Pith. sign in

REVIEW 3 major objections 7 minor 44 references

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation

T0 review · 3 major / 7 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read MMD-LoRA adapts a sunny-domain depth estimator to night and rain by injecting 0.035M low-rank parameters guided by text, and reports state-of-the-art d1 accuracy on nuScenes and Oxford RobotCar.

desk verdict A plausible parameter-efficient LoRA recipe for adverse-condition depth estimation that is undone by a missing ablation row: the text attributes gains to PDDA that actually come from VTCCL alone. read the letter →

arxiv 2412.20162 v1 pith:3UQADXVI submitted 2024-12-28 cs.CV

Guanglei Yang , Rui Tian , Yongqiang Zhang , Zhun Zhong , Yongqiang Li , Wangmeng Zuo This is my paper · ORCID
classification cs.CV
keywords adverseconditiondepthestimationmonocularlow-rankadaptationLoRAdomaingeneralizationmultimodalalignmentcontrastivelearningautonomousdriving
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a depth estimator trained on sunny daytime images can be adapted to unseen adverse conditions such as night and rain by injecting tiny low-rank adaptation (LoRA) matrices into its image encoder and steering them with text, without ever seeing a target-domain image. The proposed MMD-LoRA has two parts: Prompt Driven Domain Alignment (PDDA), which trains the LoRA so the visual difference between source and target domains matches the text-embedding difference between "day" and "night" (or "rain"), and Visual-Text Consistent Contrastive Learning (VTCCL), which pulls matching weather representations across CLIP text and diffusion visual features closer while pushing mismatched ones apart. On the nuScenes validation split the method raises the d1 accuracy from 88.03% to 96.46% on clear day, from 75.33% to 79.96% at night, and from 82.82% to 95.37% on rainy day; it also improves day and night d1 on Oxford RobotCar. If this holds, adverse-condition depth adaptation becomes a text-prompt problem rather than an image-generation or data-collection problem, at a cost of 0.035M added parameters.

What carries the argument

The load-bearing object is the low-rank adaptation matrix pair $W = W_0 + BA$ with $B \in \mathbb{R}^{d\times r}$, $A \in \mathbb{R}^{r\times k}$, and $r \ll \min(d,k)$, injected into query, key, value, and projection weights of the image encoder. What carries the argument is the alignment identity $\Delta V \approx \Delta L$: the source-to-target shift in image-feature space is supervised to equal the source-to-target shift in CLIP text-embedding space, so language supplies the direction in which the LoRA should move features. The second mechanism, VTCCL, is a contrastive loss that anchors each weather's visual representation to its own text prompt and repels other weather prompts, aligning the depth model's visual space with CLIP's text space. The efficiency claim rests on the LoRA matrices being the only trainable new parameters, 0.035M at rank 8, while the image and text encoders stay frozen.

What would settle it

Compute the cosine similarity between the LoRA-produced visual difference $\Delta V$ and the text difference $\Delta L$ after pre-training on a fixed validation set; if the method's depth gains persist on night and rain while this similarity is near zero or negative, the alignment loss is not doing the claimed work. Alternatively, train MMD-LoRA with the night and rain prompts swapped and compare d1 with the correctly prompted run, and if the accuracy barely changes, the text direction is irrelevant to the improvement.

Watch

Extended reading notes

Core claim

MMD-LoRA's central claim is that the domain gap between sunny and adverse driving conditions lives in a low-rank subspace of the image encoder's feature space, and that this subspace can be found from language rather than from target images. In a pre-training step, a frozen CLIP text encoder provides source and target text embeddings, $F_s^l$ and $F_t^l$, and a frozen image encoder with injected LoRA matrices produces visual representations $F_s^v$ and $F_t^v$; the alignment loss $\mathcal{L}_{\text{align}} = D(\Delta V, \Delta L) + \|F_t^v - F_s^v\|_1$ requires the visual direction of change $\Delta V$ to match the linguistic direction of change $\Delta L$, with an L1 term keeping the target representation near the source. VTCCL then applies contrastive learning so that each weather's visual representation matches its own text embedding and repels the others. After pre-training, the learned LoRA is frozen and injected into the query, key, value, and projection layers of the depth estimator's self-attention, and only the depth decoder is trained on ground-truth depth. The paper reports that this configuration surpasses all prior methods on both benchmarks, with the largest night and rain gains coming from the contrastive term.

