static or spatial tasks (e.g., I and 3) using short clips, while dynamic or temporal tasks (e.g., 4 and 5) use full videos.
Specifically, the pipeline is tailored to each QA type and consists of three stages:
1) Nighttime captioning. For each clip, we prompt advanced MILLMs to generate detailed captions with an explicit focus on the target QA type (e.g., highlighting object-related attributes for object recognition or text/logos for text recognition). This ensures that the captions capture the most key information or construct relevant QA pairs.
2) Nighttime question generation. The caption, together with the corresponding night clip, is then fed into the same MLLM to produce diverse question candidates centered on the given QA type. This step encourages variety in phrasing and perspective while maintaining fidelity to the visual content.
3) Pay-augmented pseudo answer synthesis. For paired QA types, pscudo answers are generated by consulting the aligned daytime clip, where content is more visible and less ambiguous. For unpaired QA types or datasets without alignment (e.g.. EgoNight-Oxford answers are instead derived directly from the nighttime clip.
All three stages are powered by GPT-4.1. Empirically, we find that both the QA-type-spectie prompting and the inclusion of daytime videos substantially improve the quality and reliability of the generated QA pairs. More examples of VQA pairs and caption generation can be found in Appendix A.4.2 and A.7.I
Human Annotator Refinement. Finally, human annotators refine QA pairs via three operations: i) delete, when QA pairs meaningless, vague, duplicated, inconsistent across day-night counterparts (for paired QA types); ii) modify, when the question is valid but the answer is wrong (or vice versa), or to resolve ambiguity; iii) add, when many pairs are removed or when important, challenging questions, especially about dynamic concepts, are missing.
After the first labeling round, we performed a random double-check to refine low-quality annotations. Thus, although our pipeline combines model generation with human refinement, every QA pair (3,658 in total) is manually verified at least once. In total, ~200 hours of human effort were invested, ensuring the quality and reliability of EgoNight VQA.
Dataset Statistics. EgoNight- VQA comprises 3,658 high-quality, fully numan-verified QA pairs across 12 task types, sourced from EgoNight-Synthetiic EgoNight-Sofia, and EgoNight-Oxford, with an average of 40 pairs per video. Detailed statistics on QA distribution, video durations, task difficulties, scenarios, and illumination are shown in Fig. 4 The number of videos across the three subsets- subsets-Synthetic (50), Sofia (20), and Oxford (20)-is proportionally reflected in the VQA annotations (2029: 813: : 816). This results an approximately 1:1 balance between synthetic and real (Sofia + Oxford) VQA samples, ensuring that our benchmark is not dominated by synthetic content. We provide more comparison of Egocentric VQA datasets in Appendix A..] Overall, EgoNight VQA provides diverse and comprehensive benchmark for evaluating egocentric vision models under nighttime conditions.
3.3 BENCHMARKS BEYOND EGOCENTRIC VQA
Day-Night Correspondence Retrieval. To further assess model capabilities beyond VQA, we introduce day-nigh correspondence retrieval, which evaluates a model's ability to match visual content across illumination conditions, Specifically, we define two subtasks: i) Spatial Retrieval (Scene Recognition). Spatial retrieval, or scene recognition, is a long-standing vision task AAranjeelovic et al. (2016): Miao et al.) (2024); [Dogfangg et al.] (2025). Here, it is extended: given query clip and a set of N candidate clips of equal duration s, the model must retrieve the one
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Figure 4: Statistics of EgoNight- VQA benchmark. (a) Distribution of QA pairs across QA types and sources. (b) Video duration distribution. (c) Task difficulty levels cross scenarios. (d) Scenario coverage. (e) Illumination coverage.
depicting the same scene. This evaluates a model's 's ability to capture and relate spatial relations in egocentric videos, e.g.. distinguishing bedroom from bathroom or another bedroom. We built this benchmark with 1000 randomly generated meta-tasks. Each task samples a query clip, and the candidate set includes its temporally aligned counterpart (with a temporal shift for added difficulty) plus N - negatives from other scenes. Performance is measured by Top-1 accuracy across all tasks. In our setup, we use N- = 10, = 10 seconds, and temporal shift of [10, 20] frames. Both Day (query) + Day (database) and Day -> Night settings evaluated.
ii) Temporal Localization. We further design a temporal localization task to test whether models can align video segments across dynamics. Given query clip of duration s, the model must localize it within the corresponding full video by predicting its start and end timestamps (4,, directly evaluating temporal reasoning (e.g., grounding "The door is being locked" to 10-20s) We construct 1000 meta-tasks, each generated by randomly sampling one clip from its parent full video that is also randomly selected. Inspired by temporal grounding literature Xin et al. (2024). we adopt mean antersection-over-nniiii
(mloU) between the predicted interval (t,, 44) and the ground-truth interval (t), ft)) as the evaluation metric. Consistent with spatial retrieval, we set = 10 seconds and evaluate both Day + Day and Night >> Day settings.
