Title: When does perceptual alignment benefit vision representations?
Venue: NeurIPS 2024
Accepted: yes
Keywords: representation learning

alignment

perception

transfer learning

computer vision

foundation model
Abstract: Humans judge perceptual similarity according to diverse visual attributes, including scene layout, subject location, and camera pose. Existing vision models understand a wide range of semantic abstractions but improperly weigh these attributes and thus make inferences misaligned with human perception. 
While vision representations have previously benefited from human preference alignment in contexts like image generation, the utility of perceptually aligned representations in more general-purpose settings remains unclear. Here, we investigate how aligning vision model representations to human perceptual judgments impacts their usability in standard computer vision tasks. We finetune state-of-the-art models on a dataset of human similarity judgments for synthetic image triplets and evaluate them across diverse computer vision tasks. We find that aligning models to perceptual judgments yields representations that improve upon the original backbones across many downstream tasks, including counting, semantic segmentation, depth estimation, instance retrieval, and retrieval-augmented generation. In addition, we find that performance is widely preserved on other tasks, including specialized out-of-distribution domains such as in medical imaging and 3D environment frames. Our results suggest that injecting an inductive bias about human perceptual knowledge into vision models can make them better representation learners.

When Does Perceptual Alignment Benefit Vision Representations?

Abstract

Humans judge perceptual similarity according to diverse visual attributes, including scene layout, subject location, and camera pose. Existing vision models understand a wide range of semantic abstractions but improperly weigh these attributes and thus make inferences misaligned with human perception. While vision representations have previously benefited from alignment in contexts like image generation, the utility of perceptually aligned representations in general-purpose settings remains unclear. Here, we investigate how aligning vision representations to human perceptual judgments impacts their usability across diverse vision tasks. We finetune state-of-the-art models on human similarity judgments for image triplets and evaluate them across standard benchmarks. We find that perceptual alignment yields representations that improve upon the original backbones across many tasks, including counting, segmentation, depth estimation, instance retrieval, and retrieval-augmented generation, while deteriorating performance on natural classification. Performance is widely preserved on other tasks, including specialized out-of-distribution domains such as in medical imaging and 3D environment frames. Our results suggest that injecting an inductive bias about human perceptual knowledge into vision models can contribute to better representations.

1 Introduction

Our sense of similarity is crucial to how we perceive and act in the world. Consider the range of factors that might influence your judgment of visual similarity: layout, color, perspective, semantics, and more. These characteristics shape our local inferences about object relationships, enabling us to build a comprehensive understanding of the visual world.

Given the importance of similarity judgments in our visual perception, it follows that aligning vision models to these judgments could help them develop more human-like visual capabilities. In the language domain, we have seen the power of aligning LLMs to human feedback [RLHF; 9], resulting in safer and more useful models [3]. Similar trends are emerging in vision, where there is growing interest in alignment with human perceptual judgments to improve vision representations [e.g., 61, 17, 42, 18, 43]. Human feedback has also been used to improve the aesthetic quality of diffusiongenerated images [58, 16, 30], increase retrieval capabilities [18], and improve downstream task performance of image/text models [42]. While acknowledging this wide space of human preference data used for alignment in vision and language, we focus our study on image similarity judgments, which give us a more stable and shared measure of human visual perception [18, 41, 54, 43].

Although there is consensus that alignment to perceptual judgements can enable specific goals (e.g., similarity [18, 61, 41, 42]), their utility in creating general-purpose representations is less clear. Does human alignment improve model performance by leveraging human-provided labels as an inductive bias, or compromise the model's original representational power by diverting it towards a separate task? Recent work suggests that the conclusions are nuanced: naively incorporating human perceptual knowledge into model finetuning can distort representations, requiring strong regularization to maintain downstream performance while increasing the representations' interpretability and alignment [42]. Furthermore, objective function and training data appear to matter more than architecture or model size for aligning to perceptual judgments [41]. The nature of human preference labels also plays a crucial role, as different types of labels produce distinct learning signals. For instance, fine-tuning vision backbones with mid-level perceptual similarity judgments yields representations well-suited for image retrieval tasks [18], while low-level similarity labels are more effective for image reconstruction losses [61].

It is clear from this body of work that careful perceptual alignment helps with specific tasks, however the effect of these adjustments on the models' representation spaces is less well understood. That is, are models aligned to perceptual judgments only better at specific tasks - such as predicting image similarity - or do they, like humans, actually have better general-purpose representations? Here, we evaluate the usefulness of human-aligned representations not just in predicting perceptual judgments but also on standard vision benchmarks requiring diverse notions of visual understanding. We finetune several state-of-the-art models - including CLIP, DINO, DINOv2, and SynCLR - on NIGHTS, a dataset of human similarity judgments over synthetic image triplets [18], and evaluate the finetuned models on standard tasks. Our experiments suggest that these human-aligned representations demonstrate strong gains compared to the original model backbones, even on tasks requiring skills beyond those needed during training or perceptual alignment. We also identify limitations of human perceptual alignment, and find that finetuning can sacrifice performance on some natural data tasks in which models had strong prior performance. In summary, our contributions are the following:

We investigate the effects of aligning pretrained vision models to human perceptual judgments on various downstream visual recognition tasks.

Wefind that propagating global image-level human similarity annotations to ViT patch tokens benefits downstream dense prediction tasks such as depth prediction and segmentation.

We show that human-aligned representations are also beneficial in retrieval-based tasks requiring global image understanding, including retrieval-augmented generation for recent vision-language models, counting-based retrieval, and instance-based retrieval.

We ablate the effect of various human-annotated visual similarity datasets, and find that mid-level image similarity, as opposed to low-level pixel-level variations or high-level semantic associations, offers the largest improvements in generalization capabilities.

