LocCa: Visual Pretraining with Location-aware Captioners

Authors: bo  wan
michael  tschannen
yongqin  xian
filip  pavetic
ibrahim  alabdulmohsin
xiao  wang
andré susano pinto
andreas  steiner
lucas  beyer
xiaohua  zhai
google  deepmind
  google
  zürich
k u leuven
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-15 12:41:02

Abstract: Image captioning was recently found to be an effective pretraining method similar to contrastive pretraining. This opens up the largely-unexplored potential of using natural language as a flexible and powerful interface for handling diverse pretraining tasks. In this paper, we demonstrate this with a novel visual pretraining paradigm, LocCa, that incorporates location-aware tasks into captioners to teach models to extract rich information from images. Specifically, LocCa employs two tasks, bounding box prediction and location-dependent captioning, conditioned on the image pixel input. Thanks to the multitask capabilities of an encoder-decoder architecture, we show that an image captioner can effortlessly handle multiple tasks during pretraining. LocCa significantly outperforms standard captioners on downstream localization tasks, achieving state-of-the-art results on RefCOCO/+/g, while maintaining comparable performance on holistic tasks. Our work paves the way for further exploration of natural language interfaces in visual pretraining.

Main paper: 
 introduction: 
 Remarkable progress has been made in large-scale visual pretraining [1, 2, 3, 4] , where vision models are pretrained on large-scale annotated datasets [5, 6, 4] with a supervised classification loss. Yet, the manual annotation required for such datasets is time-consuming and costly, posing a challenge to scalability.In light of this, the modern contrastive pretraining methods [7, 8] extract learning signals from web-crawled image-text pairwise datasets [9, 10] , circumventing the need for extensive manual annotations. The contrastively pretrained models demonstrate remarkable capability on zero-shot transfer tasks, especially on downstream applications that require fine-grained visual [11, 12] or textual understanding [13] . More recently, image captioning has been shown as an alternative visual pretraining task to learn capable vision encoders [14] , where an encoder-decoder architecture is pretrained to generate text captions from the image input. Some studies, such as [15, 16] , pioneered the joint pretraining of contrastive and generative methods. Typically, encoded image features are fed into two parallel branches: one employs a text encoder to produce sentence embeddings for contrastive learning, while the other utilizes a text decoder to generate image captions. Despite the effectiveness of these works, they typically focus on a holistic understanding of images, often overlooking the region-specific details of the visual content.The recent success of image captioning [14] and the advancements in multitasking learning of decoders [17, 18, 19] opens up the largely-unexplored potential of using natural language as a Figure 1 : Overview of LocCa. LocCa consists of a standard vision transformer and a transformer decoder. The vision transformer takes image pixel as input, produces visual tokens as cross attention input to the transformer decoder. The transformer decoder is trained to read out rich information from the visual tokens. We adopt the following three task for pretraining: Cap, AREF and GCAP. flexible and powerful interface for handling diverse tasks. We demonstrate this with a novel visual pretraining paradigm, LocCa, that enhances the visual representation with location-aware context. Works including [20, 21, 22] investigate the matching of image regions with corresponding text during pretraining. The central concept involves extracting Region of Interest (RoI) features from image embedding to facilitate contrastive learning with corresponding textual features. These approaches yield encouraging outcomes in location-sensitive tasks, such as object detection [23, 24, 25, 26] and referring expression [27, 28, 29, 30] . However, they require complex model architectures (e.g. RPN [24] and FPN [25] ) for RoI generation. Also, given the presence of multiple object candidates within an image, region-wise matching becomes computationally demanding.By contrast, LocCa is a simple yet effective location-aware captioning pretraining method as shown in Figure 1 , which uses an autoregressive decoder as an interface to handle additional location-aware pretraining tasks. Concretely, other than the image-to-text captioning task, LocCa also pretrains the model with two location-aware tasks: (i) automatic referring expressions, which amounts to predict bounding box coordinates from automatically generated captions for specific image regions, and (ii) grounded captioning to jointly predict box coordinates and captions from the image. Specifically, LocCa leverages a multi-task decoder [17] for pretraining, where the model outputs are conditioned on the task prefixes for each task. Thanks to the shared vision transformer for multiple tasks, the additional localization losses are relatively cheap to compute, while the model inference speed is identical to the standard image caption pretrained models.Our experimental results show that the LocCa performs significantly better on downstream tasks that require localization capabilities, while maintaining the same level of capabilities on holistic tasks. We summarize our contributions as follows: (i) For the first time we explore location-aware tasks as proxies for generative visual pretraining (as opposed to transfer/instruction tuning in prior works), enabling flexibly customized inference (detailed in Sec.3.3); (ii) Without bells and whistles, LocCa achieves state-of-the-art results on localization tasks, while preserving the competitive performance on holistic tasks; and (iii) When integrated in vision-language models, i.e. PaLI-3 [31] , the vision encoder outperforms strong SigLIP baselines [13] . 
related works: 
 Contrastive visual pretraining is a prominent direction in training vision and vision-language foundation models. Early works [32, 33, 34, 35] explore image-only contrastive loss by matching different views of the same image in the self-supervised learning setting. In vision-language model pretraining, CLIP [7] and ALIGN [8] show that a two-tower architecture trained with the contrastive objective on noisy image-text pairs can learn highly transferable image and text representations for various downstream tasks. There have been many follow-up works [10, 36, 13, 37] that further improve the zero-shot image classification and retrieval performance. Notably, [20, 21] propose to incorporate location cues by contrastively aligning image regions and text phrases. In contrast, our work focuses on learning a location-aware vision-language model with a generative loss.A natural alternative to contrastive pretraining is image captioning: Rather than matching image and text embeddings, one tries to predict captions from an image embedding. Early works investigate this approach at small scale [38, 39, 40, 41] . Later works augment large-scale contrastive pretraining with a captioning loss [15, 16, 42] , or scale-up captioning as a stand-alone task without investigating transfer to a broad set of vision tasks [43, 44] . [14] recently showed that image captioning alone leads to visual representations competitive with contrastive pretraining.Many recent large multimodal models are trained with a mixture of tasks [45, 18, 19, 46, 10, 47, 31, 48, 49] . Among the most popular types of tasks are those which can be formulated by mapping an image to a text string, including captioning, detection, and VQA [50, 45, 18, 19, 10, 47, 31, 48] . Another popular group of tasks are dense prediction tasks such as segmentation and depth prediction [19, 46] . While several studies have enhanced model pretraining by incorporating location information, their methodologies primarily leverage either pretrained language decoders [18, 51, 52] or pretrained cross-modal encoder-decoder [51] to integrate vision and language features for multitasking purposes, often neglecting the independent significance of visual pretraining from scratch. Furthermore, there is a trend of towards co-training on images, video, and audio [53, 54, 55] , highlighting the multifaceted nature of current multi-modal research. Crucially, essentially all of these works rely on pretrained vision and language backbones, and merely fine-tune those together on the described tasks. Here, by contrast, we use multi-task pretraining to train visual encoders from scratch. 