Egocentric Depth Estimation at Night. Depth estimation is fundamental component of computer vision. On the one hand, extensive research Yang et al. (2024alh) bb: Wang et al. (2025a) has focused on depth estimation in non-egocentric settings (typically not with fisheye cameras), while egocentric depth estimation remains largely underexplored, es- pecially under nighttime conditions. On the other hand, recent works Chen et al.] (2024a); Liu al. (2025) suggest that incorporating depth can enhance models' spatial reasoning abilities. These two observations motivate to con- struct an auxiliary benchmark for egocentric depth estimation at night. Specifically, we use EgoNight-Syntheee the testbed, where ground-truth depth maps are provided by the rendering engine. Thanks to the day-night aligned design, we can quantitatively evaluate models under both controlled daytime and nighttime conditions. For evalua- tion, we adopt standard depth estimation metries, including absolute relative error (AbsRel), 8, (1.25), 62.2.2 and 6s(1.253), where &e measures the percentage of predicted pixels whose relative error is within a threshold of 1.25*.
as
4 EXPERIMENTS
4.1 EVALUATED MLLMS& & METRICS
We evaluate broad set of state-of-the-arr MLLMs on the proposed benchmarks. i) For Eggtttttt we include two closed-source commercial models, GPT-4.1 [Achiam et al. (2023) and Gemini 2.5 Pro Comanici et al. (2025) )
eight open-source models, VL (3B, 7B, 72B) Bai et al. (2023) Videol.
(7B) /hhag et al. (2023b)
Intern (8B) Chen et al. (2024b), GI M--41VVV (9B-Base) Hong et al. (2025). and L.L Vi- (7B) Li al.
( well EgoGPT Yang al. (2025). one of the few open-source egocentric models tailored for open-ended QA. Following prior work Plizzari et al. (2025); Fan 2019). we adopt an LLM-as-a-Judge strategy to assess semantic consistency between predictions and ground truth, and report average accuracy across the test sets. ii)We provide further in-depth analysis on synthetic data quality, and potential model improvements. iii) For day-night correspon- dence retrieval, we benchmark Beature-based retrieval methods, DINOv2 Oquab et al. (2023) and Perception Encoder (Percep. Enc.) Bolya Enc.) et al. (2025), alongside MLLM- methods, GPT-4.1 and Intern VL.3 (8B). As described in
Sec. [.3] Top-1 accuracy (Acc-R@1, 1, %) and mloU (%) are used for evaluating the spatial and temporal subtasks, respectively. iv) For egocentric depth estimation, we test general monocular depth model (Depth Anything Yang
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EigoNihh-Syythettic Medium
EgoNight-Sofiaa Medium
EgoNight-Oxford Medium
Avg.
Models
Easy
Hard
Easy
Hard
Easy
Hard
Closed Source MLLMs
GPT-4.1
29.30
26.87
18.87
32.04
29.35
31.69
39.72
37.13
40.72
30.93
Gemini 2.5 Pro
31.05
24.81
16.51
38.24
26.81
28.87
36.75
36.81
27.88
30.60
Open-source source MIL.Ms
Intern VL3-8B
20.21
15.50
16.98
24.03
21.74
20.42
22.90
20.85
16.36
20.06
Qwen2.5-VL-72B
18.39
15.25
12.26
24.03
17.03
20.42
24.81
22.80
16.36
18.99
Qwen2.5-VL-7B
13.01
13.95
13.68
15.44
12.68
12.68
13.74
13.36
12.73
13.44
Qwen2.5-VLL-3B
14.69
10.34
7.08
15.50
13.04
12.68
17.18
11.40
12.12
13.41
GLM--.11-9BB-Bas
19.09
13.70
15.57
18.60
18.48
16.20
17.15
22.15
18.79
18.20
VideoLLaMA3-7B
16.85
13.44
14.62
11.11
10.87
9.15
12.26
10,46
9.15
13.64
M.AVA-NcXT-Video-TB
6.36
11.37
1.89
13.95
9.78
14.79
3.05
2.61
3.03
7.28
Egocentric MLLMs
EgoGPT
15.79
13.55
12.04
12.41
12.13
10.36
12.37
13.58
13.68
14.29
Table I: Comparison results on EgoNight- VQA. Accuracies (%) of OpenQA results across three datasets and three difficulty levels. We compare closed-source models, open-source models, and egocentric-specifie models.
