CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Our proposed architecture trained in a self-supervised manner outperforms existing approaches in several biologically relevant downstream tasks. Additionally it can be used to bridge the gap for the first time between assays with different microscopes channel numbers or types by embedding various image and experimental modalities into a unified biological image representation. The latter should facilitate interdisciplinary studies and pave the way for better adoption of deep learning in biological image-based analyses.
The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs focusing on image-text alignment. However existing methods primarily rely on cosine similarity for alignment which may not fully capture the complex relationship between medical images and reports. To address this gap we introduce a novel approach called Cross-Attention Alignment for Radiology Zero-Shot Classification (CARZero). Our approach innovatively leverages cross-attention mechanisms to process image and report features creating a Similarity Representation that more accurately reflects the intricate relationships in medical semantics. This representation is then linearly projected to form an image-text similarity matrix for cross-modality alignment. Additionally recognizing the pivotal role of prompt selection in zero-shot learning CARZero incorporates a Large Language Model-based prompt alignment strategy. This strategy standardizes diverse diagnostic expressions into a unified format for both training and inference phases overcoming the challenges of manual prompt design. Our approach is simple yet effective demonstrating state-of-the-art performance in zero-shot classification on five official chest radiograph diagnostic test sets including remarkable results on datasets with long-tail distributions of rare diseases. This achievement is attributed to our new image-text alignment strategy which effectively addresses the complex relationship between medical images and reports.
Our approach is simple yet effective demonstrating state-of-the-art performance in zero-shot classification on five official chest radiograph diagnostic test sets including remarkable results on datasets with long-tail distributions of rare diseases. This achievement is attributed to our new image-text alignment strategy which effectively addresses the complex relationship between medical images and reports. Code and models are available at https://github.com/laihaoran/CARZero.
3D hand-object interaction data is scarce due to the hardware constraints in scaling up the data collection process. In this paper we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data. Our model is a conditional diffusion model that takes both the 3D hand-object geometric structure and text description as inputs for image synthesis. This offers a more controllable and realistic synthesis as we can specify the structure and style inputs in a disentangled manner. HOIDiffusion is trained by leveraging a diffusion model pre-trained on large-scale natural images and a few 3D human demonstrations. Beyond controllable image synthesis we adopt the generated 3D data for learning 6D object pose estimation and show its effectiveness in improving perception systems. Project page: https://mq-zhang1.github.io/HOIDiffusion.
We present VecFusion a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raster diffusion model followed by a vector diffusion model. The raster model generates low-resolution rasterized fonts with auxiliary control point information capturing the global style and shape of the font while the vector model synthesizes vector fonts conditioned on the low-resolution raster fonts from the first stage. To synthesize long and complex curves our vector diffusion model uses a transformer architecture and a novel vector representation that enables the modeling of diverse vector geometry and the precise prediction of control points. Our experiments show that in contrast to previous generative models for vector graphics our new cascaded vector diffusion model generates higher quality vector fonts with complex structures and diverse styles.
Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers which however are not always grounded in the input image. We investigate this phenomenon usually referred to as "hallucination" and show that it stems from an excessive reliance on the language prior. In particular we show that as more tokens are generated the reliance on the visual prompt decreases and this behavior strongly correlates with the emergence of hallucinations. To reduce hallucinations we introduce Multi-Modal Mutual-Information Decoding (M3ID) a new sampling method for prompt amplification. M3ID amplifies the influence of the reference image over the language prior hence favoring the generation of tokens with higher mutual information with the visual prompt. M3ID can be applied to any pre-trained autoregressive VLM at inference time without necessitating further training and with minimal computational overhead. If training is an option we show that M3ID can be paired with Direct Preference Optimization (DPO) to improve the model's reliance on the prompt image without requiring any labels. Our empirical findings show that our algorithms maintain the fluency and linguistic capabilities of pre-trained VLMs while reducing hallucinations by mitigating visually ungrounded answers.
