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.
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Recent thrilling progress in large-scale text-to-image (T2I) models has unlocked unprecedented synthesis quality of AI-generated content (AIGC) including image generation 3D and video composition. Further personalized techniques enable appealing customized production of a novel concept given only several images as reference. However an intriguing problem persists: Is it possible to capture multiple novel concepts from one single reference image? In this paper we identify that existing approaches fail to preserve visual consistency with the reference image and eliminate cross-influence from concepts. To alleviate this we propose an attention calibration mechanism to improve the concept-level understanding of the T2I model. Specifically we first introduce new learnable modifiers bound with classes to capture attributes of multiple concepts. Then the classes are separated and strengthened following the activation of the cross-attention operation ensuring comprehensive and self-contained concepts. Additionally we suppress the attention activation of different classes to mitigate mutual influence among concepts. Together our proposed method dubbed DisenDiff can learn disentangled multiple concepts from one single image and produce novel customized images with learned concepts. We demonstrate that our method outperforms the current state of the art in both qualitative and quantitative evaluations. More importantly our proposed techniques are compatible with LoRA and inpainting pipelines enabling more interactive experiences.
What When and Where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
PDF ↗Spatio-temporal grounding describes the task of localizing events in space and time e.g. in video data based on verbal descriptions only. Models for this task are usually trained with human-annotated sentences and bounding box supervision. This work addresses this task from a multimodal supervision perspective proposing a framework for spatio-temporal action grounding trained on loose video and subtitle supervision only without human annotation. To this end we combine local representation learning which focuses on leveraging fine-grained spatial information with a global representation encoding that captures higher-level representations and incorporates both in a joint approach. To evaluate this challenging task in a real-life setting a new benchmark dataset is proposed providing dense spatio-temporal grounding annotations in long untrimmed multi-action instructional videos for over 5K events. We evaluate the proposed approach and other methods on the proposed and standard downstream tasks showing that our method improves over current baselines in various settings including spatial temporal and untrimmed multi-action spatio-temporal grounding.
We present Image Sculpting a new framework for editing 2D images by incorporating tools from 3D geometry and graphics. This approach differs markedly from existing methods which are confined to 2D spaces and typically rely on textual instructions leading to ambiguity and limited control. Image Sculpting converts 2D objects into 3D enabling direct interaction with their 3D geometry. Post-editing these objects are re-rendered into 2D merging into the original image to produce high-fidelity results through a coarse-to-fine enhancement process. The framework supports precise quantifiable and physically-plausible editing options such as pose editing rotation translation 3D composition carving and serial addition. It marks an initial step towards combining the creative freedom of generative models with the precision of graphics pipelines.
Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However when adapting VLMs to specialized domains such as remote sensing and medical imaging domain prompt learning remains underexplored. While large-scale domain-specific foundation models can help tackle this challenge their concentration on a single vision level makes it challenging to prompt both vision and language modalities. To overcome this we propose to leverage domain-specific knowledge from domain-specific foundation models to transfer the robust recognition ability of VLMs from generalized to specialized domains using quaternion networks. Specifically the proposed method involves using domain-specific vision features from domain-specific foundation models to guide the transformation of generalized contextual embeddings from the language branch into a specialized space within the quaternion networks. Moreover we present a hierarchical approach that generates vision prompt features by analyzing intermodal relationships between hierarchical language prompt features and domain-specific vision features. In this way quaternion networks can effectively mine the intermodal relationships in the specific domain facilitating domain-specific vision-language contrastive learning. Extensive experiments on domain-specific datasets show that our proposed method achieves new state-of-the-art results in prompt learning.
With the growing size of pre-trained models full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible. In this paper we propose a new parameter-efficient fine-tuning method Gradient-based Parameter Selection (GPS) demonstrating that only tuning a few selected parameters from the pre-trained model while keeping the remainder of the model frozen can generate similar or better performance compared with the full model fine-tuning method. Different from the existing popular and state-of-the-art parameter-efficient fine-tuning approaches our method does not introduce any additional parameters and computational costs during both the training and inference stages. Another advantage is the model-agnostic and non-destructive property which eliminates the need for any other design specific to a particular model. Compared with the full fine-tuning GPS achieves 3.33% (91.78% vs. 88.45% FGVC) and 9.61% (73.1% vs. 65.57% VTAB) improvement of the accuracy with tuning only 0.36% parameters of the pre-trained model on average over 24 image classification tasks; it also demonstrates a significant improvement of 17% and 16.8% in mDice and mIoU respectively on medical image segmentation task. Moreover GPS achieves state-of-the-art performance compared with existing PEFT methods. The code will be available in https://github.com/FightingFighting/GPS.git.