et al. (2024a] b)). 3D reconsiruction-based method (VGGTStream Zhuo et al. (2025) y Wang et al. (025a)), and two al. (2025)). For Depth Anything
egocentric fisheye-specific models (DAC Guo et al. (025) and UniK3D Piccinelli Piccinell and VGGTStream, input fisheye RGB frames and depth maps undistorted prior to inference for fair comparison. Additional implementation details (c.g., fps for frame extraction, prompts, and model settings) and discussion about IL-M-as-a-Judge strategy are provided in the Appendix Sec. A.5
4.2 RESULTS ON EGONIGHT- VQA
The main results of all MLLMs are shown in Tab. In addition, we provide per-QA performance comparisons between day (striped bars) and night (solid bars) for paired QA types (Fig. a)) and report nighttime performance across all QA types (Fig. (()). based averages across all models. Note that com-common case detection is available only in EgoNight- Synthetic while dynamic events and actions are included only in the real-world data.
From the results in Tab. M we observe that almost all MLLMs struggle on benchmark, with maximum averaged accuracies of 30.93% from the closed-source GPT-4.1, 1, 20.06% from the open-source Intern VL.388B and 14.29% from the egocentric EgoGPT. The wide performance spread also confirms that dataset is sufficiently challenging and effective for distinguishing model capabilities. Fig. 5|a) further highlights the performance gap, showing declines of 32.8% and 25.0% on EgoNiigt-Synthetic and Nightttt-Sofia, respectively. Together, these results underscore the substantial challenges posed by our benchmark, exposing the limitations of current MLLMs under nighttime scenarios and highlighting the need for more Ilumination-robuu models. Beyond the overall trends, we note three additional insights from Tab. I i) Closed-source models perform best. Within open-source models, Qwen2.5-VL VL generally improves with scale, yet Intern VI. outperforms the larger Qwen2.5-VL. (72B), suggesting that size alone is insufficient. The relatively low results of EgoGPT further emphasize the need for more robust egocentric models. ii) EgoNight-Oxford achieves the highest scores, but its illumination conditions are more challenging than those in EgoNight- -yyttheii and EgoNight-Sofia (Sec. A.4.2) Appendix). This indicates that without paired day videos and our day-augmenter auto-labeling strategy, even human annotators face difficulties generating challenging QA pairs, underscoring the practical value of our dataset design; iii) Overall, performance declines across task levels (easy, medium, hard), validating the diversity and difficulty of our benchmark.
From the per-QA results in Fig. 5)a) and Fig. (b)), we further observe three key trends: i) Models perform better on perception-orientel tasks (e.g.. object recognition, text recognition, scene sequence) than reasoning-oriented tasks (e.g., navigation, counting, non-common-sense reasoning cases) under daytime conditions. However, at night, per- ception tasks suffer larger performance drops, indicating their higher sensitivity to illumination, whereas reasoning tasks, though harder overall, are relatively less affected since they rely more on temporal and contextual cues. MLLMs achieve substantially lower accuracy on our newly proposed tasks, such as lighting recognition, lighting dy- namies, scene sequence, dynamic detection, navigation, and con--comoon-sene reasoning, suggesting that existing MLLMs generalize poorly novel tasks compared with well-studied ones like object recognition. iii) Each dataset in Fig. 5 b) emphasizes distinct aspects of nighttime challenges, together providing complementary perspectives that ensure EgoNight spans a balanced range of perception-reasooingg difficulties under low-light conditions.