2 Related Works

Vision backbones as learned feature extractors. Using the intermediate activations of deep networks has been a long-standing strategy, originally used as a way of harnessing data-driven priors

from models trained on fewer large datasets and repurposing them for downstream tasks where such expensive supervision is less readily available [12, 50]. For instance, ImageNet pretrained models have proven useful for tasks such as texture synthesis [19], style-transfer [20], and super-resolution [27]. But given the challenges of collecting large-scale labelled datasets using human annotators, other approaches such as self-supervised learning [6, 7, 59, 4, 44] and contrastive learning on image/alt text pairs [46, 8, 26] have since superseded supervised learning as the standard approach for training vision models. Such models have been shown to yield rich multipurpose representations that can generalize to a variety of tasks involving visual perception, scene understanding, and image generation [51, 38, 52, 31, 39, 57].

View selection for self-supervised learning. The self-supervised, contrastive-learning objective aims to maximize the feature similarity between similar views of the data and minimize the similarity between different (negative) views. While originally the negative views are selected randomly from a pool of images [6, 22], this often results in requiring large batch sizes to learn useful representations. The process of selecting these positive and negative learning examples remains an active research area. Recent approaches have suggested that alternative strategies including hard-negative mining [47], nearest neighbors for positive pairs [14], and supervised labels when available [28] can provide useful learning signals. Other avenues include training the contrastive framework on synthetic data from generative models [25, 55], which provides an infinite data source of image variations rather than the traditional data augmentations typically used to generate alternative views.

Learning with human alignment. Learning directly from human feedback can provide models with more targeted supervision using fewer examples [9], and, thus, has been beneficial for fine-tuning large models towards specific human preferences [53, 62]. Ding et al. [11] show that image similarity metrics, trained on human feedback, can be subsequently used for evaluating the diversity of a text-to-image model. Several datasets aim to annotate human visual preferences in image similarity, including low-level [61] and high-level [23] image variations. In particular, Zhang et al. [61] and Fu et al. [18] use these datasets to learn a more human-aligned perceptual metric that improves the image retrieval abilities of vision models, while Muttenthaler et al. [42] used THINGS for learning a linear transform on top of vision representations to increase downstream task performance and improve alignment with human similarity judgments. These annotated human preference datasets serve as useful signals for contrastive objectives, providing direct supervision for positive and negative pairings that are aligned with human decisions; here, we investigate how fine-tuning self-supervised models using these human annotations impacts model performance on various downstream tasks.

3 Learning from perceptual judgments

We propose to use the method described below as a "second pretraining stage", which aligns the feature representations from large vision models with human perceptual judgments before applying them to downstream tasks. We note that prior work on this dataset aimed to develop a model for measuring image similarity based on human judgments. Here, we investigate if pretraining on this dataset leads to a better general-purpose representation, as measured by performance on different downstream tasks.

3.1 Human Similarity Annotations

We use the NIGHTS dataset to produce human-aligned variations of several large vision models [18]. The NIGHTS dataset consists of 20k synthetically generated image triplets, annotated with two alternative forced-choice human similarity judgments. These triplets are collected so that each has 6-10 unanimous human ratings, thus eliminating ambiguous cases where humans are likely to disagree.

NIGHTS consists of image triplets varying in mid-level information. Images in a triplet roughly share the same semantic content; however, they vary in pose, layout, shape, color, and the number of objects (see Fig. 15 in the Appendix for examples). Thus, the perceptual judgments indicate the shared visual appearance properties, as opposed to requiring higher-level semantic knowledge about the image content.

3.2 Image-level objective

Given a pre-trained backbone f θ , we fine-tune its parameters θ on a dataset of triplets D = { ( x, ˜ x 0 , ˜ x 1 ) , y } , where x denotes a reference image, and ˜ x 0 and ˜ x 1 denote two variation images. The judgement y ∈ { 0 , 1 } indicates which of ˜ x 0 and ˜ x 1 is more similar to x . We measure distance

(dissimilarity) between two images ( x, ˜ x 0 ) using the cosine distance between their respective image features ( f θ ( x ) , f θ ( ˜ x 0 )) , which is defined as:

We use an alignment loss to encourage the model to match human preferences:

where ∆ d = d ( x, ˜ x 0 ) -d ( x, ˜ x 1 ) , ¯ y maps y ∈ { 0 , 1 } → {-1 , 1 } , and m is the margin, which we set to 0.05 following [18]. Note that this loss is equivalent to the triplet loss [5], and thus minimizes the cosine distance between the representations of the more similar pair and maximizes the distance between the representations of the other pair.

3.3 Patch-level objective

CLS

patch embeds

avgpool

The NIGHTS dataset contains global annotations of similarity at the image level, but the holistic label is the result of several local attributes, such as perspective, layout, foreground appearance etc. In addition to the global CLS token from the Vision Transformer model backbones, each model also contains a set of spatial patch embeddings. Propagating this global human annotation to individual patch tokens allows for spatial representations that are aligned with human similarity preferences. Thus, we formulate a patch alignment objective to optimize these local patch features jointly with the global image label.

The local objective only differs from the global objective in how the features are extracted. Instead of computing L (CLS A , CLS B ) , we compute

CLS is of dimension (1 , d ) and PATCH is ( s, s, d ) where s is the number of patches along each spatial dimension. We spatially average the patch tokens to get dimension (1 , d ) . We then concatenate the CLS and pooled patch tokens to get dimension (1 , 2 d ) . We fine-tune the same alignment loss (see Eq. 2) on concatenated CLS and averaged-pooled patch tokens, and train heads for semantic segmentation and depth estimation on the resulting patch embeddings. Note that only experiments reported in Sec. 4.1 use this objective, as they require local features. For all other evaluations, we exclusively use CLS tokens (see Eq. 2) if not mentioned otherwise.