location-aware captioner: 
 In this section, we introduce the location-aware image captioner LocCa for multitask pretraining. LocCa builds on an image captioner but provides a recipe for integrating location-aware information during model pretraining.The pretraining phase of LocCa draws inspiration from pioneering works that have successfully integrated a unified decoder for multitasking based on pretrained models [18, 10, 19, 46, 52] , utilizing a task-specific prefix for each distinct task. This enhances the model's ability to handle multiple tasks concurrently.For conventional image captioning, the process involves taking an image x as input and generating a sequence of text tokens y = [y 1 , y 2 , . . . , y n ]. In the LocCa framework, a task-specific prefix, labeled as "Cap:", is added to the beginning of the caption sequence to designate the task at hand. Moreover, LocCa integrates two additional location-aware tasks during its pretraining phase: automatic referring expression (AREF) and grounded captioning (GCAP). These tasks are inspired by referring expression comprehension [27, 28, 29, 30] and dense captioning [56, 57, 58, 59, 60] respectively. The key difference is that LocCa predicts both regional captions and box coordinates sequentially with task prefixes, instead of relying on either caption or box conditional inputs (see Fig. 1 ).The foundation of LocCa's pretraining is built upon dense, automatically generated region annotations. Each image x is associated with a comprehensive set of annotations {(b, c)}, where b ∈ N 4 denotes the bounding box coordinates, and c represents the corresponding textual descriptions or labels. For every bounding box, two distinct prompts are generated to cater to the aforementioned location-aware tasks: "ARef : {c} : {b}" for automatic referring expression and "GCap: {b} : {c}" for grounded captioning, each prefixed with "ARef :" and "GCap:" respectively. These prompts are then tokenized to produce the sequence y for each task, facilitating pretraining with a text interface.For each image, LocCa utilizes the same visual features extracted by the image encoder and performs three tasks using the same decoder in parallel. This pretraining scheme aims to make LocCa adept at linking fine-grained regional visual elements with appropriate textual descriptions.Architecture LocCa utilizes a conventional encoder-decoder framework, where the encoder comprises a Vision Transformer [2] to transform the input image x into a sequence of feature embeddings. The decoder, built on a Transformer architecture [61] , processes these image features, employing cross-attention across each layer to integrate visual and textual information effectively.Autogressive decoding In the decoding stage, LocCa uses causal attention masks [61] to guide the prediction of each token in the sequence, ensuring that each token is generated based on the ones before it, in a step-by-step manner. This setup helps in creating coherent and context-aware captions, drawing from the visual cues encoded earlier and maintaining a logical flow in the generated text.Parallel prediction Inspired by [14] , LocCa also adopts parallel prediction for a fraction of the training examples (concretely 50%) in the standard image captioning task. This technique requires the model to predict the caption tokens independently in parallel, focusing exclusively on visual information to prevent the model from relying on preceding text tokens to predict the following ones. This strategy was shown to improve the learned representation on a range of tasks [14] .Objective The optimization of LocCa's parameters θ is achieved through the maximization of the log-likelihood: |y| i=1 log P θ (y i |y <i , x). It is important to emphasize that, in the learning process for the location-aware tasks, LocCa is structured to predict captions and bounding boxes sequentially, contrasting with traditional approaches that might predict a caption based on a given bounding box or vice versa [18, 51] . This is achieved by applying losses to the entire prompt excluding the task prefix. Taking the AREF task as an example, the overall loss is computed for both textual predictions c and box coordinates b. The loss on c guides the model to identify a foreground region and generate its caption, while the loss on b aims at refining the model's ability to accurately regress the box location relative to the caption.To the best of our knowledge, LocCa is the first end-to-end method that incorporates the locationaware tasks (i.e., AREF and GCAP) into generative VLM pretraining. Our key novelty lies in the formulation of the location-aware proxy tasks which allows for scalable pretraining and enhances the visual localization capabilities. Compared to [22, 18, 51, 52] that require either a pretrained visual encoder or language decoder, LocCa does not need any pretrained model for initialization. Compared to [22, 62] that employ complex architectures with multiple losses, LocCa adopts a simple encoder-decoder architecture with a single generative loss. Compared to [18, 19] that are pretrained on a large number of tasks, LocCa introduces the novel use of simple AREF and GCAP proxy tasks for visual pretraining.The dual-faceted loss structure of AREF and GCAP achieves comparable results to directly adopting the traditional referring expression and dense captioning tasks. However, it enhances inference flexibility of LocCa, allowing for varied input configurations. For instance, users can input a single task prefix (e.g., "ARef :") to prompt the model to identify and describe an area of interest along with its location. Alternatively, by inputting both the task and a conditional input (e.g., "ARef : a black and white cat :"), LocCa can be directed to focus solely on predicting the location. This flexibility allows for customized responses to various inquiries, highlighting the model's adaptability to meet specific user needs.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? Answer: [NA]Answer: [No] Justification: LocCa is pretrained on a subset of the WebLI dataset, which consists of image-text pairs and is not publicly accessible. Nonetheless, we provide details on how to prepare a pretraining dataset, applicable to any public image-text datasets such as LAION [9] . The code will be released soon. Guidelines:• The answer NA means that paper does not include experiments requiring code. • The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
experimental setup: 
 Pretraining dataset We use a subset of the WebLI dataset [10] corresponding to English websites and apply text-based filtering [8] to obtain 1B image/alt-text pairs. The WebLI data was de-duplicated w.r.t. all the images in the evaluation data sets used in this paper. To obtain fine-grained object locations, a publicly available OWL-ViT CLIP L/14 model [63] is applied to generate detection pseudo annotations. Specifically, two groups of box categories are generated: the n-gram texts from alt-text and the object categories as used by PaLI [10] , more details can be found in [64] . In LocCa pretraining, we first filter the candidate bounding boxes according to their OWL-ViT confidence score with a threshold of 0.3, and then randomly sample one box-caption or box-category pair for referring expression and grounded captioning separately.Baselines Our main baselines are CLIP-style contrastively pretrained dual-encoder models [7, 8] on our dataset (referred to as CLIP* to differentiate from the model released by [7] ), as well as the captioning-pretrained encoder-decoder models from [14] . For both types of models we follow the exact training recipe from [14] . For the captioning-based pretraining we consider standard autoregressive captioning (Cap) as well as the variant with parallel prediction (CapPa). This variant removes the decoder attention