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35.0
EgoNight-Synthetic
Day Night
Novigatlon
Scme Sequence
Spatial Ressoning
Eyttoghn-Oynthnn
#gahligh-stttin
Fgshighl-Oadord
Counting
Text
20.2
20.1
32
Stotic
Recognition
15.0
10.4
11.4
Objecct
Spatial
Con-Co Reasening
ObjecT Recognitton
EgeNight-Sofia
Day Night
26.5
Lighting Recegnition
Action Redognition
21,4
22.3
25.0%
10.3
12.5
16.1
37.4
Lighting Dynamic
Dynámic
Counting of Dynomic
Detection
Object
Text
Spatiel
Scene
Nevi
Count
Action
Overall
(a) Day-Night Performance Gap on Paired QA Types
(b) Performance Comparison on All QA Types
Figure 5: Performance analysis of MLLMs on EgoNight- VQA. (a) Day-night performance gap across paired QA types, showing consistent degradation at night. (b) Nighttime performance across all 12 QA types. NonC means non-common-senss reasoning.
4.3 MORE IN-DEPTH ANALYSIS
Quality of SigoNight-Synthetic. More examples of synthetic visualizations are provided in Appendix A.4.2 and A To quantitatively evaluate how well the synthetic data reflects real-world performance, we benchmark diverse set of multimodal large language models (MLLMs) on three subsets: Synthetic, Sofia (real), and Oxford (real). For each model, we compute its overall VQA accuracy on each subset and then calculate the Pearson correlation between the average per-model accuracies on synthetic and real-world data (Appendix (A.6). We observe strong correlations between synthetic and Sofia (0.9359, p-value 6.847 10-5), and between synthetic and Oxford (0.8588, p-value 1.462 x 10 1). These results indicate that models that perform well on synthetic data also tend to perform well on real nighttime data, suggesting that the synthetic set preserves the relative difficulty and ranking of models.
To further examine transferability, we fine-tune Qwen2.5-VL-777 using supervised fine-tuning (SFT) on the synthetic training split only, while keeping the real-world data unseen during training. We then evaluate the fine-tuned model directly on the real dataset. The accuracy improves from 14.83% (zero-shot baseline) to 20.57%, demonstrating that supervision from synthetic nighttime scenes generalizes to real-world nighttime scenarios.
Impact of Fine-Tuning To study model adaptation to nighttime egocentric QA, split EgoNight into 70% training and 30% testing subsets, ensuring no video overlap between splits. We fine-tune Swen2.5-VL-77 with supervised fine-tuning (SFT) under three configurations: i) full model fine-tuning (updating both vision encoder and LLM), ii) vision-encoder-only tuning (freezing the LLM), and iii) LLM-only tuning (freezing the vision encoder). All models are trained the same training split and evaluated the held-out test split.
Tab. reports the performance for synthetic and real (Sofia and Oxford) separately. shows consistent performance gains across all configurations compared the zero-shot baseline. Updating either the vision encoder or the LLM independently improves accuracy, while full fine-tuning achieves the best performance, indicating that both improved visual representations and stronger vision- language alignment contribute adaptation under low-light conditions.
Perception vs. Reasoning under Fine-Tuning To better understand which capabilities benefit most from adaptation, we group tasks into cnreption-orientee (Object Recognition, Text Recognition) and aasoning-orientee (Navigation, Counting) categories and report their accuracies separately in Tab.
iirception-orienttrd tasks show larger absolute improvements after fine-tuning, suggesting that low-level visual adap- tation plays significant role in nighttime scenarios. ession-encoder-on/ tuning primarily boosts perception tasks, whereas LLM-only tuning improves both perception and reasoning tasks to certain extent. However, reasoning- oriented tasks exhibit comparatively smaller gains overall, indicating that high-level reasoning under degraded visual input remains challenging even after adaptation.
Failure Cases. Additional analysis of failure cases is provided in Appendix A.8.1
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Published as a conference paper at ICLR 2026
Model
Synthetic
Real
Task
Qwen7B
Enc. Only
LLM. Only
Qwen7B (Base)
23.23
16.40
Object
8.435
34.718
35,855
Enc. Only
29.74
20.92
Text 18.440
49.890
50.988
LLM. Only
35.50
22.26
Navigation Counting 16,558
17.870
19.495
19.918
Full
36.25
25.61
16.945
24.275
Table 2: Fine-tuning performance comparison
Table Fine-tuning performance comparison
across datasets.
across tasks.
4.4 RESULTS ON DAY-NIGHT CORRESPONDENCE RETRIEVAL
Spatial Retrieval (Ace- R@ 1 %
Temporal Localization (mIoU % f)
Models
EgoNight- Synthetic
EgoNight-
EgoNight-
EgoNight-
Day->Day
Night-DDay
Day++Day
Night
Day Day->Day
Night-
Day -Dy
Night-Daay
DINOv2
45.7
28.7
84.5
74.5
33.7
33.1
Pereep. Enc.