Human Mesh Recovery (HMR) aims to estimate the 3D human body from 2D images which is a challenging task due to inherent ambiguities in translating 2D observations to 3D space. A novel approach called PostureHMR is proposed to leverage a multi-step diffusion-style process which converts this task into a posture transformation from an SMPL T-pose mesh to the target mesh. To inject the learning process of posture transformation with the physical structure of the human body model a kinematics-based forward process is proposed to interpolate the intermediate state with pose and shape decomposition. Moreover a mesh-to-posture (M2P) decoder is designed by combining the input of 3D and 2D mesh constraints estimated from the image to model the posture changes in the reverse process. It mitigates the difficulties of posture change learning directly from RGB pixels. To overcome the limitation of pixel-level misalignment of modeling results with the input image a new trimap-based rendering loss is designed to highlight the areas with poor recognition. Experiments conducted on three widely used datasets demonstrate that the proposed approach outperforms the state-of-the-art methods.
Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competitive baseline results from combining popular clustering algorithms with recent improvements in 3D geometry estimation. However such purely geometric methods are understandably oblivious to plane semantics which are crucial to discerning distinct planes. To overcome this limitation we propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches and our strong geometric baseline for the task of plane estimation.
Parkinson's disease (PD) is a devastating movement disorder accelerating in global prevalence but a lack of precision symptom measurement has made the development of effective therapies challenging. The Unified Parkinson's Disease Rating Scale (UPDRS) is the gold-standard for assessing motor symptom severity yet its manual scoring criteria are vague and subjective resulting in coarse and noisy clinical assessments. Machine learning approaches have the potential to modernize PD symptom assessments by making them more quantitative objective and scalable. However the lack of benchmark video datasets for PD motor exams hinders model development. Here we introduce the TULIP dataset to bridge this gap. TULIP emphasizes precision and comprehensiveness comprising multi-view video recordings (6 cameras) of all 25 UPDRS motor exam components together with ratings by 3 clinical experts in a cohort of Parkinson's patients and healthy controls. The multi-view recordings enable 3D reconstructions of body movement that better capture disease signatures than more conventional 2D methods. Using the dataset we establish a baseline model for predicting UPDRS scores from 3D poses illustrating how existing diagnostics could be automated. Looking ahead TULIP could aid the development of new precision diagnostics that transcend UPDRS scores providing a deeper understanding of PD and its potential treatments.
Vision-language (VL) models have achieved unprecedented success recently in which the connection module is the key to bridge the modality gap. Nevertheless the abundant visual clues are not sufficiently exploited in most existing methods. On the vision side most existing approaches only use the last feature of the vision tower without using the low-level features. On the language side most existing methods only introduce shallow vision-language interactions. In this paper we present a vision-inspired vision-language connection module dubbed as VIVL which efficiently exploits the vision cue for VL models. To take advantage of the lowerlevel information from the vision tower a feature pyramid extractor (FPE) is introduced to combine features from different intermediate layers which enriches the visual cue with negligible parameters and computation overhead. To enhance VL interactions we propose deep vision-conditioned prompts (DVCP) that allows deep interactions of vision and language features efficiently. Our VIVL exceeds the previous state-of-the-art method by 18.1 CIDEr when training from scratch on the COCO caption task which greatly improves the data efficiency. When used as a plug-in module VIVL consistently improves the performance for various backbones and VL frameworks delivering new state-of-the-art results on multiple benchmarks e.g. NoCaps and VQAv2.
We present a method that uses a text-to-image model to generate consistent content across multiple image scales enabling extreme semantic zooms into a scene e.g. ranging from a wide-angle landscape view of a forest to a macro shot of an insect sitting on one of the tree branches. We achieve this through a joint multi-scale diffusion sampling approach that encourages consistency across different scales while preserving the integrity of each individual sampling process. Since each generated scale is guided by a different text prompt our method enables deeper levels of zoom than traditional super-resolution methods that may struggle to create new contextual structure at vastly different scales. We compare our method qualitatively with alternative techniques in image super-resolution and outpainting and show that our method is most effective at generating consistent multi-scale content.