3.4 Implementation details

Vision Model Backbones. We fine-tune several state-of-the-art Vision Transformer [VIT; 13] backbones including DINO [4], DINOv2 [44], CLIP [46], OpenCLIP [8], and SynCLR [55]. For DINO, DINOv2, MAE, and SynCLR, we use the CLS token of the final layer. We extract the CLS token before layer norm for MAE and after it for the other backbones. For CLIP and OpenCLIP, we use the representations of the image encoder. We also experiment with concatenated features from DINO, CLIP, and OpenCLIP, used to train the DreamSim Ensemble in [18] (referred to as "ensemble" in our results). For each model, we use its base size, ViT-B.

We implicitly ablate the usage of synthetic triplets in NIGHTS by including SynCLR in our experiments; as that backbone was contrastively trained on generated images, additional performance changes can be attributed to human perceptual alignment.

Finetuning with human preference labels. We finetune each backbone using Low-Rank Adaptation (LoRA), which was found to achieve better alignment performance and efficiency than full fine-tuning in [18]. For more training and technical details see Sections C.1 and C.6 in the Appendix.

4 Experiments

In this section, we evaluate perceptually-aligned backbones against base models on common vision tasks. We study global representations through instance retrieval, object-counting, and retrievalaugmented generation experiments. Additionally, we find that local patch-level representations can be improved by tuning on image-level perceptual judgments, and show performance increases on semantic segmentation and depth estimation.

4.1 Dense Prediction

Semantic segmentation. Following the procedure detailed in Section 3.3 and Fig. 2, we LoRAtune new backbones with perceptually-aligned CLS and patch tokens. To evaluate segmentation performance, we freeze these backbones and train a single linear layer transforming patch tokens to a segmentation map. We evaluate DINO and DINOv2 on standard segmentation benchmarks in Table 1 and show that human-aligned models boost performance in 16 out of 20 cases. Across all datasets and metrics, human-aligned DINO (denoted as DINO-HA) outperforms the base model and often achieves the highest mIoU and Pixel Accuracy (P.A.) overall. DINOv2-HA also outperforms its nonaligned counterpart on COCO and DAVIS2017.

We flag datasets already seen in the DINOv2 retrieval pretraining [44], such as Pascal VOC, ADE20k, and Cityscapes. If a dataset is already in-distribution for a backbone, fine-tuning on different data may be more likely to change the feature space such that that dataset is more out-of-distribution. Thus this is a potential confounding factor.

Depth estimation. We follow the evaluation protocol of [44, 37] and train a single linear layer on frozen patch tokens to output a depth map with values mapped into 256 uniformly-distributed bins. This head is trained with a scale-invariant log loss introduced in [15] and a scale-invariant gradient-matching term as described in [36]. In Table 2, we report performance on monocular depth estimation and show that human-aligned models outperform base models in 27 out of 36 cases. Consistent with segmentation performance, human-aligned DINO outperforms the base model on all metrics across all datasets, and is often the highest-performing model overall (denoted by † ). We also evaluate out-of-distribution generalization by training a depth head on NYUv2 and evaluating on the 4D Light Field dataset; combined with the performance boost even on datasets that DINOv2 was trained on (NYUv2 and SUN-RGBD), these results demonstrate that human-aligned models have strong generalization capabilities prior to any training for downstream tasks.

4.2 Retrieval-augmented generation

First introduced for text generation with non-parametric memory [34], retrieval-augmented generation (RAG) has become a popular method for selecting relevant few-shot examples when prompting large vision-language models (VLMs) [1, 2, 32, 33]. RAG evaluations go beyond conventional retrieval benchmarks on top-k recall, offering a more informative indicator of downstream large-model performance and utility in the multimodal domain. We evaluate OpenFlamingo [2]'s few-shot classification accuracy by using a vision backbone to retrieve a query image's 3 nearest neighbors and prepending the query image with those examples along with their class labels. Following OpenFlamingo's image classification evaluation framework [2], we extract the model's logits per

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Street View House Numbers

Diabetic Retinopathy

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object class and determine the model's decision by selecting the class with the largest log probability. See the Appendix for full details on the RAG experimental setup.

As illustrated in Fig. 3, classification accuracy on a wide variety of data domains improves with prompts retrieved by human-aligned models, compared to the original model backbones. Even in out-of-distribution domains such as medical imagery and 2D renders of game scenes, human-aligned models retrieve in-context examples that boost OpenFlamingo classification accuracy. These results suggest that human-aligned models can select more informative examples for in-context learning, thereby boosting the few-shot generalization abilities of a downstream multimodal VLM.

4.3 Counting

A well-documented limitation of large vision backbones is their performance on compositional tasks: in particular, on object counting [45]. We investigate how aligning to perceptual judgments affects performance on counting tasks via the FSC147, CARPK, and Clevr-Count (adapted from the original Clevr dataset by [60]) benchmarks by computer k-Nearest Neighbors accuracy on frozen vision representations. We report results in Table 3 and retrieval visualizations for few ( n = 3 ) and many ( n = 8 , 10 ) objects in Fig. 4 and find that, across 6 different models, the human-aligned versions outperform their counterparts in 35 out of 36 cases. See the Appendix for full details on the counting experimental setup. Given that the perceptual similarity dataset we use for finetuning

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contains image-level similarity judgments, the consistent improvements that we observe on counting tasks, which requires local object awareness, is somewhat surprising. We hypothesize that the sensitivity of our human-aligned models to object counts may be a byproduct of NIGHTS examples themselves, many of which include image triplets with varying numbers of objects (see Fig. 15 in the Appendix). In terms of human perception, we note that humans consider object count when evaluating image similarity as soon as they develop counting profiency [40]. Thus, it is possible that given the prevalence of triplets with object-count variations in NIGHTS, the human annotations naturally capture this counting aware effect in the global image-level labels and propagate this information to the human-aligned models.