Load-bearing premise

The load-bearing premise is that the CLIP text difference between "day" and "night" (or "rain") points in the same direction, in feature space, as the shift the depth model's visual features need to move from sunny to adverse conditions; if language and vision do not change in proportion, the alignment loss steers the LoRA wrongly and the reported gains collapse.

Editorial extensions

If this is right

  • Adapting to a new adverse condition requires only a text description; no target images, GANs, or diffusion-generated pseudo-targets are needed during adaptation.
  • The added cost is 0.035M parameters over the EVP baseline, compared with 0.604M for the learned-augmentation baseline, so the approach could be deployed on embedded driving hardware.
  • On nuScenes, d1 accuracy improves from 88.03% to 96.46% for clear day, from 75.33% to 79.96% for night, and from 82.82% to 95.37% for rain over the previous state of the art.
  • On Oxford RobotCar, day d1 rises from 87.17% to 92.56% and night d1 from 83.68% to 89.33%, showing the adaptation transfers across datasets.
  • The same frozen LoRA together with the text encoder also guides depth decoding via cross-attention, so multimodal alignment is part of the depth-estimation pipeline rather than a separate preprocessing step.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the same text-difference alignment could be tested for fog, snow, or sandstorm by swapping only the prompt description, since the method never needs target images; this extension is valid only if the cross-modal proportionality of Eq. 3 holds for those conditions.
  • The contrastive alignment between CLIP text and diffusion-model visual features may transfer to other dense prediction tasks whose encoders are not CLIP-aligned, such as semantic segmentation or optical flow under domain shift.
  • Because the VTCCL weights differ between nuScenes and RobotCar, per-domain calibration of contrastive weights still matters; an implicit extension would be to learn those weights or prompts automatically rather than setting them per dataset.
  • A strong sanity check not reported in the paper: train the LoRA with shuffled or random prompts; if the night and rain gains persist, the text direction is not the active ingredient.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. This paper proposes MMD-LoRA, a parameter-efficient method for adverse-condition monocular depth estimation. The approach injects low-rank adapters into the image encoder of an EVP backbone, guided by two losses: Prompt Driven Domain Alignment (PDDA), which encourages the source-to-target difference in visual features to match the corresponding difference in CLIP text embeddings, and Visual-Text Consistent Contrastive Learning (VTCCL), which aligns visual and text representations across weather conditions. The pre-trained LoRA is frozen and the depth estimator is then fine-tuned with ground-truth depth. Experiments on nuScenes and Oxford RobotCar are reported, claiming state-of-the-art results.

Significance. If the reported results are reproducible, the paper makes a meaningful contribution toward target-image-free, parameter-efficient adaptation of depth estimators to adverse weather. The use of text descriptions for target conditions is practical, and the parameter overhead is small (0.035M for rank 8). The paper provides explicit equations for all losses and sufficient implementation detail for reimplementation. However, the central claims are weakened by the missing PDDA-only ablation and the omission of the EVP backbone from the main comparison table; the margins over the backbone are small (0.76% d1 on day-clear, 5.47% on night, 0.85% on day-rain), so the claimed SOTA gains are not yet convincingly attributable to the proposed mechanism.