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data. In the computer vision community PEFT has shown effectiveness in image classification but little research has studied its ability for image segmentation. Fine-tuning segmentation models usually require a heavier adjustment of parameters to align the proper projection directions in the parameter space for new scenarios. This raises a challenge to existing PEFT algorithms as they often inject a limited number of individual parameters into each block which prevents substantial adjustment of the projection direction of the parameter space due to the limitation of Hidden Markov Chain along blocks. In this paper we equip PEFT with a cross-block orchestration mechanism to enable the adaptation of the Segment Anything Model (SAM) to various downstream scenarios. We introduce a novel inter-block communication module which integrates a learnable relation matrix to facilitate communication among different coefficient sets of each PEFT block's parameter space. Moreover we propose an intra-block enhancement module which introduces a linear projection head whose weights are generated from a hyper-complex layer further enhancing the impact of the adjustment of projection directions on the entire parameter space. Extensive experiments on diverse benchmarks demonstrate that our proposed approach consistently improves the segmentation performance significantly on novel scenarios with only around 1K additional parameters.
Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations. In this regard studies on WSSGG have utilized image captions to obtain unlocalized triplets while primarily focusing on grounding the unlocalized triplets over image regions. However they have overlooked the two issues involved in the triplet formation process from the captions: 1) Semantic over-simplification issue arises when extracting triplets from captions where fine-grained predicates in captions are undesirably converted into coarse-grained predicates resulting in a long-tailed predicate distribution and 2) Low-density scene graph issue arises when aligning the triplets in the caption with entity/predicate classes of interest where many triplets are discarded and not used in training leading to insufficient supervision. To tackle the two issues we propose a new approach i.e. Large Language Model for weakly-supervised SGG (LLM4SGG) where we mitigate the two issues by leveraging the LLM's in-depth understanding of language and reasoning ability during the extraction of triplets from captions and alignment of entity/predicate classes with target data. To further engage the LLM in these processes we adopt the idea of Chain-of-Thought and the in-context few-shot learning strategy. To validate the effectiveness of LLM4SGG we conduct extensive experiments on Visual Genome and GQA datasets showing significant improvements in both Recall@K and mean Recall@K compared to the state-of-the-art WSSGG methods. A further appeal is that LLM4SGG is data-efficient enabling effective model training with a small amount of training images.
Existing methods to fine-tune LLMs like Adapter Prefix-tuning and LoRA which introduce extra modules or additional input sequences to inject new skills or knowledge may compromise the innate abilities of LLMs. In this paper we propose LLaMA-Excitor a lightweight method that stimulates the LLMs' potential to better follow instructions by gradually paying more attention to worthwhile information. Specifically the LLaMA-Excitor does not directly change the intermediate hidden state during the self-attention calculation of the transformer structure. We designed the Excitor block as a bypass module for the similarity score computation in LLMs' self-attention to reconstruct keys and change the importance of values by learnable prompts. LLaMA-Excitor ensures a self-adaptive allocation of additional attention to input instructions thus effectively preserving LLMs' pre-trained knowledge when fine-tuning LLMs on low-quality instruction-following datasets. Furthermore we unify the modeling of multi-modal tuning and language-only tuning extending LLaMA-Excitor to a powerful visual instruction follower without the need for complex multi-modal alignment. Our proposed approach is evaluated in language-only and multi-modal tuning experimental scenarios. Notably LLaMA-Excitor is the only method that maintains basic capabilities while achieving a significant improvement (+6%) on the MMLU benchmark. In the visual instruction tuning we achieve a new state-of-the-art image captioning performance of 157.5 CIDEr on MSCOCO and a comparable performance (88.39%) on ScienceQA to cutting-edge models with more parameters and extensive vision-language pertaining.
Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during training and illumination-specific hyper-parameters limiting their ability to handle unseen scenarios. In this paper we propose a new zero-reference low-light enhancement framework trainable solely with normal light images. To accomplish this we devise an illumination-invariant prior inspired by the theory of physical light transfer. This prior serves as the bridge between normal and low-light images. Then we develop a prior-to-image framework trained without low-light data. During testing this framework is able to restore our illumination-invariant prior back to images automatically achieving low-light enhancement. Within this framework we leverage a pretrained generative diffusion model for model ability introduce a bypass decoder to handle detail distortion as well as offer a lightweight version for practicality. Extensive experiments demonstrate our framework's superiority in various scenarios as well as good interpretability robustness and efficiency. Code is available on our project homepage: http://daooshee.github.io/QuadPrior-Website/
Omnidirectional cameras are extensively used in various applications to provide a wide field of vision. However they face a challenge in synthesizing novel views due to the inevitable presence of dynamic objects including the photographer in their wide field of view. In this paper we introduce a new approach called Omnidirectional Local Radiance Fields (OmniLocalRF) that can render static-only scene views removing and inpainting dynamic objects simultaneously. Our approach combines the principles of local radiance fields with the bidirectional optimization of omnidirectional rays. Our input is an omnidirectional video and we evaluate the mutual observations of the entire angle between the previous and current frames. To reduce ghosting artifacts of dynamic objects and inpaint occlusions we devise a multi-resolution motion mask prediction module. Unlike existing methods that primarily separate dynamic components through the temporal domain our method uses multi-resolution neural feature planes for precise segmentation which is more suitable for long 360-degree videos. Our experiments validate that OmniLocalRF outperforms existing methods in both qualitative and quantitative metrics especially in scenarios with complex real-world scenes. In particular our approach eliminates the need for manual interaction such as drawing motion masks by hand and additional pose estimation making it a highly effective and efficient solution.