mask for a fraction of the training examples and replaces the decoder input, which corresponds to the right-shifted output sequence during autoregressive training, with a sequence of all mask tokens. Parallel prediction [14] improves the representation learning capabilities captioning-based pretraining at no extra computation cost.Implementation details LocCa adopts an encoder-decoder structure where, unless otherwise noted, the encoder defaults to a standard ViT-L/14 design with 24 transformer blocks handling input image patches of size 14. The decoder, following [14] , is a Transformer-L model consisting of 12 transformer decoder blocks. In total, the model comprises approximately 600M parameters. Two more LocCa setups with ViT-B/16 and ViT-G/14 (see [4] for model specifications) are adopted for ablation tests and scaling experiments respectively.Pretraining details LocCa is pretrained for about 9 billion image/alt-text seen examples, which corresponds to about 9 epochs on our tailored subset of WebLI. For the optimizer, we employ the Scaling-ViT AdaFactor variant [4] , combined with a cosine schedule that includes 10,000 warmup steps. The batch size is set at 8,192, while the learning rate and decay factor are adjusted to 10 -3 and 10 -4 , respectively. During this process, images are uniformly resized to a resolution of 224 x 224 pixels. Alt-texts are tokenized into a vocabulary consisting of 32,000 tokens using a sentence piece model trained on the English segment of C4 [65] , with a cap on the sequence length at 64 tokens. We represent bounding box coordinates using up to 500 integral numbers, which are then directly converted into strings for straightforward representation of coordinate tokens. For parallel prediction in the vanilla image captioning task, the fraction of examples is set to 50% by default. The pretraining of LocCa L takes 153 hours using 256 TPUv3 chips.Our goal with LocCa is to preserve or even improve the capability on various image-level understanding tasks, and get a higher performance on fine-grained location-aware tasks. To this end, we assess the capabilities of LocCa in holistic and location-aware image understanding tasks across a range of downstream tasks. In the realm of holistic image understanding, we focus on the same LiT-Decoder tasks [17] in CapPa [14] including image classification (CLS) [5, 66, 67, 68, 69] , image captioning (CAP) [70, 71] , optical character recognition (OCR-VQA) [72] , visual question answering (VQA) [73] , and graph question answering (GQA) [74] . For evaluating location-aware image understanding, we choose two widely recognized tasks like referring expression comprehension (REC) [27] , referring expression segmentation (RES) [75] and object detection (OD) [24] . Additionally, paralleling the approaches of PaLI [10, 31] , we integrate LocCa to a pretrained large language model to assess performance on a variety of vision-language tasks, including image captioning and visual question answering. Notably, despite LocCa not being exposed to any video content during pretraining, we adapt it to various video-related tasks, such as video captioning, QA, and classification. This adaptation aims to assess its generalization capabilities to new modalities, demonstrating its versatility compared to other image-text pretraining approaches. We adopt different strategies for transferring LocCa to downstream tasks, as summarized in Appendix A.1.We conduct extensive experiments to evaluate LocCa. The integration of location-aware cues enables LocCa to maintain its performance on holistic image understanding tasks while achieving substantially improved outcomes on location-aware tasks. Further enhanced by an advanced pretrained large language model, LocCa exhibits exceptional performance across a range of vision-language tasks, substantially surpassing baseline models.In this section, we present results on the referring expression comprehension benchmarks, including RefCOCO, RefCOCO+ and RefCOCOg [28] . As shown in Table 1 , LocCa establishes a new state-of-the-art across these benchmarks. This advancement is particularly noteworthy considering the inherent limitations of previous methods. For example, UNINEXT [77] , which adopts a Deformable DETR architecture [84] tailored for detection-based tasks, and OFA, which requires a pretrained BART [85] for initialization, both achieve good results but are constrained by their specialized setups. Besides, some location-aware VLMs, such as Shikra [78] and Ferret [79] , require an LLM for knowledge-based reasoning, which increases inference costs. In contrast, LocCa employs a standard encoder-decoder architecture with auto-regressive pretraining from scratch, significantly outperforming these methods across all benchmarks without the need for complex task-specific adaptations.Achieving good performance on RefCOCO usually requires pretraining at high image resolutions. For instance, OFA L is pretrained at 480 2 px, while UNI-NEXT undergoes multi-scale pre-training. We opt for a standard 224 2 px pretrain resolution for simplicity. We transfer the 224 2 px pretrained model to RefCOCO by fine-tuning with 640 2 px resolution, using learning rate 10 -4 and no weight decay. We report the standard metric Acc@0.5 on the validation and test sets.Notably, we train on the combined training sets of RefCOCO, RefCOCO+ and RefCOCOg but removing all validation and test images from this combination. The splits of these three datasets are largely overlapping, meaning that methods trained on the combined training sets without deduplication, a recently common phenomenon, have trained on about half of the test images, see Appendix A.3 for details. We provide the list of removed image IDs in the Appendix A.3. Furthermore, some models such as UNITER [80] , VILLA [81] , and MDETR [82] use COCO pre-trained detection components which have seen test images from the three RefCOCO versions. We have removed all training, validation, and test images from the COCO dataset from our pre-training data, as well as near-duplicates thereof. Hence, we group models reported in Table 1 into three distinct categories. We transfer LocCa on both the full and the "clean" combined training set and provide both results. We hope that, going forward, the community evaluates models on RefCOCO in this clean setup.Moreover, as shown in Table 2 , LocCa improved significantly over all the baselines of image-text pretrained models. To ensure a fair comparison with contrastive baselines (i.e. CLIP) which lack a text decoder for box prediction, we employ the LiT-Decoder [17] setup for comparison on RefCOCO benchmarks. It involves freezing the pretrained image encoder while training a text decoder from scratch with a small resolution of 224 2 px. This setup is necessary to properly compare with CLIPstyle models without a decoder. The performance improvements attributable to location-aware pretraining are evident, demonstrating the enhanced sensitivity of visual encoder to object regions, which is crucial for excelling in referring expression tasks. Referring Expression Segmentation For referring expression segmentation, we extend the REC task by adding a suffix "Mask:" to the text, followed by the indexes of segmentation tokens. The segmentation tokens specify the precise shape of the segmentation mask within the bounding box that is identified during the REC task. This process leverages a pretrained VQ-VAE [86] to convert the semantic masks to tokens, please refer to Appendix A.3 for more details.LocCa is adapted to RES under the same settings as in REC, using the "clean" combined training sets of RefCOCO, RefCOCO+, and RefCOCOg (c.f. Appendix C). Specifically, we compare different frozen encoders and train a decoder from scratch. As shown in Table 2 , LocCa's vision encoder outperforms other encoders substantially, thereby providing further validation of its location-wise sensitivity. Moreover, we employ the full encoder-decoder LocCa model and fine-tune it for RES. Notably, LocCa achieves competitive results even compared to the state-of-the-art PaLI-3 model, albeit with considerably fewer parameters (0.6B vs. 5B). See Appendix A.3 for details.Holistic Image Understanding While being great on the referring expression