4.4 Instance retrieval

In this section, we evaluate how aligning models to perceptual judgments affects their performance on the instance retrieval task. This task aims to retrieve images from a gallery containing a shared subject or object with the query image. At test time, retrieval is performed by computing the cosine similarity between the extracted features of the query and gallery images. Succeeding at this task requires that a representation be robust to recognizing instance identities under different lighting, backgrounds, poses, and other such shifts.

We evaluate all base models and their human-aligned counterparts on the Consumer-to-Shop benchmark of the DeepFashion2 dataset [21]. The benchmark consists of 10990 consumer "in the wild images" as the query set, and 21438 gallery images with matching clothing items to consumer images. Following the evaluation protocol from [21], we report Top-1, 3, and 5 accuracy in Table 4. Human aligned models outperform base

models by a significant margin across all metrics and backbones (visualized in Fig. 6. In Fig. 7 we provide qualitative retrieval results. These results agree with prior work showing that training on NIGHTS improves performance in retrieving similar images to queries [18].

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4.5 What type of human similarity annotation is most beneficial?

As evidenced by the current widespread use of large vision models [4, 44, 46], trained on massive datasets, model performance largely correlates with data scale. This raises the question: are our performance gains purely due to training the base models on additional data, or as a result of the perceptual qualities embedded in NIGHTS? To investigate this, we ablate the training dataset - rather than tune on 13,900 NIGHTS triplets, we train on three other image triplet datasets of the same size:

BAPPS [61]: Originally the training set for the LPIPS perceptual similarity metric [61], BAPPS consists of image patch triplets with various low-level distortions applied (e.g. color jitter, gaussian blur, JPEG compression artifacts).

THINGS [24]: This dataset contains image triplets with each image encoding a different concept (e.g. a triplet of images categorized as {airplane, elephant, football}), labeled by humans tasked to determine which concept is the odd-one-out.

ImageNet [48]: To ablate whether perceptual judgments at any level are needed, we construct an image triplet dataset by randomly selecting two images from one category and one image from another, labeling the first image pair as more similar to each other.

For all three datasets, we apply the same training settings as with the original LoRA-tuning on NIGHTS. See Fig. 8 for dataset ablations on object counting and instance retrieval.

Tuning on NIGHTS indeed provides the largest improvements across these tasks, with THINGS worsening performance overall and BAPPS/ImageNet having minimal effect. These trends may appear because BAPPS' photometric distortions are too low-level to impart any perceptual signal onto the backbones, THINGS encodes a higher-level conceptual similarity irrelevant to these mid-level vision tasks, and pre-trained vision models already perform quite well at discriminating ImageNet categories. Indeed, previous work [18] has found that similarity judgments by perceptual metrics trained on BAPPS correlate better to low-level metrics such as color than to semantic attributes.

In Section A.2 of the Appendix we find that the performance boost from NIGHTS over other datasets is also consistent across semantic segmentation and depth estimation. Conversely, tuning on NIGHTS fails to improve over other datasets on classification datasets; we further discuss this finding in Sections 5 and A.1.

5 Discussion

Recently, the vision research community has converged on the idea that using human perception to improve machine perception can bolster the transfer of vision representations to downstream tasks [61, 17, 41, 18, 42, 43]. However, it remained unclear which tasks benefit most from alignment with human perceptual judgments; different tasks may demand representations that encode different levels of granularity and semantics. Here, we fine-tune modern backbones, pretrained on different tasks, on human perceptual judgments [18] and subsequently investigate which downstream tasks benefit. We develop a better understanding of how perceptual alignment affects performance on important tasks - e.g., segmentation, depth estimation, RAG - which may in turn inform future model training and data curation decisions across different applications. By evaluating competency at downstream tasks, we quantify what these representations capture and make decodable, enabling a better understanding of the human-aligned feature space. While we probe representations in terms of competency, understanding them in terms of their mechanism is a rich direction for future work.

We find widespread benefits from perceptual alignment across both image- and patch-level tasks. At the global level, performance consistently improves for retrieval-augmented generation, countingbased and instance-based retrieval. Moreover, propagating the supervision from image-level similarity judgments to ViT patch tokens improves performance on dense prediction tasks (semantic segmentation and depth prediction). Since model performance is closely linked to data scale, we also ablate the choice of perceptual dataset. While fine-tuning on NIGHTS - a dataset of mid-level perceptual judgments - leads to improvements across various tasks, fine-tuning on triplets from ImageNet [10], BAPPS [61], and THINGS [23] preserves or deteriorates transfer performance.

Why does fine-tuning on NIGHTS in particular lead to improvements? We hypothesize that the variations found in BAPPS and THINGS are solely high- or low-level, whereas the mid-level distortions in NIGHTS cover salient features that humans use when making inferences about what they see; these characteristics include style, pose, color, and count (see Fig.15), and largely correlate with the characteristics a model must successfully extract for many computer vision tasks. Previous work [18] found that models fine-tuned on NIGHTS seem to attend to both low-level and semantic attributes. Aligning a feature space to these concepts may be useful for visual tasks requiring both visual and semantic knowledge, such as retrieval, counting, segmentation, etc. This hypothesis may

also explain why tuning on NIGHTS hurts performance on fine-grained tasks, in which perceptually similar images may belong to different categories.