65.4
41.6
89.8
80.9
32.9
33.4
GPT-4,1
75.6
54.1
92.5
84.5
14.7
10.0
21.2
15.5
IntemVLL-8B
39.4
27.7
73.9
56.3
10.2
9.9
12.5
13.3
Table 4: Night-to-Day retrieval performance. Each dataset is evaluated on both Day and Night->Day settings.
The results of day-night retrieval are reported in Tab. 4 The gap between Night-Day and Day-Day shows that cross-illumination retrieval remains highly challenging compared with in-domain retrieval. For spatial retrieval, GPT- 4.1 consistently outperforms other methods, achieving over 80% accuracy. This suggests that Retrieval-Augmentt Generation methods could further improve performance, as Fig. 5ia) already shows that daytime inputs significantly benefit the models. For temporal retrieval, however, 1PT-4.1, despite its strong results egocentric VQA (Tab. [ and spatial retrieval, shows a substantial drop compared with feature- based methods (DINOv2 and Perception Encoder). A similar degradation is observed for Intern VL338B. These findings suggest that while MILMS excel spatial semantic understanding, they struggle with temporal reasoning, such as timestamp prediction, which critical for temporal localization. Further results on temporal limitations are provided in Appendix
4.5 RESULTS ON DEPTH ESTIMATION
Results for depth estimation are reported in Tab. The relatively low scores across all models highlight the difficulty of our EgoNight dataset, which combines egocentric motion, complex geometry, and extreme lighting variations. A clear gap between daytime and nighttime performance again underscores the challenges of low-light conditions. Among the methods, fisheye-based methods (DAC and UniK3D) outperform general depth estimators, suggesting the need for egocentric-specific algorithms. Additional qualitative results are provided in Sec. A.7.3
Abs Rel
8 (1.25)
5g (1.25*) f
8g (1.25%) 1
Method
Day
Night
Day
Night
Day
Night
Day
Night
Depth Anything (U)
0.297
0.302
0.249
0.237
0.463
0.447
0.622
0.60
VGGTStream (U)
0.293
0.298
0.234
0.232
0.447
0.442
0.615
0.609
DAC (F)
0.245
0.292
0.255
0.216
0.495
0.425
0.684
0.602
UniK3D (F)
0.224
0.253
0.280
0.254
0.524
0.481
0.706
0.658
Table 5: Depth estimation results on EgoNight-Synthetic. U: undistorted input; F: fisheye input.
5 CONCLUSION
In this work, we introduced EgoNight, the first benchmark suite designed to systematically evaluate egocentric mul- timodal large language models (MLLMs) under challenging nighttime conditions. EgoNight integrates synthetic and real-world videos with day-night alignment, enabling rigorous analysis of illumination effects. Building upon this data, we proposed EgoNight- VQA, spanning 12 QA types with 3,658 human-verified pairs, alongside two comple- mentary benchmarks: day-night correspondence retrieval and egocentric depth estimation. Experiments reveal that even ofate-of-the-art MLLMs struggle under low-light conditions, with performance dropping substantially compared to daytime. This highlights that nighttime egocentric vision remains far from being solved, motivating future research into illumination-robhe egocentric perception and reasoning. We believe EgoNight provides valuable and timely benchmark that will drive progress toward more reliable egocentric AI assistants.
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ACKNOWLEDGMENTS
This research was partially funded by collaboration project between VIVO and INSAIT indoor scene understand- ing and editing. Research was also funded by the Ministry of Education and Science of Bulgaria (support for INSAIT, part of the Bulgarian National Roadmap for Research Infrastructure) This project resources provided by Google Cloud Platform (GCP). We sincerely thank Dr. Zirui Wang, the author of the Oxford Day-and-Night dataset, for the helpful discussions and for kindly providing the source videos.