comprehension tasks, we also verified that LocCa performs equally well on the holistic image understanding tasks. Interestingly, we found that LocCa outperforms the image-text pretrained baselines on the objectcentric tasks like VQAv2 and GQA.Following the similar evaluation protocol in [14] , we evaluate the capability of LocCa with the "LiT decoder" [17] setup, to investigate the adaptation capability of the learned representations to interface with a text decoder. Here we report the classification accuracy on 5 classification (CLS) datasets (ImageNet-1k [5] , Sun-397 [67] , Food-101 [66] , Resisc-45 [68] , Oxford-Pet [69] ), and also answer accuracy on VQAv2 and GQA and OCR-VQA datasets. Besides, we report the CIDEr score on 2 captioning (CAP) datasets (COCO [70] and Flickr [71] ).As shown in Table 3 , the performance of LocCa is better than image-text pretrained baselines on image captioning, VQA and GQA, comparable on image classification, and slightly lags on OCR-Figure 2 : Result on COCO detection with a limit of 25 output boxes. For reward tuned models we show both the results before (dark blue and orange) and after (light blue and orange) reinforce tuning [88] . VQA. Notably, both VQA and GQA necessitate fine-grained instance-level comprehension, focusing on the spatial and semantic relationships between objects to accurately provide the correct answer. For example, GQA involves complicated information about objects, attributes and relations provided by scene graphs [57] . Such an observation further reveals the advantage of LocCa on fine-grained object-level sensitivity.It is also interesting to investigate the full potential of the LocCa model by fine-tuning it on holistic tasks with a small 3 × 10 -6 learning rate without weight decay. To this end, we choose the widely used COCO image captioning task as an example. LocCa achieves competitive results of 138.0 CIDEr score with a standard 224 2 px. When increasing the transfer resolution to 640 2 px, the LocCa L model achieves 140.3 CIDEr score without CIDEr metric optimization [87] .Vision-Language Models with LocCa In this section, we present results on vision-language tasks with LocCa plugged into a pretrained large language model. More specifically, we use PaLI-3 [31] here to test the vision encoder quality. Importantly, we discovered that the generative LocCa vision backbone outperforms the SigLIP backbone, which is the default setup used in PaLI-3.We select a wide range of tasks to assess the models' proficiency in understanding various visual concepts, including: image scene (COCO Caption), objects (VQAv2 and TallyQA [89] ), visuallysituated text (TextVQA [90] , ST-VQA [91] ) and general knowledge (OKVQA [92] ). Remarkably, as shown in To evaluate for object detection, we use COCO detection dataset and follow [88] which models the task with an encoder-decoder model that outputs sequences of bounding boxes similar to pix2seq [50] . The model is trained in two phases, first it maximizes the log-likelihood of generating the ground truth sequences of boxes, secondly it uses reinforce to tune the model for a reward related to the mAP metric.Both [88, 50] pretrain their encoder-decoder model in Objects365 [93] and we found this to be critical.In particular the task output format is different from the ones LocCa used during pretraining and the size of COCO is too small to learn it without overfitting. To measure the performance of the encoders, we opt to initialize the decoder from an Objects365 pretrained model. More details of adapting LocCa for object detection please refer to Appendix A.5.We present the comparative results in Figure 2 , where LocCa significantly outperforms all imagetext pretrained baselines in both mAP and AR, at both stages -before and after the application of reinforcement tuning. This performance aligns with our expectations regarding LocCa's object-centric comprehension, attributed to the integration of additional location-aware pretraining.Zero-shot Transfer Following locked-image tuning [36] , we freeze the pre-trained vision encoder and train a text encoder contrastively to perform zero-shot classification and zero-shot retrieval on downstream tasks. Semantic Segmentation We investigate the dense feature learning capabilities of LocCa by evaluating it for semantic segmentation on ADE20k [95] along with Cap and CapPa. To this end, we use the Segmenter framework [94] which attaches a linear layer to every patch embedding to predict the semantic label of that patch, and obtains the high-resolution semantic map by upsampling the low-resolution map. The results in Table 5 show that LocCa outperforms Cap and CapPa by about 2 mIoU points, and the Seg ViT-L baseline which is based on an ImageNet-21k-pretrained ViT-L by about 1 point. The transfer details are provided in Appendix A.7.Video Understanding We follow PaLI-3 [31] to evaluate LocCa and CapPa on video understanding tasks. Specifically, we use the image encoder to process each frame separately and concatenate all resulting tokens. A LiT-Decoder [17] is then tuned on a mixture of six video understanding tasks. Despite no video has been seen during pretraining, LocCa showcases the video understanding capabilities and outperforms CapPa on most of the datasets. In particular, for the video captioning task on VATEX [96] dataset, LocCa achieves a CIDER score of 65.0 vs 64.0 of CapPa. For the video QA task on MSVD [97] dataset, LocCa obtains an accuracy of 50.9 vs 50.0 of CapPa. More details could be found from Appendix A.8.In this section, we discuss the raw LocCa model's zero-shot capability to detect multiple objects using its own decoder, despite only a single object per example being observed during pretraining. To achieve this, we employ beam search when decoding the output from LocCa. This involves using the prompt of a single task prefix "GCap:" to instruct LocCa to predict the RoIs along with their labels. As shown in Appendix Fig. 6 , we observe that depending on the setting we get lower number of bounding boxes with correct class names, or higher number of boxes with class names which start to contain noise. We provide more discussions in Appendix B. Nevertheless, pre-trained LocCa model shows powerful capability after a short finetuning on a clean downstream dataset as shown in Table 1 .Finding a decoding strategy which can output high number of boxes and quality labels at the same time is an open problem. Pretraining and transfer resolutions We present results with different pretrain resolution and transfer resolution combinations in Figure 3 (a). To get a LocCa model with higher 384 pretrain resolution, we finetune the 224 resolution model using the target 384 resolution for 900M examples seen on the same dataset. In Figure 3 (a), we transfer both the 224 resolution model and the 384 resolution model to RefCOCOs using different transfer resolutions (224, 384, 640) as marked on the x-axis. We find that the higher pretrain model achieves slightly higher or competitive results compared to the 224 resolution models. We opt for the 224 resolution pretrain models by default in this paper for simplicity. More results with high pretrain resolution could be found in the Appendix A.10.Previous studies [18, 51] often tokenize object coordinates by adding a location vocabulary. In contrast, LocCa simplifies this process by directly converting integral coordinates into textual strings, which are then tokenized with the same text tokenizer. This section presents an ablation study on the RefCOCO benchmarks to examine the differences between these two options. As shown in Figure 3 (b), both tokenization strategies for box coordinates yield remarkably similar results. However, our approach is notably simpler and more straightforward.In this study, we introduce three methodologies to evaluate the performance of the pretrained LocCa across a variety of downstream tasks. The first methodology focuses on assessing the efficacy and generalization capability of the standalone pretrained visual encoder. In this setup, the visual encoder is kept frozen, and we employ two main strategies: (i) Drawing inspiration from Lit-Decoder [17] , we train a multi-task decoder from scratch for all downstream tasks. This approach is particularly