Limitations. Perceptual alignment does not appear to improve performance for standard image classification tasks such as natural image datasets in the VTAB benchmark [60] (see Tables 5a-b in the Appendix). This is surprising in light of recent findings that demonstrate downstream task improvements in image classification tasks for human-aligned representations [42]. Although it is hard to pinpoint the exact cause, we hypothesize two reasons: First, perceptual judgments at different levels of abstraction may be helpful for different downstream tasks. While the mid-level perceptual judgments in NIGHTS boost performance for retrieval-based and dense prediction tasks, they may not impart a useful inductive bias for standard image classification tasks; high-level semantic associations could simply be better suited for these kinds of tasks. Alternatively, the visual features that humans use to judge similarity may not be appropriately captured in classification accuracy metrics. Some VTAB datasets have numerical ground truth in which the distance from the correct answer is meaningful; thus, it may be ill-suited for classification, and better suited to continuous evaluations, which we report in sections of the paper.

Additionally, a key insight from our dataset ablations is that not all human preferences improve performance. Finetuning on perceptual datasets with solely high-level (THINGS) or low-level (BAPPS) variations hurts performance for many downstream tasks (Section 4.5 and A.2). Similarly, Muttenthaler et al. [43] recently discovered that finetuning vision models on THINGS can hurt downstream task transfer. Due to our evaluation of the synthetically-pretrained SynCLR [55], we attribute the drop in performance to perceptual alignment. Furthermore, by ablating the number of fine-tuning steps in Section A.2, we show that overfitting to perceptual judgments may also harm downstream performance.

Societal impacts. Beyond our findings, there are other possibilities for harm outside the scope of the type of perceptual annotations we have studied: Human preferences may reflect unwanted biases. A long-standing problem in both language and vision is that biases (e.g. gender, racial) reflected in Internet language/images are inherited by large models, and reflected in their embeddings. One can imagine similar phenomena to happen in visual preferences [29]. Humans may disagree on a preference label, or even disagree with themselves if asked at different points in time. This may lead to noisy data if not filtered carefully, thus harming representations [54, 43]. Without sufficient demographic diversity in the annotator group, emerging biases may be reflected in the model. For example, some RLHF-trained language models have been shown to develop a bias towards the opinions of high-income, liberal individuals over their non-RLHF counterparts [49].

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Appendix

In the Appendix, Section A contains additional experiments and ablations, Section B contains qualitative results and examples from different perceptual datasets, and Section C contains methodological/implementation details.

A Experiments

A.1 Classification with the VTAB Benchmark

We evaluate on the Visual Task Adaptation Benchmark (VTAB) [60], a comprehensive set of nineteen classification datasets that is commonly used to study the transferability of representations. VTAB covers a broad set of domains beyond classic natural image datasets, including medical images, scene understanding, and fine-grained classification. Several of the datasets are drawn from domains that are not included in the NIGHTS dataset.

In Tables 5a-b, we show classification results on all VTAB datasets for each backbone, and its humanaligned version (denoted with -HA). The VTAB datasets are divided into three categories: Natural, Specialized, and Structured. Natural datasets include standard vision benchmarks; specialized datasets include benchmarks captured through specialized imaging equipment, such as satellites and microscopes; structured datasets include benchmarks that focus on structure and layout, with both naturally-captured and simulated images.

We find that human-aligned models largely lead to worse performance on standard natural tasks. Results on specialized tasks are mixed; for most datasets, human alignment helps or hurts by a marginal amount. Results on structured datasets (Table 6) are also mixed, however on particular datasets such as sNORB, dSprites-Orientation, and dSprites-Location, alignment improves performance across backbones. We remark, however, that these structured datasets may have limited evaluation utility. The Clevr-Count task, for instance, has a numerical ground truth (i.e. the number of objects in an image) in which the distance from the correct answer is meaningful; thus it may be ill-suited to classification, and better suited to continuous evaluations such as RMSE or MAE, which we report in Section 4.3.

A.2 Additional dataset ablations

In Section 4.5 we show that tuning on NIGHTS leads to larger performance improvements on object counting and instance retrieval than training on other triplet datasets. Here, we show that this finding is consistent across dense prediction tasks as well. We evaluate models trained on NIGHTS, BAPPS, THINGS, and ImageNet - as described in section 4.5 - on semantic segmentation and depth estimation. As shown in Fig. 9- 10, training on NIGHTS outperforms training on all other datasets.

We additionally run this ablation on a subset of VTAB (see Fig. 11a). On these classification datasets, base models perform best; the exception is sNORB (pose prediction) for which NIGHTS is best. Amongst perceptual datasets, NIGHTS is sometimes outperformed by BAPPS/ImageNet. This result is consistent with our findings in Section A.1, that tuning on NIGHTS often fails to improve classification performance.

Finally, we ablate the strength of alignment - i.e. the training loss when fine-tuning on different perceptual datasets. In Fig. 11b we show the downstream performance on DeepFashion2 when fine-tuning for increasing numbers of steps on different datasets. Tuning on NIGHTS outperforms other datasets over the full training trajectory. Moreover, while performance rises significantly with a small amount of alignment to NIGHTS, it trends down after >1000 steps. This indicates that a small amount of alignment is helpful, however overfitting may harm performance.

A.3 Additional RAG results

We replicate our RAG experiment on OpenFlamingo in Section 4.2 on IDEFICS2, a recently released 8B multimodal model achieving state-of-the-art results across several benchmarks [56]. As shown in Fig. 12, across the same four classification datasets, performance consistently improved when using NIGHTS-tuned models in the RAG pipeline. These results validate our RAG results on OpenFlamingo, suggesting that performance gains from perceptual alignment are not specific to a particular VLM.

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A.4 Perceptual alignment for CNNs

Our main experiments assess the effects of perceptual alignment for an array of ViT models, as these are state-of-the-art pretrained vision representations. Of further interest, however, is whether the same effects are observed for pre-trained CNNs as well.

We assess the effects of perceptual alignment with NIGHTS on two popular CNNs: ResNet50 and ConvNeXt-B, using the same loss described in Section 3.2. We evaluate on counting and instance retrieval tasks; our results are shown in Tables 7a-b. Similarly to ViTs, human-aligned CNNs outperform their pretrained counterparts across both tasks. Note that we report results for both ConvNeXt and ResNet, however acknowledge that the accuracies for ResNet are likely too small to draw conclusions from.