major comments (3)
  1. [§IV-D, Table III] The ablation study never reports the PDDA-only condition, leaving the central mechanism unsubstantiated. The row with '× ✓' (PDDA off, VTCCL on) already achieves d1 of 96.09/80.46/94.09, which is close to or above the full method's 96.46/79.96/95.37. The text's claim that 'MMD-LoRA with PDDA still outperforms the EVP by 0.39% and 5.97%' matches the VTCCL-only row, not any PDDA-only result. Please add the PDDA-only row and, ideally, report multiple seeds to assess whether the small differences are significant.
  2. [§IV-C, Table I] The SOTA comparison table omits EVP [4], the very backbone of MMD-LoRA. Table IV shows that MMD-LoRA's gains over EVP are only 0.76% d1 on day-clear, 5.47% on night, and 0.85% on day-rain, whereas the claimed gains over md4all-AD are 8.43%, 4.63%, and 12.55%. The large SOTA margin is therefore largely a consequence of comparing to a much weaker baseline. EVP should be included in Table I, and the narrative should be reframed around the improvement over the backbone.
  3. [§III-A/B, Eqs. (2)–(4)] The method defines a single LoRA transform (Eq. 2) that produces target-domain features F_v_t, but Eq. (4) requires distinct target visual features for multiple adverse conditions (e.g., night and rain). Since a single low-rank update can realize only one shift direction, it is unclear how one LoRA can align with multiple text-difference directions ΔL. The paper does not specify whether the LoRA is conditioned on the text prompt or whether separate LoRA matrices per domain are used; per-domain parameter counts are also missing. This is a conceptual gap that needs to be resolved for the pre-training objective to be well-defined.
minor comments (7)
  1. [Table I] The row 'DepthAnything with ft.' duplicates the exact numbers of the 'Depth Anything' row above it, which appears to be a copy-paste error; either the fine-tuned variant should be reported with distinct numbers or the duplicate row should be removed.
  2. [Fig. 3 and Fig. 4 captions] The captions refer to 'our proposed LDDG' instead of MMD-LoRA, suggesting an incomplete revision from an earlier manuscript.
  3. [§III-A, after Eq. (3)] In the sentence '∆L denotes the difference of F_l_s and F_l_t by text encoder V (·)', the encoder should be T(·), not V(·).
  4. [Introduction and §IV-D] The acronym 'PPDA' is used in several places (e.g., 'our PPDA as a learned model-based method' and 'MMD-LoRA with PPDA') where 'PDDA' is meant.
  5. [§III-B, Eq. (4)] The contrastive loss is not fully specified: the sum over N crops is omitted, and for target-domain anchors the negative samples include only the source text, not the other target-domain text descriptions; please provide the complete formulation.
  6. [Table IV] The adaptation of 'learned augment [12]' to depth estimation is not described; since [12] is an object detection method, please specify how it was applied to the EVP depth model.
  7. [General experimental reporting] No error bars or multiple-seed results are reported; given the small margins over the backbone (e.g., 0.76% d1 on day-clear), the reported differences may not be statistically significant.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MMD-LoRA's text-guided alignment is an externally evaluated training objective, not a fitted prediction.

full rationale

The derivation chain is self-contained against external benchmarks. PDDA's alignment loss (Eq. 3: Lalign = D(ΔV, ΔL) + ||F_v_t - F_v_s||1) is a well-defined training objective that uses frozen CLIP text embeddings as a source of supervision; the paper explicitly states that 'the text encoder T(·) and image encoder V(·) are frozen' and that 'the training images only include day-clear images without adverse weather images.' The LoRA matrices are optimized to minimize this objective, and the resulting target-domain features are then evaluated by depth accuracy on nuScenes and Oxford RobotCar. No parameter is fitted to the reported d1 metrics, no quantity is renamed as a prediction, and no load-bearing claim is justified solely by a self-citation; the only self-citation (ref. [39] in the multi-modality alignment cluster) is not load-bearing. The appended Limitations section acknowledges dependence on predefined text descriptions and a constant-brightness assumption, which are scope conditions rather than circular definitions. One non-circular evidentiary gap should be flagged: Table III omits the PDDA-only condition, and the text's statement that 'MMD-LoRA with PDDA' improves over EVP by 0.39%/5.97% numerically matches the VTCCL-only row rather than any PDDA-only row. This weakens the attribution of gains to PDDA but does not make the method's formulation circular, because the full system is still compared against previously published state-of-the-art methods and external datasets.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central method relies on cross-modal difference alignment and the capacity of a single low-rank adapter to encode multiple weather shifts. These are domain assumptions not proved in the paper; the free parameters are the LoRA rank and the VTCCL loss weights, tuned per dataset.