suited for holistic image understanding and video-related tasks due to the broad range of tasks, allowing for a simplified evaluation process with a single decoder; and (ii) Aligning with the recent advancements [10, 47, 31, 52 ] that showcase the exceptional results achieved by integrating a pretrained visual encoder with a large language model [100] across a multitude of downstream tasks, we adopt the training strategy outlined in PaLI-3 [31] , substituting their visual encoder with our LocCa pretrained encoder.The second methodology entails assessing the performance of the full LocCa model by fine-tuning it on downstream tasks using a minimal learning rate and weight decay. Given the high cost associated with fine-tuning the entire model, we selectively evaluate its adaptability on just two tasks: image captioning for holistic image understanding and referring expression comprehension for locationaware understanding.The third methodology involves combining the pretrained LocCa visual encoder with a task-specific pretrained decoder for selected tasks, such as object detection. This approach is designed to leverage the strengths of LocCa's visual encoder in particular scenarios, notably in contexts requiring finegrained object-centric understanding.For the evaluation of different frozen vision backbones on classification, captioning, and VQA with the protocol from [17] (LiT-Decoder) we rely on the hyper-parameters from [14] (which only differ from [17] in the data mixing strategy).We also apply this protocol to Referring Expression Comprehension to compare the encoders only (without pretrained decoders), with small modifications. Specifically, besides training on a data mix of the different RefCOCO variants, we reduce the number of decoder layers from 12 to 6, and reduce the learning rate from 10 -3 to 3 × 10 -4 . Also note that transfer is done at the pretraining resolution of 224 2 px, unlike the fine-tuning transfers which are done at higher resolution.For referring expression segmentation, we extend the REC task by adding a suffix "Mask:" to the text, followed by 16 integer numbers corresponding to segmentation tokens. The segmentation tokens specify the precise shape of the segmentation task within the bounding box that is predicted as part of the REC task. We use the vector-quantized variational auto-encoder (VQ-VAE) from [86] , which was trained as in [31] on OpenImages data [101] . The VQ-VAE converts a single channel 64 × 64 pixel mask into a sequence of 16 integer values from a discrete codebook of 128 tokens. LocCa is trained to predict the 16 segmentation tokens, and the VQ-VAE decoder is then used to convert these tokens to a 64 × 64 pixels segmentation mask. This mask is then resized to the predicted box coordinates (with bilinear interpolation), before computing the IoU with the ground truth mask.In Table 7 , we showcase the results of employing the full encoder-decoder LocCa model and finetuning it for RES task. It achieves competitive results even compared to the state-of-the-art PaLI-3 model, with considerably smaller model size.We follow the setup of PaLI-3 [31] ablations for all PaLI-3 transfer evaluations. More specifically, we pretrain PaLI-3 stage 1 (frozen image encoder, 224 2 px resolution) with batch 16k for 13k steps (i.e. 208 million examples). Then we fine-tune PaLI-3 model on each downstream task separately, keeping image encoder frozen and the same 224 2 resolution. The details of each task is listed in Table 8 .To validate the effectiveness of LocCa compared with image-text pretrained baselines on object detection, we employ a ViT-B/16 encoder and pretrain all these models on our WebLI subset for 9B examples with 224 2 px. Subsequently, we integrate the pretrained visual encoder with a 6-layer Objects365 pretrained decoder, adapting the combined model for the COCO detection task. This adaptation processes inputs at a 640 2 px and is designed to predict up to 25 bounding boxes, using a batch size of 256. First we fine-tune the model for 10k steps with learning rate of 10 -4 , employing a standard auto-regressive loss. This is followed by an additional fine-tuning phase focused on maximizing the mAP reward for 20k steps with learning rate of 10 -6 .Following locked-image tuning [36] , we freeze the pre-trained vision encoder and train a text encoder contrastively to perform zero-shot classification and zero-shot retrieval on downstream tasks. We take the L/14 vision encoder from a LocCa L model, and then train a text encoder from scratch to pair with the frozen L/14 model. Input image is resized to resolution 224 2 px. The model is trained for 3B seen examples, with a standard 10 -3 learning rate and 10 -4 weight decay. Beyond that, we also attach a randomly initiated MAP head [4] to the L/14 vision encoder and finetune the MAP head following the CapPa baseline [14] .We follow the setup of Segmenter [94] which fine-tunes the vision encoder jointly with a linear head predicting the semantic label for every patch, followed by upsampling. The input image resolution is set to 512 × 512, and we apply random resizing with aspect ratio in the range [0.5, 2.0], photometric jitter, and random horizontal flipping. We train for 160k steps with batch 16 using Adam with learning rate 3 × 10 -5 and decoupled weight decay of 0.01. To accommodate variable inference resolution for evaluation, we apply our model in sliding-window fashion at the training resolution.Even though LocCa has never seen any videos during pretraining, its training recipe results in an improvement upon CapPa in many video-related tasks, including captioning, QA, and classification.Here, we follow the setup used in PaLI-3 [31] , in which we use the image encoder to process each VLOGS [106] , a multi-label classification containing 23 classes. Of the six datasets, VLOGS is location-related, since the task involves tracking the movement of a hand. Indeed, while LocCA leads to favorable improvements overall over CapPa, especially using ViT-L/16 encoder, the gain is particularly notable in VLOGS as expected. Table 9 summarizes all the results.For the grounded captioning task, we follow the standard setup that generates regional captions based on a given bounding box location [58, 108, 109] . We report the grounded captioning results on the Visual Genome dataset and compare them with state-of-the-art methods.As shown in Table 10 , LocCa (0.6B, without LLM), outperforms GPT4RoI [108] (7B, with LLM) on both METEOR and CIDEr scores. METEOR evaluates the precision, recall, and alignment of words between the generated and reference captions, while CIDEr assesses the similarity of n-grams between them. When compared with GLaMM [109] (7B, with LLM), LocCa performs better on METEOR but lags on CIDEr. This difference can be attributed to the relatively simple captions in Visual Genome, which typically consist of only a few words. LocCa uses a simpler decoder (0.3B params only) that closely matches the reference captions in terms of word choice and order, which aligns well with the dataset's simple nature. GLaMM, on the other hand, uses a more complex LLM as its decoder, which likely generates more diverse captions that include n-grams that match the reference captions more effectively.It is important to note that the pretrained LocCa encoder complements multimodal LLMs (i.e. as a better alternative option to the CLIP encoder), and we anticipate further performance gains on downstream tasks when combining both. A.9 Experiments on grounded captioning A.10 Impact of high resolution pretraining and tokenization strategiesIn this section, we provides the complete results of the ablation studies on different image resolution pretraining in Fig. 4 , and different tokenization strategies for box coordinates in Fig. 5 , focusing on the RefCOCO benchmarks. We provide visualizations of LocCa L 's zero-shot predictions on Visual Genome [57] in Fig. 6 . We have two notable observations: (i) When comparing the top rows to the bottom rows, LocCa excels at identifying foreground regions, yet the box regions exhibit significant overlap without non-maximum suppression. This is because only one single object per example is observed during LocCa pretraining, leading to the selection of RoIs that are highly random. (ii) From left to right we increase noise during sampling, which increases the variety and quantity of decoded boxes, while simultaneously degrades the quality of captions. We hypothesize that box sampling demands higher noise levels to explore to explore more diverse RoIs, whereas text sampling requires lower noise levels to ensure the generated texts are highly semantic relevant.Recent studies in referring expression comprehension [52, 76, 77, 18] typically train their models using the combined training sets of RefCOCO, RefCOCO+, and RefCOCOg. However, the splits of these three datasets largely overlap, implying that methods trained on more than half of the test images. In Table 11 , we present the image ratio of validation and test splits of RefCOCOs that overlap with the combined training sets. Adhering to the general principle, we provide a list of image IDs in the training set of RefCOCO benchmarks that are duplicated with validation and test images. 