supported with computational
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14


Reviews:
[{"review_id": "8jZ4EP8Re9", "reviewer": "Reviewer_akCw", "summary": "The paper introduces EgoNight, a benchmark focused on egocentric vision understanding in nighttime. It consists of three data sources: (i) EgoNight-Synthetic, 50 ideally aligned egocentric pairs with varying illumination levels using a simulator; (ii) EgoNight-Sofia, 20 pairs of real-world egocentric videos with spatially and temporally aligned day–night counterparts; (iii) EgoNight-Oxford,  20 nighttime videos from the Oxford Day-and-Night dataset", "questions": "- Could the authors explain the human verification process for the day-augmented annotation? Specifically, how were QA pairs handled if the answer was only derivable from the day video and not the night video?\n- Are there any strategies to address the synthetic-to-real gap in EgoNight-Synthetic?\n- In Figure5, is NonC in the egoNight-Syn bar plot a typo? Could the authors use the same color for the same task to improve readability?\n- What is the performance of reasoning on MLLM-generated captions of the videos?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- While most egocentric datasets are overwhelmingly focused on well-lit, daytime scenarios, this paper provides valuable nighttime egocentric data and thus has high significance for real-world applications.\n- The proposed benchmark includes a comprehensive list of QA tasks, along with three critical tasks (day-night correspondence retrieval, temporal localization, egocentric depth estimation) that are unique for nighttime videos.\n- The paper is well-written and easy to follow with carefully designed figures.", "weaknesses": "- The EgoNight-Sofia (real-world) dataset is the most valuable and novel component in the proposed benchmark. However, it is quite small with only 20 video pairs. The vast majority of QA pairs (2029 of 3658) come from the synthetic dataset, which may not capture the full complexity of real-world sensor noise and lighting artifacts.\n- The day-augmented annotation pipeline could introduce a bias. It risks generating questions about details that are perfectly visible in the day video but are genuinely invisible or non-inferable in the night video. Is this considered in the human verification stage?\n- The evaluation uses an \"LLM-as-a-Judge\" (GPT-4.1) to score answers, which might introduce bias towards its own outputs. GPT4.1 achieving the highest accuracy under this metric is less convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "deT2Er6laY", "reviewer": "Reviewer_Cyx7", "summary": "This paper introduces EgoNight, a new benchmark suite designed to evaluate egocentric vision understanding in challenging nighttime or low-light conditions. The authors identify this as a critical but overlooked area, as most existing benchmarks focus on daytime scenarios.\nThe primary contributions are threefold:\n1. A Novel Dataset: The EgoNight dataset combines three sources: a synthetic dataset with perfectly aligned day-night video pairs (EgoNight-Synthetic), a real-world dataset with carefully recorded, temporally aligned day-night pairs (EgoNight-Sofia), and an unaligned real-world night video dataset for diversity (EgoNight-Oxford). The day-night alignment is a core feature, enabling controlled analysis of model performance under different illumination.\n2. A Comprehensive Benchmark Suite: The main task is Visual Question Answering (VQA), featuring 3,658 human-verified QA pairs across 12 diverse types, including novel tasks like lighting recognition and non-common-sense reasoning. The suite is complemented by two auxiliary tasks: day-night correspondence retrieval and egocentric depth estimation.\n3. Some Empirical Analysis: The authors evaluate a wide range of state-of-the-art Multimodal Large Language Models (MLLMs), including closed-source models like GPT-4.1 and Gemini. The results consistently show a substantial performance drop from day to night across all models and tasks, highlighting that current systems are not robust to illumination changes.", "questions": "Please address the weakness in the above. Some additional questions: \n\n1. The paper's main argument rests on its novelty as the \"first comprehensive benchmark for nighttime egocentric vision.\" However, given existing work like the Oxford Day-and-Night dataset, could you clarify what makes your contribution fundamentally different, rather than just an extension in scale and task diversity? \n\n2. Regarding the scope of the benchmark: The paper presents EgoNight as a suite for evaluating existing models. However, the true value of a new benchmark often lies in its ability to drive the development of new models. Have the authors considered creating and releasing a dedicated training set? Providing a training split would fundamentally increase the benchmark's impact, allowing the community to build and fine-tune models specifically for the challenges of nighttime egocentric vision. Without a training set and a baseline evaluation of a model fine-tuned on it, the novelty and contribution of the work could be seen as limited to just a test set. \n\n3. The paper's analysis is too simple. While it successfully demonstrates that models fail in low-light conditions, it provides almost no insight into why they fail or how the community should go about fixing them. The key takeaway is a problem