free parameters (3)
  • LoRA rank r = 8
    Selected via ablation in Table V; r=8 gives best overall d1 across nuScenes and RobotCar, with larger r degrading night performance (overfitting to text descriptions).
  • Contrastive loss weights lambda = nuScenes: lambda0=1, lambda1=0.1, lambda2=1; RobotCar: lambda0=1, lambda1=0.05
    Hand-tuned per dataset in VTCCL (Section IV-B); affects the balance between source-weather and target-weather contrastive terms.
  • Temperature tau in Eq. 4 = not specified
    The temperature parameter in the contrastive softmax is not stated in the paper; presumably defaults to the CLIP value (e.g., 0.07), but this is an unstated hyperparameter that affects the loss scale.
assumptions (4)
  • domain assumption Text embedding differences between weather descriptions are proportional to visual feature differences in the depth model's image encoder space (Eq. 3).
    PDDA minimizes D(ΔV, ΔL) where ΔV = F_v_t - F_v_s and ΔL = F_l_t - F_l_s. This assumes CLIP text space and the diffusion image encoder space share a linear-ish direction semantics, which is not independently verified.
  • domain assumption A single low-rank LoRA (rank 8) can simultaneously represent multiple target-domain shifts (night, rain).
    The same adapter matrices B and A are used for all adverse weather conditions during inference; the method assumes one low-rank subspace can encode all domain gaps.
  • domain assumption The pre-trained CLIP text encoder and diffusion-based image encoder are compatible enough for contrastive alignment (Eq. 4).
    VTCCL pulls visual and text features of the same weather together; this relies on both encoders having comparable semantics despite different training objectives.
  • domain assumption The baseline EVP depth estimator provides image captions and cross-attention between text and visual features during training (Section III-C).
    The method inherits EVP's text-conditioned decoder; if captions are unavailable or misaligned, the depth decoder's text guidance fails.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Multi-Modality Driven LoRA for Adverse Condition Depth Estimation." pith.science (2026). https://pith.science/paper/3UQADXVI

@misc{pith2026241220162,
  author       = {Pith},
  title        = {Pith review of: Multi-Modality Driven LoRA for Adverse Condition Depth Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3UQADXVI}},
  note         = {Machine review of arXiv:2412.20162}
}
read the original abstract

The autonomous driving community is increasingly focused on addressing corner case problems, particularly those related to ensuring driving safety under adverse conditions (e.g., nighttime, fog, rain). To this end, the task of Adverse Condition Depth Estimation (ACDE) has gained significant attention. Previous approaches in ACDE have primarily relied on generative models, which necessitate additional target images to convert the sunny condition into adverse weather, or learnable parameters for feature augmentation to adapt domain gaps, resulting in increased model complexity and tuning efforts. Furthermore, unlike CLIP-based methods where textual and visual features have been pre-aligned, depth estimation models lack sufficient alignment between multimodal features, hindering coherent understanding under adverse conditions. To address these limitations, we propose Multi-Modality Driven LoRA (MMD-LoRA), which leverages low-rank adaptation matrices for efficient fine-tuning from source-domain to target-domain. It consists of two core components: Prompt Driven Domain Alignment (PDDA) and Visual-Text Consistent Contrastive Learning(VTCCL). During PDDA, the image encoder with MMD-LoRA generates target-domain visual representations, supervised by alignment loss that the source-target difference between language and image should be equal. Meanwhile, VTCCL bridges the gap between textual features from CLIP and visual features from diffusion model, pushing apart different weather representations (vision and text) and bringing together similar ones. Through extensive experiments, the proposed method achieves state-of-the-art performance on the nuScenes and Oxford RobotCar datasets, underscoring robustness and efficiency in adapting to varied adverse environments.

Figures

Figures reproduced from arXiv: 2412.20162 by the authors.