11282, 11324, 11774, 12224, 12377, 12382, 12440, 12495, 12614, 12790, 13352, 13576, 13670, 13763, 13856, 14008, 14025, 14160, 14283, 14468, 14484, 14502, 14864, 15190, 15195, 15554, 15809, 16089, 16273, 16465, 16616, 16659, 16669, 16725, 16796, 16814, 16836, 16870, 17236, 17468, 17566, 17587, 17938, 17997, 18075, 18089, 18093, 18211, 18244, 18370, 18542, 18780, 19123, 19374, 19501, 19789, 19959, 19967, 20044, 20156, 20188, 20279, 20513, 20619, 20917, 21292, 21504, 21750, 21830, 22102, 22195, 22222, 22287, 22575, 22740, 22928, 23014, 23141, 23194, 23420, 23538, 23967, 24038, 24086, 24129, 24404, 24689, 24706, 24762, 24842, 24939, 25237, 25353, 25414, 25515, 25628, 26052, 26421, 26438, 26583, 26997, 27070, 27149, 27424, 27495, 27750, 27763, 28012, 28038, 28069, 28154, 28281, 28451, 28560, 28824, 28974, 28988, 29304, 29456, 29473, 29601, 29752, 29799, 30203, 30274, 30340, 30418, 30631, 30973, 31112, 31187, 31230, 31329, 31374, 31382, 31418, 31838, 32061, 32105, 32289, 33017, 33527, 33572, 33991, 33992, 34223, 34616, 34739, 34810, 35045, 35132, 35230, 35265, 35322, 35473, 35558, 35571, 35796, 36017, 36445, 36488, 36546, 36755, 36981, 37089, 37282, 37429, 37539, 37582, 37698, 37719, 37800, 37847, 37862, 38033, 38266, 38552, 38890, 39159, 39185, 39258, 39629, 39802, 39812, 40130, 40346, 40735, 41713, 41730, 41818, 41988, 42696, 42804, 43163, 43609, 43655, 43664, 43813, 43892, 44123, 44266, 44298, 44637, 44901, 44960, 45226, 45464, 45659, 45672, 45840, 46055, 46385, 46519, 46609, 46809, 47093, 47175, 47198, 47294, 47347, 47357, 47391, 47451, 47554, 47774, 47928, 48267, 48432, 48572, 48665, 48707, 48937, 49022, 49073, 49866 Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.• The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
experiment statistical significance: 
 Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments? Answer: [Yes] Justification: For most experiments we ran 3 seeds and report the mean but the results show marginal variability. For video understanding, we present the mean and standard deviation in Table 9 .Guidelines:• The answer NA means that the paper does not include experiments.• The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper. • The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).• Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
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 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [No] Justification: The main focus of this work is on visual representation learning, rather than vision/language generation. Furthermore, we do not plan to release model checkpoints or data.Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort. 12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: We rely on widely used academic data sets and referenced all of them according to established standards. Further, we carefully cited the source of our baseline CLIP and compared with it in Sec. 4.1. Baselines.• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.• If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. • For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. • If this information is not available online, the authors are encouraged to reach out to the asset's creators. 
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 Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?Answer: [NA]Justification: [NA] Guidelines:• The answer NA means that the paper does not release new assets.• Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc. • The paper should discuss whether and how consent was obtained from people whose asset is used. • At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file. 
crowdsourcing and research with human subjects: 
 Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?Answer: [NA] Justification: We do not rely on crowdsourcing or research with human subjects.Guidelines:• The answer NA means that the paper does not involve crowdsourcing nor research with human subjects. • Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper. • According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector. 
institutional review board (irb) approvals or equivalent for research with human subjects: 
 Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?Answer: [NA]Justification: [NA] Guidelines:• The answer NA means that the paper does not involve crowdsourcing nor research with human subjects. • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.• We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. • For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.Selection of pretraining tasks To evaluate the importance of the selected tasks for pretraining LocCa, an ablation study was conducted focusing on the effects of AREF and GCAP tasks. Specifically, the study explored the impact on LocCa by removing these tasks individually and collectively, with the model defaulting to the CapPa baseline when both are excluded. All these models are pretrained on the WebLI split for 900M examples with the resolution of 224 2 px, and subsequently evaluated on the holistic tasks using a LiT-Decoder setup and on RefCOCOs by fine-tuning the whole model. As shown in Table6, incorporating any location-aware task into the pretraining of LocCa yields significant performance improvements, particularly evident in the RefCOCO results. Interestingly, incorporating solely the GCAP task leads to a marked improvement in RefCOCO performance compared to the CapPa baseline, despite GCAP not being directly aligned with the AREF task. This highlights the importance of introducing object concepts for enhancing regional-level understanding, which is beneficial for fine-grained visual comprehension and applicable to other object-sensitive tasks. Furthermore, the combined inclusion of both AREF and GCAP tasks yields even better results, demonstrating their complementary nature in improving LocCa's performance.5 ConclusionLocCa introduces a novel visual pretraining paradigm, using natural language interface to construct the proxy location-aware pretrain tasks. This model excels in understanding both the overall scene and specific spatial details, setting new performance standards on location-aware tasks while preserving the capability on holistic image understanding. LocCa simplifies the process of blending location information with visual data, offering significant improvements on tasks requiring detailed spatial awareness. Our contributions pave the way for advanced model capabilities in processing a broad spectrum of vision-language tasks, demonstrating LocCa's versatility and effectiveness.LocCa already demonstrates superior zero-shot detection capability in qualitative results. However, it lacks the capability for zero-shot segmentation due to the absence of pretraining on pixel-level annotations, and we leave this for future work. Building on the robust foundation of LocCa, a promising direction for future work involves enhancing its pixel-level precision through the incorporation of segmentation tasks during the pretraining phase. This extension aims to equip LocCa with a more nuanced understanding of images, enabling it to discern and interpret intricate details and textures with unparalleled accuracy, further broadening its applicability across diverse vision-language tasks. 
 