Due to a lack of open-source LoRA implementations for CNNs, we instead train MLPs on top of the respective final layers. Previous work [18] indicated that training MLPs with NIGHTS can still affect representations, but may be less effective than full fine-tuning. Nonetheless, we still see downstream improvements with this method.

B Qualitative examples

B.1 Additional visualizations

See Figures 13 and 14 for additional examples of instance retrieval on DeepFashion2 and object counting on Clevr-count.

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See Figure 15 for examples of the triplet datasets we train on in this paper. In each triplet, the reference image (middle) and outlined image are labeled as the similar pair. Perceptually-aligned models in this paper are tuned on NIGHTS triplets (top row), whose image variations encompass a variety of mid-level perceptual attributes including object count, identity, layout, subject pose, and color. In contrast, the datasets studied in our ablation encompass differing image attributes: BAPPS

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focuses on image patches with low-level distortions such as CNN artifacts and color jitter, THINGS encodes similarity in concept space, and our constructed ImageNet triplets outline class boundaries.

C Implementation Details

C.1 Training human-aligned models

The human-aligned models studied in this paper were fine-tuned with LoRA parameters: r = 16 , α = 0 . 5 , p = 0 . 0 . All contrastive training (for image- and patch-level objectives) are trained with an Adam optimizer ( lr = 0 . 0003 ), batch size of 16, and a hinge loss margin of m = 0 . 05 as detailed in [18]. We train all models for 8 epochs and evaluate on the checkpoint with the lowest validation loss. Train/val/test splits on NIGHTS, BAPPS, and THINGS were used as-provided in the dataset.

C.1.1 Dense prediction heads

Each semantic segmentation head is a single linear layer mapping frozen DINO/DINOv2 patch tokens to a segmentation map. We train with the Adam optimizer ( α = 0 . 0003 ) for 10 epochs with a batch size of 16 and 4 workers. The semantic segmentation head is optimized with a Jaccard Index loss (equivalently, mean Intersection-over-Union) defined as J ( A,B ) = | A ∩ B | | A ∪ B | .

Dense prediction heads are defined as a single linear layer mapping frozen DINO/DINOv2 patch tokens to a spatial map, whose values are then categorized into 256 bins. We train with the Adam optimizer ( α = 0 . 0003 ) for 10 epochs with a batch size of 128 and 12 workers. The scale-invariant log loss for prediction A and target B is defined as:

where d i = log( A + ϵ ) -log( B + ϵ ) , ϵ = 0 . 001 to avoid gradient issues, and N is the number of valid pixels.

All training and evaluation for dense prediction tasks is done on a single NVIDIA Titan RTX GPU.

C.2 Retrieval-augmented generation

We follow the evaluation protocol in the codebase provided by [2], constructing a prompt containing three image/text examples, a query image, and a candidate class. We then average the model's output logits, and repeat for each candidate class in a dataset to form a softmaxed array of log probabilities. OpenFlamingo's classification prediction is determined as the class with the highest corresponding log probability, while classifications from IDEFICS2 are taken purely from a multiplechoice text response. Error bars were calculated as 95% confidence intervals over five trials with 1,000 randomly-sampled queries each. All evaluation for RAG tasks is done on a single NVIDIA Titan RTX GPU.

C.2.1 Object counting

Our k-Nearest Neighbor evaluations reported in the Counting section are run over k ∈ { 1 , 3 , 5 , 10 } for the training set, and the argmax k with highest classification accuracy is used to report the final performance on the test set. We use the train and test sets as provided for the FSC147 and CARPK datasets, and construct a random 80:20 data split for the Clevr-Count dataset. All evaluation for this section is done on a single NVIDIA Titan RTX GPU.

C.3 Instance retrieval

We evaluate backbones on instance retrieval by extracting features (CLIP/OpenCLIP: embedding, DINO/DINOv2/SynCLR: CLS token) from the gallery and query sets. For every query image, we compute the cosine similarity with every image in the gallery. We then calculate topk accuracy as the number of queries for which a correct gallery pair image was in the top k retrievals.

C.4 Dataset ablations

We train models for the dataset ablation section with identical hyperparameters as the human-aligned models (details in Section C.1 and on the same amount of data as the NIGHTS training set (13,900 triplets). We randomly select these triplets without replacement from the BAPPS and THINGS training sets, and construct the ImageNet triplets by randomly sampling two images from one class and a third image from a different class.

C.5 VTAB classification

To evaluate models on VTAB, we follow standard procedure and train a linear classifier on top of the frozen representations using multinomial logistic regression, with the sci-kit learn implementation [42]. For each classifier we perform a hyperparameter search over the regularization strength using 10-fold cross validation over the training set, and report performance on the validation set. We search over the values c = { 1 e 0 , 1 e 1 , 1 e 2 , 1 e 3 , 1 e 4 , 1 e 5 , 1 e 6 } where c is the inverse regularization strength.

C.6 Additional compute details

See subsections above for experiment-specific compute details. This full research project required additional compute for experiments and results that are not included in this paper; these computations were also done on single NVIDIA Titan RTX, GeForce 2080, GeForce 3090, and V100 GPUs.