Figure 1
Figure 1. Comparison of the baseline depth estimation [4] with learned [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The overview of the MMD-LoRA pipeline including the pre-training step and training step. In the pre-training step, MMD-LoRA captures accurate [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Qualitative results of our proposed MMD-LoRA and the previous SOTA depth estimation method on the nuScenes validation sets. The first column [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Qualitative results of MMD-LoRA and the previous SOTA depth estimation method on the Robotcar test set. The first column denotes the original [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Ablation visualization of our proposed MMD-LoRA with PDDA and VTCCL. The first column denotes the original image. The second, third and [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 27 canonical work pages

  1. [4]

    Evp: Enhanced visual perception using inverse multi-attentive feature refinement and regularized image-text alignment,

    M. Lavreniuk, S. F. Bhat, M. M ¨uller, and P. Wonka, “Evp: Enhanced visual perception using inverse multi-attentive feature refinement and regularized image-text alignment,” arXiv preprint arXiv:2312.08548 , 2023

    arXiv 2023

  2. [1]

    Ro- bodepth: Robust out-of-distribution depth estimation under corruptions,

    L. Kong, S. Xie, H. Hu, L. X. Ng, B. Cottereau, and W. T. Ooi, “Ro- bodepth: Robust out-of-distribution depth estimation under corruptions,” Advances in Neural Information Processing Systems , vol. 36, 2024

    2024

  3. [2]

    Adabins: Depth estimation using adaptive bins,

    S. F. Bhat, I. Alhashim, and P. Wonka, “Adabins: Depth estimation using adaptive bins,” inProceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition , 2021, pp. 4009–4018

    work page 2021

  4. [3]

    Transformer-based attention networks for continuous pixel-wise prediction,

    G. Yang, H. Tang, M. Ding, N. Sebe, and E. Ricci, “Transformer-based attention networks for continuous pixel-wise prediction,” in Proceedings of the IEEE/CVF International Conference on Computer vision , 2021, pp. 16 269–16 279

    work page 2021

  5. [5]

    Repurposing diffusion-based image generators for monoc- ular depth estimation,

    B. Ke, A. Obukhov, S. Huang, N. Metzger, R. C. Daudt, and K. Schindler, “Repurposing diffusion-based image generators for monoc- ular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 9492–9502

    2024

  6. [6]

    Planning-oriented autonomous driving,

    Y . Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, et al. , “Planning-oriented autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 17 853–17 862

    work page 2023

  7. [7]

    Robust monocular depth estimation under challenging conditions,

    S. Gasperini, N. Morbitzer, H. Jung, N. Navab, and F. Tombari, “Robust monocular depth estimation under challenging conditions,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 8177–8186

    2023

  8. [8]

    Diffusion models for monoc- ular depth estimation: Overcoming challenging conditions,

    F. Tosi, P. Zama Ramirez, and M. Poggi, “Diffusion models for monoc- ular depth estimation: Overcoming challenging conditions,” in European Conference on Computer Vision (ECCV) , 2024

    work page 2024

Show all 44 references
  1. [9]

    R4dyn: Exploring radar for self-supervised monocular depth estimation of dynamic scenes,

    S. Gasperini, P. Koch, V . Dallabetta, N. Navab, B. Busam, and F. Tombari, “R4dyn: Exploring radar for self-supervised monocular depth estimation of dynamic scenes,” in 2021 International Conference on 3D Vision (3DV) . IEEE, 2021, pp. 751–760

    2021

  2. [10]

    Defeat-net: General monocular depth via simultaneous unsupervised representation learning,

    J. Spencer, R. Bowden, and S. Hadfield, “Defeat-net: General monocular depth via simultaneous unsupervised representation learning,” in Pro- ceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition, 2020, pp. 14 402–14 413

    2020

  3. [12]

    Clip the gap: A single domain generalization approach for object detection,

    V . Vidit, M. Engilberge, and M. Salzmann, “Clip the gap: A single domain generalization approach for object detection,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition , 2023, pp. 3219–3229

    2023

  4. [13]

    Lora: Low-rank adaptation of large language models,

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685 , 2021

    2021 arXiv

  5. [14]

    Sqldepth: Generalizable self-supervised fine-structured monocular depth estimation,

    Y . Wang, Y . Liang, H. Xu, S. Jiao, and H. Yu, “Sqldepth: Generalizable self-supervised fine-structured monocular depth estimation,” in Proceed- ings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 6, 2024, pp. 5713–5721

    2024

  6. [15]