 Appendix:  


Decision: This paper presents a novel pretraining framework for vision foundation models. To enhance  location awareness of a model, the proposed method incorporates two location-related tasks, bounding box prediction and location-dependent captioning, in addition to the standard holistic captioning.   
The reviewers appreciated the strong performance of the method on downstream tasks, extensive experiments, and good writing and presentation of the paper. Meanwhile, they also raised some concerns. Some major ones include; comparison and positioning of the proposed method with respect to other location-aware MLLMs, lack of evaluation on some downstream tasks, insufficient ablation studies, lack of evidence that the prosed method is more efficient than previous location-based pretraining  methods, etc.   
During the discussion phase, the authors addressed the above concerns carefully with many new experimental results as evidence. The reviewers have been generally satisfied with the authors rebuttal, and two of them decided to upgrade the scores, resulting in unanimous recommendation for acceptance of the paper. The AC agrees that the reviewers’ concerns have been properly addressed and cleared, and concludes that the paper is above the bar for acceptance. The AC does encourage the authors to take in the new results, analysis and discussion in the camera ready version as promised.

Reviews: review: paper_summary: paper_summary: This paper presents LocCa, a novel visual pretraining paradigm that incorporates location-aware tasks into captioners. Specifically, LocCa employs two tasks, bounding box prediction and location-dependent captioning, conditioned on the image pixel input. This multi-task training helps LocCa significantly outperforms standard captioners on downstream localization tasks while maintains comparable performance on holistic tasks.
strength_weakness: strength_weakness: Several works also investigate he matching of image regions with corresponding text during pretraining. The authors claim that compared to these methods, the proposed LocCa can git rid of complex model architectures and become more computationally efficient. To demonstrate this, more experimental results should be provided to validate this claim, for example, FLOPs, trainable paramters or training time.
questions: questions: Several works also investigate he matching of image regions with corresponding text during pretraining. The authors claim that compared to these methods, the proposed LocCa can git rid of complex model architectures and become more computationally efficient. To demonstrate this, more experimental results should be provided to validate this claim, for example, FLOPs, trainable paramters or training time.
limitations: limitations: Several works also investigate he matching of image regions with corresponding text during pretraining. The authors claim that compared to these methods, the proposed LocCa can git rid of complex model architectures and become more computationally efficient. To demonstrate this, more experimental results should be provided to validate this claim, for example, FLOPs, trainable paramters or training time.
paper_summary: paper_summary: This work proposed a location-aware pre-training for vision-language learning. The pre-training contains two tasks: one has location input and output caption/text; the other one has text input and output location of the corresponding object. The model is trained from scratch in ~1B large-scale image-text pairs with object location extracted by an off-the-shelf detection model. The experiments demonstrate the strong performance of the proposed model and the generalizability of the pre-trained vision encoder.
strength_weakness: strength_weakness: 1. The key novelty is claimed to be pre-training a vision-language model via two location-aware tasks (Line139): `automatic referring expression` and `grounded captioning`. However, similar location-input-text-output and text-input-location-output tasks have been widely used in the training (mostly fine-tuning though) of location-aware MLLMs recently, eg, Shikra[1], Ferret[2], GLaMM[3]. Moreover, Ferret-v2 [4] also proposed dense referring and dense grounding as a pre-training stage. Those two tasks are quite similar to what this work proposes, and they even involve multiple regions in one round.