C.7 Licenses for existing assets

The datasets and models we use are released under the following licenses:

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper investigates the benefits of aligning vision model representations with human perceptual judgments to improve their performance across various computer vision tasks. The study fine-tunes state-of-the-art models using a dataset of human similarity judgments and demonstrates that this alignment enhances performance in tasks like semantic segmentation, depth estimation, and instance retrieval. The results suggest that integrating human perceptual knowledge as an inductive bias can improve vision model representations without significantly sacrificing performance on other tasks, including specialized domains like medical imaging and 3D environments.\n\nSTRENGTHS:\n1. The experiment evaluation is comprehensive. The paper evaluates the impact of human perceptual alignment on a wide range of computer vision tasks, including semantic segmentation, depth estimation, instance retrieval, and counting. The paper also use multiple state-of-the-art models (e.g., CLIP, DINO, DINOv2, SynCLR) and a detailed experimental setup.\n\n2. The idea of introduces alignment for vision models with human perceptual judgments is reasonable and innovative.\n\n3. The analysis is insightful. The paper provides a nuanced discussion on the benefits and limitations of human perceptual alignment, offering insights into how different levels of perceptual judgments (low-level, mid-level, high-level) impact model performance on various tasks.\n\nWEAKNESSES:\nWriting Part:\n1. There is a question mark in a citation on line 95.\n2. The order of the references is shuffled; reference pages usually come before the supplementary files.\n3. The paper lacks a conclusion section.\n\nSuggestions for Experiments:\n1. More experiments on advanced vision-language models (VLMs) are expected, such as Llava, MiniGPT-4, and instructBLIP.\n2. While not necessarily a weakness, it would be interesting to see the performance improvement on CLIP-blind image pairs, as introduced in the paper \"Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs.\"\n\nQUESTIONS:\nHow does the patch-level objective differ from the image-level objective? Does it show different performance on various downstream tasks?\n\nLIMITATIONS:\nIntroduced in Section 5.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the effects of aligning pretrained vision models to human judgments. Image-level and patch-level learning objectives are proposed for fine-tuning the pretrained models. The experimental results demonstrate that fine-tuning pretrained models (such as CLIP, DINO, and DINOv2) on the NIGHTS dataset leads to performance improvements in various downstream tasks, including semantic segmentation, depth estimation, retrieval-augmented generation (RAG), counting, and instance retrieval. Ablation experiment regarding the fine-tuning dataset is conducted to demonstrate the perceptual qualities embedded in the NIGHTS dataset.\n\nSTRENGTHS:\n1. The proposed perceptual alignment process is simple yet effective, leading to performance improvements across various downstream tasks.\n2. This paper discussed the effects of aligning vision models using datasets with different levels of variations, providing emprical evidence for further research.\n\nWEAKNESSES:\n1. This paper demonstrates the effects of perceptual alignment by showing the performance improvement in downstream tasks. However, the presented evidence cannot fully explain \"how\" perceptual alignment affects model features as general-purpose representations. Specifically, it is unclear what the difference is between the features before and after perceptual alignment, and why such differences lead to performance improvements.\n\n2. This paper investigates the effects of aligning vision models using dataset with mid-level variations leads to a better “general-purpose representation”. However, the reasons why using datasets with such mid-level variations (instead of datasets with high or low levels of variations) for alignment results in better representations remain unclear.\n3. Experiments in this paper are conducted using ViT based models. However, the effects of perceptual alignment for CNN based models are not explored.\n4. This paper compares the effects of aligning vision models using datasets with low, mid, and high levels of variations on counting and instance retrieval tasks. However, to better demonstrate the high perceptual qualities embedded in the NIGHTS dataset, comparisons should be conducted in a wider range of downstream tasks.\n\nQUESTIONS:\nPlease refer to the weaknesses.\n\nLIMITATIONS:\nThe authors discuss some of the limitations of their work in Section 5. But I would like them to consider some of my concerns above.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aligns representations with human perception on mid-level semantics to improve performance on various downstream tasks. Specifically, it does this by pre-training models on additional synthetic image triplets, where the visual similarity within each triplet is annotated by human subjects.\n\nSTRENGTHS:\n- The dataset this paper used for additional pre-training is rather small compared to the standard datasets, yet authors show that there is still a noticeable improvement on multiple tasks.\n\nWEAKNESSES:\n- Lacks general instructions on when aligning with human perception would benefit representation learning. From the title, I would expect a series of studies providing insights into which levels of alignment benefit or impair different types of tasks. The empirical study in its current version is not comprehensive enough to compensate for the lack of theoretical novelty compared to previous work [1].\n\n    [1] *\"Improving neural network representations using human similarity judgment,\"* Oh et al., NeurIPS 2023\n\n- It would be better if the authors could provide some empirical analysis to give insights into why mid-level alignment is better than other levels.\n\nQUESTIONS:\n- Regarding dense prediction tasks, there are cases where pre-training with human alignment impairs performance, e.g. DINOv2(-HA) on VOC, etc. The authors stated that DINOv2 has seen these data, and considering that DINOv2-HA has also seen these data, how does this explain the performance drop? Since the only difference in the dataset for models with or without HA should be NIGHTS, this needs clarification.\n- It seems odd that the performance change from using ImageNet and THINGS for pre-training is reversed in Figure 8 for the counting task, and pre-training on THINGS looks extremely bad for retrieval tasks. Can the authors provide an explanation for this phenomenon?\n- Compared with previous work [1], the authors use additional patch-level alignment during pre-training. Isn't there some redundancy between the average pooling of patches and the [CLS] token? How does patch-level alignment affect performance? Additionally, since some patches from negative samples and reference images are similar, it seems counterintuitive to push them away in the embedding space.\n\n    [1] *\"Improving neural network representations using human similarity judgment,\"* Oh et al., NeurIPS 2023\n\nLIMITATIONS:\nSee above", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAs the title clearly indicates, this article investigates how aligning vision model representations to human perceptual judgments impacts the performance of models relying on these representations for downstream tasks such as dense prediction (semantic segmentation and depth estimation), retrieval-augmented generation, object counting and instance retrieval. Given a vision model backbone, it is finetuned with LoRA on the NIGHTS dataset, which contains human similarity judgments over synthetically-generated image triplets (NeurIPS 2023). For each considered downstream task, several backbones are compared to their \"human-aligned version\" with globally better results when the backbone is fine-tuned on human perception.