    Towards zero-shot scale-aware monocular depth estimation,

    V . Guizilini, I. Vasiljevic, D. Chen, R. Ambrus,, and A. Gaidon, “Towards zero-shot scale-aware monocular depth estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 9233–9243

    2023

  7. [16]

    Zoedepth: Zero-shot transfer by combining relative and metric depth,

    S. Bhat, R. Birkl, D. Wofk, P. Wonka, and M. Muller, “Zoedepth: Zero-shot transfer by combining relative and metric depth,” ArXiv, vol. abs/2302.12288, 2023. [Online]. Available: https://api.semanticscholar. org/CorpusID:257205739

    2023 arXiv

  8. [17]

    Depth anything: Unleashing the power of large-scale unlabeled data,

    L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao, “Depth anything: Unleashing the power of large-scale unlabeled data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 10 371–10 381

    2024

  9. [18]

    Forkgan: Seeing into the rainy night,

    Z. Zheng, Y . Wu, X. Han, and J. Shi, “Forkgan: Seeing into the rainy night,” in European Conference on Computer Vision (ECCV) , 2020, pp. 155–170

    2020

  10. [19]

    Single domain generalization for lidar semantic segmentation,

    H. Kim, Y . Kang, C. Oh, and K.-J. Yoon, “Single domain generalization for lidar semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 587–17 598

    2023

  11. [20]

    Turning a clip model into a scene text spotter,

    W. Yu, Y . Liu, X. Zhu, H. Cao, X. Sun, and X. Bai, “Turning a clip model into a scene text spotter,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024

    2024

  12. [21]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al., “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning . PMLR, 2021, pp. 8748–8763

    2021

  13. [22]

    T2vlad: global-local sequence alignment for text-video retrieval,

    X. Wang, L. Zhu, and Y . Yang, “T2vlad: global-local sequence alignment for text-video retrieval,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 5079–5088

    2021

  14. [23]

    What you see is what you read? improv- ing text-image alignment evaluation,

    M. Yarom, Y . Bitton, S. Changpinyo, R. Aharoni, J. Herzig, O. Lang, E. Ofek, and I. Szpektor, “What you see is what you read? improv- ing text-image alignment evaluation,” Advances in Neural Information Processing Systems, vol. 36, 2024

    2024

  15. [24]

    Clip-vip: Adapting pre-trained image-text model to video-language alignment,

    H. Xue, Y . Sun, B. Liu, J. Fu, R. Song, H. Li, and J. Luo, “Clip-vip: Adapting pre-trained image-text model to video-language alignment,” in The Eleventh International Conference on Learning Representations , 2023

    2023

  16. [25]

    Unleashing text-to-image diffusion models for visual perception,

    W. Zhao, Y . Rao, Z. Liu, B. Liu, J. Zhou, and J. Lu, “Unleashing text-to-image diffusion models for visual perception,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 5729–5739

    2023

  17. [26]

    Hierarchical text-conditional image generation with clip latents,

    A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” arXiv preprint arXiv:2204.06125, vol. 1, no. 2, p. 3, 2022

    2022 arXiv

  18. [27]

    Self-supervised monocular depth estimation for all day images using domain separation

    L. Liu, X. Song, M. Wang, Y . Liu, and L. Zhang, “Self-supervised monocular depth estimation for all day images using domain separation.” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 12 717–12 726

    2021

  19. [28]

    Learning depth estimation for transparent and mirror surfaces,

    A. Costanzino, P. Z. Ramirez, M. Poggi, F. Tosi, S. Mattoccia, and L. Di Stefano, “Learning depth estimation for transparent and mirror surfaces,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 9244–9255

    2023

  20. [29]

    Regularizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark,

    K. Wang, Z. Zhang, Z. Yan, X. Li, B. Xu, J. Li, and J. Yang, “Regularizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark,” in Proceedings of IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 055–16 064

    2021

  21. [30]

    Self-supervised monocular depth estimation: Let’s talk about the weather,

    K. Saunders, G. V ogiatzis, and L. J. Manso, “Self-supervised monocular depth estimation: Let’s talk about the weather,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8907–8917

    2023

  22. [31]

    Towards robust monocular depth estimation: Mixing datasets for zero-shot cross- dataset transfer,