Non-Exemplar Class Incremental Learning (NECIL) involves learning a classification model on a sequence of data without access to exemplars from previously encountered old classes. Such a stringent constraint always leads to catastrophic forgetting of the learned knowledge. Currently existing methods either employ knowledge distillation techniques or preserved class prototypes to sustain prior knowledge. However two critical issues still persist. On the one hand as the model is continually updated the preserved prototypes of old classes will inevitably derive from the suitable location in the feature space of the new model. On the other hand due to the lack of exemplars the features of new classes will take the place of similar old classes which breaks the classification boundary. To address these challenges we propose a Feature Calibration and Separation (FCS) method for NECIL. Our approach comprises a Feature Calibration Network (FCN) that adapts prototypes of old classes to the new model via optimal transport learning approximating the drift of prototypes caused by model evolution. Additionally we also propose a Prototype-Involved Contrastive Loss (PIC) that enhances feature separation among different classes. Specifically to mitigate the boundary distortion arising from the interplay of classes from different learning stages prototypes are involved in pushing the feature of new classes away from the old classes. Extensive experiments on three datasets with different settings have demonstrated the superiority of our FCS method against the state-of-the-art class incremental learning approaches. Code is available at https://github.com/zhoujiahuan1991/CVPR2024-FCS.
We introduce GoMAvatar a novel approach for real-time memory-efficient high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rendering from novel viewpoints while seamlessly integrating with rasterization-based graphics pipelines. Central to our method is the Gaussians-on-Mesh (GoM) representation a hybrid 3D model combining rendering quality and speed of Gaussian splatting with geometry modeling and compatibility of deformable meshes. We assess GoMAvatar on ZJU-MoCap PeopleSnapshot and various YouTube videos. GoMAvatar matches or surpasses current monocular human modeling algorithms in rendering quality and significantly outperforms them in computational efficiency (43 FPS) while being memory-efficient (3.63 MB per subject).
We present a novel semi-supervised framework for breast ultrasound (BUS) image segmentation which is a very challenging task owing to (1) large scale and shape variations of breast lesions and (2) extremely ambiguous boundaries caused by massive speckle noise and artifacts in BUS images. While existing models achieved certain progress in this task we believe the main bottleneck nowadays for further improvement is that we still cannot deal with hard cases well. Our framework aims to break through this bottleneck which includes two innovative components: an adaptive patch augmentation scheme and a hard-patch contrastive learning module. We first identify hard patches by computing the average entropy of each patch and then shield hard patches to prevent them from being cropped out while performing random patch cutmix. Such a scheme is able to prevent hard regions from being inadequately trained under strong augmentation. We further develop a new hard-patch contrastive learning algorithm to direct model attention to hard regions by applying extra contrast to pixels in hard patches further improving segmentation performance on hard cases. We demonstrate the superiority of our framework to state-of-the-art approaches on two famous BUS datasets achieving better performance under different labeling conditions. The code is available at https://github.com/jjjsyyy/PH-Net.
In the field of class incremental learning (CIL) generative replay has become increasingly prominent as a method to mitigate the catastrophic forgetting alongside the continuous improvements in generative models. However its application in class incremental object detection (CIOD) has been significantly limited primarily due to the complexities of scenes involving multiple labels. In this paper we propose a novel approach called stable diffusion deep generative replay (SDDGR) for CIOD. Our method utilizes a diffusion-based generative model with pre-trained text-to-image diffusion networks to generate realistic and diverse synthetic images. SDDGR incorporates an iterative refinement strategy to produce high-quality images encompassing old classes. Additionally we adopt an L2 knowledge distillation technique to improve the retention of prior knowledge in synthetic images. Furthermore our approach includes pseudo-labeling for old objects within new task images preventing misclassification as background elements. Extensive experiments on the COCO 2017 dataset demonstrate that SDDGR significantly outperforms existing algorithms achieving a new state-of-the-art in various CIOD scenarios.