statement, but it leaves researchers with no clear direction. For example, is the core issue a lack of diverse, low-light data in the models' massive pre-training datasets, or can this deficit be effectively overcome with targeted fine-tuning (post-training) on a smaller, specialized dataset like EgoNight? The paper does not investigate this. The analysis in Figure 5b shows that perception-oriented tasks (e.g., object/text recognition) degrade more severely than reasoning tasks (e.g., navigation). This is a good observation, but the paper stops there. It fails to diagnose whether this is due to the vision encoder failing to extract meaningful features from noisy pixels (an issue with the pre-trained foundation) or the language model's inability to handle uncertain visual inputs (an issue that might be solved via fine-tuning). The analysis simply presents the final scores without diagnosing the root cause of the failure.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Addresses a Relevant Problem: The paper highlights the practical and important challenge of model robustness to illumination changes, a common failure mode for vision systems in real-world applications.\n2. Significant Data Collection Effort: The authors have clearly invested a substantial amount of effort in collecting and annotating a new dataset, particularly in creating the temporally aligned real-world videos (EgoNight-Sofia). This resource, if made public, could be of some use to the community.", "weaknesses": "1. Limited Novelty: The work feels incremental. It takes the well-established paradigm of egocentric VQA and applies it to the nighttime domain. This is more of a domain generalization study on a new dataset than a novel research contribution. Prior work like the Oxford Day-and-Night dataset already provided a basis for nighttime egocentric vision, and benchmarks like EgoCross have already explored cross-domain challenges for egocentric VQA. The current work is a straightforward combination of these existing concepts.\n2. Potential for Dataset Contamination and Bias: The VQA pairs were bootstrapped using GPT-4.1. This creates a significant risk that the dataset is \"contaminated\" with the stylistic and factual biases of the model used to generate it. Consequently, the benchmark may unfairly favor models that are architecturally or stylistically similar to GPT-4.1, making it an unreliable tool for impartially assessing a diverse set of MLLMs.\n3. Flawed and Unreliable Evaluation: The use of an LLM-as-a-Judge for open-ended VQA is a major methodological weakness. This evaluation strategy is not yet considered robust or unbiased in the research community. Without calibration against human judgments, the reported accuracy numbers are not trustworthy and cannot be reliably used to compare models. This undermines the paper's core empirical claims.\n4. Presentation Can Be Further Addressed: The paper suffers from some grammatical errors and lacks the polish expected for a top-tier publication. The writing style is often convoluted, making it difficult to parse key details of the data collection and annotation pipeline.\n\nHere are some mistakes i listed, but more: \n\nPage 2, Line 70-71: \"This motivates us to investigate egocentric vision at night, with an emphasis on complex scene understanding and reasoning tasks.\"\nIssue: The sentence structure is a bit convoluted. \"With an emphasis on\" feels like an add-on. A clearer phrasing might be: \"This motivates us to investigate egocentric vision at night, focusing on complex scene understanding and reasoning tasks.\"\n\nPage 2, Line 74-75: \"A central challenge in constructing such a benchmark lies in obtaining suitable video sources that capture the characteristics of nighttime environments, as well as developing annotation methods that ensure high labeling quality.\"\nIssue: The phrase \"as well as developing\" creates a slightly unbalanced structure. A more parallel construction would be better: \"...lies in obtaining suitable video sources... and developing annotation methods...\"\n\nPage 2, Line 83-84: \"However, in practice, collecting perfectly aligned day-night pairs in the real world is highly nontrival.\"\nIssue: \"Nontrival\" is not a standard English word. It should be \"non-trivial.\" This is a clear spelling/typographical error.\n\nPage 5: Starting from 3.2, there's always a space at the beginning of the paragraph, which is not aligned with the ICLR template.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gldvPU57s1", "reviewer": "Reviewer_3vts", "summary": "This paper introduces EgoNight, the first comprehensive benchmark dataset designed to evaluate egocentric vision understanding, particularly for Multimodal Large Language Models (MLLMs), under challenging nighttime and low-light conditions. The authors identify that existing egocentric datasets focus almost exclusively on well-lit daytime scenarios, ignoring a critical operational domain for real-world applications like personal assistants. The core contribution is the creation of a benchmark suite featuring day-night aligned videos, which is crucial for two reasons: (i) It enables a rigorous and fair comparison of model performance, isolating the performance drop caused by illumination changes. (ii) It facilitates a novel \"day-augmented auto-labeling\" pipeline, where the clear daytime videos are used to help generate high-quality annotations for the ambiguous nighttime videos.