CVPR 2024 Papers

In real-world scenarios image recognition tasks such as semantic segmentation and object detection often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However due to the ill-posed property of SR it is challenging for typical SR methods to restore task-relevant high-frequency contents which may dilute the advantage of utilizing the SR method. Therefore in this paper we propose Super-Resolution for Image Recognition (SR4IR) that effectively guides the generation of SR images beneficial to achieving satisfactory image recognition performance when processing LR images. The critical component of our SR4IR is the task-driven perceptual (TDP) loss that enables the SR network to acquire task-specific knowledge from a network tailored for a specific task. Moreover we propose a cross-quality patch mix and an alternate training framework that significantly enhances the efficacy of the TDP loss by addressing potential problems when employing the TDP loss. Through extensive experiments we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task including semantic segmentation object detection and image classification. The implementation code is available at https://github.com/JaehaKim97/SR4IR.

We propose a framework for automatic colorization that allows for iterative editing and modifications. The core of our framework lies in an imagination module: by understanding the content within a grayscale image we utilize a pre-trained image generation model to generate multiple images that contain the same content. These images serve as references for coloring mimicking the process of human experts. As the synthesized images can be imperfect or different from the original grayscale image we propose a Reference Refinement Module to select the optimal reference composition. Unlike most previous end-to-end automatic colorization algorithms our framework allows for iterative and localized modifications of the colorization results because we explicitly model the coloring samples. Extensive experiments demonstrate the superiority of our framework over existing automatic colorization algorithms in editability and flexibility. Project page: https://xy-cong.github.io/imagine-colorization/.

Identifying the chemical structure from a graphical representation or image of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet existing methods for chemical structure recognition do not typically generalize well and show diminished effectiveness when confronted with domains where data is sparse or costly to generate such as hand-drawn molecule images. To address this limitation we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches our method provides atom-level localization and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency accuracy and atom-level entity prediction.

The laws of model size data volume computation and model performance have been extensively studied in the field of Natural Language Processing (NLP). However the scaling laws in Scene Text Recognition (STR) have not yet been investigated. To address this we conducted comprehensive studies that involved examining the correlations between performance and the scale of models data volume and computation in the field of text recognition. Conclusively the study demonstrates smooth power laws between performance and model size as well as training data volume when other influencing factors are held constant. Additionally we have constructed a large-scale dataset called REBU-Syn which comprises 6 million real samples and 18 million synthetic samples. Based on our scaling law and new dataset we have successfully trained a scene text recognition model achieving a new state-of-the-art on 6 common test benchmarks with a top-1 average accuracy of 97.42%. The models and dataset are publicly available at \href https://github.com/large-ocr-model/large-ocr-model.github.io large-ocr-model.github.io .

Active recognition which allows intelligent agents to explore observations for better recognition performance serves as a prerequisite for various embodied AI tasks such as grasping navigation and room arrangements. Given the evolving environment and the multitude of object classes it is impractical to include all possible classes during the training stage. In this paper we aim at advancing active open-vocabulary recognition empowering embodied agents to actively perceive and classify arbitrary objects. However directly adopting recent open-vocabulary classification models like Contrastive Language Image Pretraining (CLIP) poses its unique challenges. Specifically we observe that CLIP's performance is heavily affected by the viewpoint and occlusions compromising its reliability in unconstrained embodied perception scenarios. Further the sequential nature of observations in agent-environment interactions necessitates an effective method for integrating features that maintains discriminative strength for open-vocabulary classification. To address these issues we introduce a novel agent for active open-vocabulary recognition. The proposed method leverages inter-frame and inter-concept similarities to navigate agent movements and to fuse features without relying on class-specific knowledge. Compared to baseline CLIP model with 29.6% accuracy on ShapeNet dataset the proposed agent could achieve 53.3% accuracy for open-vocabulary recognition without any fine-tuning to the equipped CLIP model. Additional experiments conducted with the Habitat simulator further affirm the efficacy of our method.

Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames performing even worse than single-modality models. While applying the common dropout techniques to the video modality enhances robustness to missing frames it simultaneously results in a performance loss when dealing with complete data input. In this study we delve into this contrasting phenomenon through the lens of modality bias and uncover that an excessive modality bias towards the audio modality induced by dropout constitutes the fundamental cause. Next we present the Modality Bias Hypothesis (MBH) to systematically describe the relationship between the modality bias and the robustness against missing modality in multimodal systems. Building on these findings we propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality maintaining performance and robustness simultaneously. Finally to address an entirely missing modality we adopt adapters to dynamically switch decision strategies. The effectiveness of our proposed approach is evaluated through comprehensive experiments on the MISP2021 and MISP2022 datasets. Our code is available at https://github.com/dalision/ModalBiasAVSR.

In this paper we address web-scale visual entity recognition specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia. One way of approaching a problem of such scale is using dual encoder models (e.g. CLIP) where all the entity names and query images are embedded into a unified space paving the way for an approximate kNN search. Alternatively it is also possible to re-purpose a captioning model to directly generate the entity names for a given image. In contrast we introduce a novel Generative Entity Recognition (GER) framework which given an input image learns to auto-regressively decode a semantic and discriminative "code" identifying the target entity. Our experiments demonstrate the efficacy of this GER paradigm showcasing state-of-the-art performance on the challenging OVEN benchmark. GER surpasses strong captioning dual-encoder visual matching and hierarchical classification baselines affirming its advantage in tackling the complexities of web-scale recognition.