\n\nSTRENGTHS:\n* the scientific question raised by the paper is simple but interesting and the study shows it is worth investigation. The problem is clearly stated and introduced, the method and protocol are well explained and the article is well written overall. This bullet point is not just a false pretext to artificially add a \"strength\" to the review, it is a real pleasure to read.\n\n* the method to fine-tune the backbones relies on well-known and standard methods and tools (visual transformer, triplet loss...), which leads to a convincing methodology. The reader is not confused by an incomprehensible labyrinthine system and can therefore fully concentrate on the results of the study.\n\n* the experimental part is impressive, with an evaluation of a large variety of tasks, with various backbones as well. This is obviously a strong part of the paper: although the original idea was simple and interesting, the general quality of the study is finally supported by all these experiments. The supplementary material and the code (zip file) allow a precise investigation of the experiments and ensure reproducibility.\n\n* Section 4.5 investigates the use of alternative datasets to fine-tune the model. Some were proposed by previous works and others were built by the author to test a specific question. All of them have a size similar to NIGHTS. The resulting analysis is interesting and is a nice complement to the study.\n\nWEAKNESSES:\n* the alignment is performed both at the image level (section 3.2) and patch level (section 3.3) and the authors claim (lines 164-166) that \"Additionally, we find that local patch-level representations can be improved by tuning on image-level perceptual judgments, and show performance increases on semantic segmentation and depth estimation\". However, in the following all experiments only compare a backbone with its \"human aligned version\" (HA) and it is not clear what is the contribution of the alignment at a global and a local level. On line 148, the text says that the model consists to \"train heads for semantic segmentation and depth estimation [at the local level]\". Globally, the manuscript could be more explicit on this \"details\": is the local patch level used for training for segmentation and depth estimation only or is it the same training protocol (with global and local levels) for all experiments? If so, what is the contribution of adding (or not) the heads that are trained at a local level?\n\n* one may regret that the negative result on VTAB is not reported in the main paper and only discussed in the Limitation without further investigation. However, one must recognize that the paper already contains significant experimental works, that allow to support the main demonstration.\n\n* the fact that the same backbones are used in several experiments/tasks is interesting but sometimes it leads to models that are very weak baselines in comparison to the state of the art. In particular, in Section 4.3, specialized class-agnostic models on few-shot counting have much better performance than those reported (less than 0.15 MAE/RMSE on FSC147 for SafeCount or CounTR)\n\n**minor** \n* the experimental protocol for \"object counting\" reports that $k$ is determined on the training set among 4 values then the best one is used on the test set. Reporting the performance for each $k$ on the training set, or at least the one that was retained on the test set, would be useful.\n* line 95: a reference is missing\n\nQUESTIONS:\n* is the local patch level used for training for segmentation and depth estimation only or is it the same training protocol (with global and local levels) for all experiments? If so, what is the contribution of adding (or not) the heads that are trained at a local level?\n\n* will the code (zip file) be released with the paper?\n\nLIMITATIONS:\nAn experiment on the VTAB benchmark, whose results are reported in the supplementary material, leads to lower performance with fine-tuned representations. This case is significantly discussed in Section 5, with several hypotheses proposed to explain the phenomenon.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies the question of when perceptual alignment supervision makes better vision representations. They fine-tune existing pre-trained vision backbones (eg, CLIP and DINO) with LoRA on NIGHTS, a synthetic triplet-based dataset of human similarity judgments. Models fine-tuned in this way generally perform better on vision tasks, ranging from dense prediction, RAG, counting, and object retrieval. However, the results can be the opposite when evaluating classification tasks, especially natural classes. They also considered fine-tuning on different types of human preference datasets, and found mid-level supervision to be more beneficial.\n\nSTRENGTHS:\n1. The tasks considered in this paper for evaluation are comprehensive and cover many different properties of the representation.\n2. The delivery of this paper is clear and easy to follow. Key motivation, setting, and conclusions of each part are easy to spot.\n3. Evaluating human preference for vision representations is an emerging topic and interesting for research.\n\nWEAKNESSES:\n1. My major concern is that although the topic is somehow interesting and the evaluation is comprehensive, many conclusions are expected and unsurprising. Provided in the form of triplets, the supervision of human preference is just another form of \"classes\", being very fine-grained and having flexible class definitions. This is similar to the learning signal of self-supervised contrastive learning, with a better form of data augmentation. In this regard, it is expected that human preference makes generally better representations.\n2. Regarding the performance drop in fine-grained classification (especially natural classes), this might be a result of the domain gap. Given that the model is fine-tuned on all synthetic images, it is unsurprising that discriminating natural images is harder afterward. The definition of human preference can be ambiguous, and the dataset for fine-tuning can have distracting factors (eg, synthetic distribution). These factors are not tackled well in the design of comparisons and could make some conclusions unreliable.\n\nQUESTIONS:\nI still find this paper could spark some interesting questions. More investigations on these aspects might increase the significance of this paper.\n1. When can human preference be harmful? One motivation of this paper is to provide some empirical guidance for future vision research, if human preference is introduced as the way in the language community. As I mentioned above, the benefit of it to vision is not very surprising, but the prevention of possible risks could be valuable. This paper has shown some and more in this direction might be more valuable than the benefits (as most improvements are just marginal).\n2. How should human preference be better defined and categorized? A precise definition of human preference is hard to obtain, but important. This paper has considered the level of supervision, and a discussion of other aspects could help readers.\n3. What distracting factors exist in current human preference datasets and how to isolate the effects of them? This is important to ensure reliable conclusions.\n\nLIMITATIONS:\nLimitations are discussed in the paper. Suggestions are flagged above.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}