\n\nThe EgoNight dataset is sourced from three streams:\n- EgoNight-Synthetic: 50 perfectly aligned pairs from a Blender simulation.\n- EgoNight-Sofia: 20 real-world pairs captured with a novel video-guided recording protocol to ensure \"decent alignment\".\n- EgoNight-Oxford: 20 unaligned nighttime videos from an existing dataset to test generalization.\n\nUpon this data, the authors build three benchmarks:\n- EgoNight-VQA (the core task): 3,658 human-verified question-answer pairs spanning 12 diverse QA types, including novel tasks like \"lighting recognition\" and \"non-common-sense reasoning\".\n- Day-Night Correspondence Retrieval: Evaluates spatial and temporal matching across illumination conditions.\n- Egocentric Depth Estimation: Uses the synthetic data's ground-truth depth to test models at night.\n\nExtensive experiments on over 10 state-of-the-art MLLMs (including GPT-4.1 and Gemini 2.5 Pro) reveal a substantial performance drop from day to night (e.g., 32.8% on Synthetic, 25.0% on Sofia), demonstrating that current models are not robust to low-light conditions and validating the difficulty and necessity of this new benchmark.", "questions": "- Could the authors provide more analysis and examples of the synthetic data?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper tackles a critical and previously overlooked gap in egocentric vision. The benchmark (EgoNight) is the first of its kind and will be a valuable community resource.\n\n- The paper's use of both perfectly-aligned synthetic data and realistically-aligned real-world data provides a robust and comprehensive foundation for evaluation.\n\n- High-Quality Annotation Pipeline: The \"day-augmented auto QA generation\" followed by over 300 hours of human verification ensures the reliability of the core VQA benchmark, which is especially difficult for low-visibility data.\n\n- Comprehensive Evaluation: The paper presents not just one VQA task, but a suite of three benchmarks (VQA, Retrieval, Depth).", "weaknesses": "- The scale of real-world data is pretty small. Only 20 day–night pairs in EgoNight-Sofia dataset.\n\n- The quality of simulation data is uncertain. Both qualitative and quantitative studies are missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TXuvFjEvIu", "reviewer": "Reviewer_VX41", "summary": "The paper presents EgoNight, a new dataset and benchmark for low-light egocentric video understanding. EgoNight has three subsets, (1) a synthetic video dataset rendered by Blender, where the authors can have more fine-grained control of the scene and the lighting conditions, (2) an aligned day-night real video dataset collected by having video recorders walking around Sofia, Bulgaria at similar speed and route during the day and the night, and (3) a subset of the Oxford day-and-night video dataset. For each subset, a set of questions and corresponding answers are generated for each of the 12 type of questions, and these generated questions and answers are verified by human annotators. In addition to VQA, the benchmark also includes day-night correspondence retrieval, temporal localization, and night-time depth estimation as additional tasks for evaluating the performance of multimodal LLMs under low-light egocentric video conditions.", "questions": "I wonder whether the authors have did some more controlled experiment with the synthetic data generator to explore why there is such a significant gap in performance between the daytime and nighttime videos. What factors (e.g. illumination, motion blur, location of light source) contribute most to the performance degradation? What are the main failure modes in low light conditions (e.g. spatial confusion, missing small objects)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well written and easy to understand. The figures are clear and provides a great summarization to the dataset construction pipeline. The paper provides abundant examples from the dataset and both qualitative and quantitative results (some in the supplementary material).\n- The new real video recordings in the dataset (especially EgoNight-Sofia) can be a good contribution to the community, especially its aligned day-night recordings captured through the video-guided recording strategy.\n- The day–night performance gap illustrated in figure 5 is very significant, highlighting the necessity for a low-light egocentric video dataset and benchmark.\n- In addition to VQA, the benchmark also include a few additional tasks that have decent practical value, and allows the user to evaluate the capabilities of MLLMs in low-light egocentric videos more holistically.", "weaknesses": "- The paper lacks sufficient discussion regarding the human verification stage. Since most of the ground-truth answers are initially generated from multimodal LLMs, the quality of the human verification stage directly determines the quality of the dataset. Therefore I think the paper should provide more information about the human verification stage, including detailed guidelines to the human annotators and reporting inter-annotator statistics. It would be even better if a subset of the data can be annotated by humans without the help of LLMs to check whether there is bias in the human verification process.\n- There are a few ethical concerns regarding the dataset. Details in the ethical concerns section below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)