2. Missing comparison with many location-aware Multimodal LLM methods.  Location-aware MLLMs load pre-trained LLMs and train on a moderate amount of data (100k-1M) in a few steps (mostly within 3 epochs). They can already show great performance in basic tasks and reasoning tasks. This work instead trains the model from scratch with large-scale data (1B) with longer training time. The authors should analyze the benefits and drawbacks of each line of work.

3. Missing Evaluations on: (1). Location-aware Reasoning tasks, such as Visual-7W[5], LookTwice-QA[6] used in Shikra[1] and Ferret-Bench used in Ferret[2]. (2). Grounded captioning capability is not evaluated, for example, Flickr30k used in GLaMM and Ferret. (3). Comparison with Multimodal LLMs in RefCOCOs and above-mentioned tasks.

4. Training data, WebLI dataset, is not available to the public. Considering the main novelty, location-aware pre-training, largely depends on the scale and quality of pre-training dataset, the work is hard to be reproduced.


Refs: \
[1] Chen, Keqin, et al. "Shikra: Unleashing multimodal llm's referential dialogue magic." arXiv preprint arXiv:2306.15195 (2023). \
[2] You, Haoxuan, et al. "Ferret: Refer and ground anything anywhere at any granularity." ICLR 2024 \
[3] Rasheed, Hanoona, et al. "Glamm: Pixel grounding large multimodal model." CVPR. 2024. \
[4] Zhang, Haotian, et al. "Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models." arXiv preprint arXiv:2404.07973 (2024). \
[5] Zhu, Yuke, et al. "Visual7w: Grounded question answering in images." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[6] Mani, Arjun, et al. "Point and ask: Incorporating pointing into visual question answering." arXiv preprint arXiv:2011.13681 (2020).
questions: questions: Please see the weaknesses.

=====================

Thank the author for the answers. Please make sure to add the experiments of Visual7W and LookTwice-QA as promised. I'd like to raise my rating.
limitations: limitations: Yes, It is discussed in the appendix.
paper_summary: paper_summary: The paper introduces LocCa, a new visual pretraining approach that incorporates location-specific tasks into image captioning-based vision language models, improving their ability to extract detailed information from images. The authors propose two location-aware tasks, automatic referring expressions (AREF), which predicts bounding box coordinates from captions, and grounded captioning (GCAP), to jointly predict the box coordinates and captions from image, utilizing a multitask encoder-decoder architecture. As a result, LocCa significantly outperforms standard captioning models on localization challenges, achieving top results on datasets such as RefCOCO/+/g, while maintaining similar performance on broader tasks.
strength_weakness: strength_weakness: - The model's training process relies on regional captions and bounding boxes, necessitating the use of pre-existing tools like OWL-ViT to produce detailed object locations for its training data. This approach creates a significant dependency on these off-the-shelf models, potentially limiting the quality of the training data. Assembling a large-scale dataset becomes challenging under these constraints. While the authors have managed to curate an impressive billion-scale dataset, this quantity doesn't necessarily ensure data quality. The reliance on external models for data generation raises questions about the overall integrity and reliability of the training information.

- Typo
    - Line 134: AREFtask → AREF task
    - Line 240: dentified → identified
questions: questions: - I have two questions about the proposed training tasks.
    - During the pretraining process or after training, is there any trend related to the size of the object in the image? Such analysis can be done among downstream tasks, such as object detection or RefCOCO series datasets.
    - Can you provide more details on the detection visualization analysis presented in Appendix C? Specifically, what is the connection between observing only one object per example and the substantial bounding box overlap observed without Non-Maximum Suppression (NMS)? Additionally, doesn't the LocCa pretraining process involve comprehension of multiple objects through its three tasks: captioning, AREF, and GCAP? How does this align with the single-object observation?
limitations: limitations: The paper addresses the limitations.
paper_summary: paper_summary: This paper explored integrating region captions into caption-based language-image pertaining. Specifically, in addition to the autoregressive text modeling on global captions, the proposed LocCa also predicts constructed strings of "{bounding box}-{region caption}" and "{region caption}-{bounding box}". Although the method is conceptually simple, the author benchmarked LocCa in a variety of tasks and showed that it could achieve strong performance.
strength_weakness: strength_weakness: 1. **Objectives of LocCa**. Overall, the paper is well-written and easy to follow. However, the presentation on LocCa objectives in section 3.2 is a bit confusing starting from line 131. The "dual-faceted" loss is hard to interpret for first-time readers. If I understand correctly, it might mean that LocCa calculates loss on both box $b$ and region caption $c$. The author may consider rewriting this part to improve readability. Also, citing references regarding "contrasting with traditional approaches" could help readers understand the novelty. Additionally, how to construct a batch with three tasks should also be mentioned somewhere in section 3.

1. **Box predictions in GCap**. In the grounded captioning (GCap) task, conditioned on the image, the model needs to first predict a bounding box, and then predict the caption. The task of predicting a box from the image looks like an object proposal generation task, but the supervision here is only one box per sample. However, it's not clear whether introducing this supervision of object proposal generation is desired by the authors since there are no relevant discussions.

1. **Ablation of pretraining tasks**. Table 6 is very important for this paper. The authors ablated AREF and GCAP tasks respectively and showed that it leads to performance drops. To make the paper more complete, the authors may consider adding the ablation of only removing the box part and keeping the prediction to the region captions. This will help demonstrate the value of location awareness. Also, ablating the global caption and keeping AREF and/or GCAP may also be interesting.

1. **String-based box representation**. Compared to special tokenizations for box coordinates, whether the string-based method require more tokens to represent one bounding box? Whether it lead to less efficient decoding? Also, the hyperparameter of box coordinates resolution also plays an important role here and needs to be discussed and studied with ablations. The authors may see the discussion and explorations in the following papers for reference on bounding box representations:
    - [1] Shikra: Unleashing Multimodal LLM’s Referential Dialogue Magic
    - [2] Kosmos-2: Grounding Multimodal Large Language Models to the World
    - [3] RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
questions: questions: N/A
limitations: limitations: N/A

Comments: title: Final rating
comment: Thank you for the rebuttal. As most of my concerns and questions are addressed, I will maintain my initial rating.
Thanks for your recognition of our work and for raising the score! We will add the mentioned revisions in the final manuscript.
Dear reviewer,

Thank you again for the detailed and constructive comments.

As the discussion period is about to end, we would like to ensure we've addressed all of your questions and concerns. If you feel we have satisfactorily responded, please let us know. Otherwise, please let us know your remaining concerns so we can address them before the discussion period closes.

Sincerely
Dear reviewer,

Thank you again for the detailed and constructive comments.

As the discussion period is about to end, we would like to ensure we've addressed all of your questions and concerns. If you feel we have satisfactorily responded, please let us know. Otherwise, please let us know your remaining concerns so we can address them before the discussion period closes.

Sincerely
Dear reviewer,

Thank you again for the detailed and constructive comments.

As the discussion period is about to end, we would like to ensure we've addressed all of your questions and concerns. If you feel we have satisfactorily responded, please let us know. Otherwise, please let us know your remaining concerns so we can address them before the discussion period closes.

Sincerely
Thanks for the response and updated experiments. They are all sound to me. Please make sure the mentioned revisions are incorporated into the final version.

I have updated my rating from weak accept to accept.