    R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V . Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross- dataset transfer,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 3, pp. 1623–1637, 2020

    2020

  23. [32]

    Unidepth: Universal monocular metric depth estimation,

    L. Piccinelli, Y .-H. Yang, C. Sakaridis, M. Segu, S. Li, L. Van Gool, and F. Yu, “Unidepth: Universal monocular metric depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 10 106–10 116

    2024

  24. [33]

    Metric3d: Towards zero-shot metric 3d prediction from a sin- gle image,

    W. Yin, C. Zhang, H. Chen, Z. Cai, G. Yu, K. Wang, X. Chen, and C. Shen, “Metric3d: Towards zero-shot metric 3d prediction from a sin- gle image,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 9043–9053

    2023

  25. [34]

    Can language understand depth?

    R. Zhang, Z. Zeng, Z. Guo, and Y . Li, “Can language understand depth?” in Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 6868–6874

    2022

  26. [35]

    Clip-decoder: Zeroshot multilabel classification using multimodal clip aligned representations,

    M. Ali and S. Khan, “Clip-decoder: Zeroshot multilabel classification using multimodal clip aligned representations,” in Proceedings of the JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 9 IEEE/CVF International Conference on Computer Vision , 2023, pp. 4675–4679

    2021

  27. [36]

    Cora: Adapting clip for open- vocabulary detection with region prompting and anchor pre-matching,

    X. Wu, F. Zhu, R. Zhao, and H. Li, “Cora: Adapting clip for open- vocabulary detection with region prompting and anchor pre-matching,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition, 2023, pp. 7031–7040

    2023

  28. [37]

    Text-to-concept (and back) via cross-model alignment,

    M. Moayeri, K. Rezaei, M. Sanjabi, and S. Feizi, “Text-to-concept (and back) via cross-model alignment,” in International Conference on Machine Learning, 2023, pp. 25 037–25 060

    2023

  29. [38]

    Extract free dense labels from clip,

    C. Zhou, C. C. Loy, and B. Dai, “Extract free dense labels from clip,” in European Conference on Computer Vision. Springer, 2022, pp. 696– 712

    2022

  30. [39]

    Con- trolvideo: Training-free controllable text-to-video generation,

    Y . Zhang, Y . Wei, D. Jiang, X. Zhang, W. Zuo, and Q. Tian, “Con- trolvideo: Training-free controllable text-to-video generation,” arXiv preprint arXiv:2305.13077, 2023

    2023 arXiv

  31. [40]

    Digging into self-supervised monocular depth estimation,

    C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow, “Digging into self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 3828– 3838

    2019

  32. [41]

    3d pack- ing for self-supervised monocular depth estimation,

    V . Guizilini, R. Ambrus, S. Pillai, A. Raventos, and A. Gaidon, “3d pack- ing for self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition , 2020, pp. 2485–2494

    2020

  33. [42]

    Self-supervised monocular depth estimation in the dark: Towards data distribution compensation,

    H. Yang, C. Zhao, L. Sheng, and Y . Tang, “Self-supervised monocular depth estimation in the dark: Towards data distribution compensation,” Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, pp. 1561–1569, 2024

    2024

  34. [43]

    nuscenes: A multi- modal dataset for autonomous driving,

    H. Caesar, V . Bankiti, A. H. Lang, S. V ora, V . E. Liong, Q. Xu, A. Krishnan, Y . Pan, G. Baldan, and O. Beijbom, “nuscenes: A multi- modal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition , 2020, pp. 11 621–11 631

    2020

  35. [44]

    1 year, 1000 km: The oxford robotcar dataset,

    W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 year, 1000 km: The oxford robotcar dataset,” The International Journal of Robotics Research, vol. 36, no. 1, pp. 3–15, 2017

    2017

  36. [45]

    When the sun goes down: Repairing photometric losses for all-day depth estimation,

    M. Vankadari, S. Golodetz, S. Garg, S. Shin, A. Markham, and N. Trigoni, “When the sun goes down: Repairing photometric losses for all-day depth estimation,” in Conference on Robot Learning , 2023, pp. 1992–2003

    2023

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.