Our empirical findings show that our algorithms maintain the fluency and linguistic capabilities of pre-trained VLMs while reducing hallucinations by mitigating visually ungrounded answers. Specifically for the LLaVA 13B model M3ID and M3ID+DPO reduce the percentage of hallucinated objects in captioning tasks by 25% and 28% respectively and improve the accuracy on VQA benchmarks such as POPE by 21% and 24%.
We are witnessing significant breakthroughs in the technology for generating 3D objects from text. Existing approaches either leverage large text-to-image models to optimize a 3D representation or train 3D generators on object-centric datasets. Generating entire scenes however remains very challenging as a scene contains multiple 3D objects diverse and scattered. In this work we introduce SceneWiz3D - a novel approach to synthesize high-fidelity 3D scenes from text. We marry the locality of objects with globality of scenes by introducing a hybrid 3D representation - explicit for objects and implicit for scenes. Remarkably an object being represented explicitly can be either generated from text using conventional text-to-3D approaches or provided by users. To configure the layout of the scene and automatically place objects we apply the Particle Swarm Optimization technique during the optimization process. Furthermore it is difficult for certain parts of the scene (e.g. corners occlusion) to receive multi-view supervision leading to inferior geometry. We incorporate an RGBD panorama diffusion model to mitigate it resulting in high-quality geometry. Extensive evaluation supports that our approach achieves superior quality over previous approaches enabling the generation of detailed and view-consistent 3D scenes.
corners occlusion) to receive multi-view supervision leading to inferior geometry. We incorporate an RGBD panorama diffusion model to mitigate it resulting in high-quality geometry. Extensive evaluation supports that our approach achieves superior quality over previous approaches enabling the generation of detailed and view-consistent 3D scenes. Our project website is at https://zqh0253.github.io/SceneWiz3D.\\
We propose EMAGE a framework to generate full-body human gestures from audio and masked gestures encompassing facial local body hands and global movements. To achieve this we first introduce BEAT2 (BEAT-SMPLX-FLAME) a new mesh-level holistic co-speech dataset. BEAT2 combines a MoShed SMPL-X body with FLAME head parameters and further refines the modeling of head neck and finger movements offering a community-standardized high-quality 3D motion captured dataset. EMAGE leverages masked body gesture priors during training to boost inference performance. It involves a Masked Audio Gesture Transformer facilitating joint training on audio-to-gesture generation and masked gesture reconstruction to effectively encode audio and body gesture hints. Encoded body hints from masked gestures are then separately employed to generate facial and body movements. Moreover EMAGE adaptively merges speech features from the audio's rhythm and content and utilizes four compositional VQ-VAEs to enhance the results' fidelity and diversity. Experiments demonstrate that EMAGE generates holistic gestures with state-of-the-art performance and is flexible in accepting predefined spatial-temporal gesture inputs generating complete audio-synchronized results. Our code and dataset are available. https://pantomatrix.github.io/EMAGE/
Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper we approach the text-to-image task from a different perspective where a 2D image is represented as an implicit neural representation (INR). We show that straightforward conditioning of the unconditional INR-based GAN method on text inputs is not enough to achieve good performance. We propose a word-level attention-based weight modulation operator that controls the generation process of INR-GAN based on hypernetworks. Our experiments on benchmark datasets show that HyperCGAN achieves competitive performance to existing pixel-based methods and retains the properties of continuous generative models.
Vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example although CLIP achieves impressive accuracy on ImageNet (60-80%) its performance drops below 10% for more than ten concepts like night snake presumably due to their limited presence in the pretraining data. However measuring the frequency of concepts in VLMs' large-scale datasets is challenging. We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts. Our analysis confirms that popular datasets such as LAION exhibit a long-tailed concept distribution yielding biased performance in VLMs. We also find that downstream applications of VLMs including visual chatbots (e.g. GPT-4V) and text-to-image models (e.g. Stable Diffusion) often fail to recognize or generate images of rare concepts identified by our method. To mitigate the imbalanced performance of zero-shot VLMs we propose REtrieval-Augmented Learning (REAL). First instead of prompting VLMs using the original class names REAL uses their most frequent synonyms found in pretraining texts.
To mitigate the imbalanced performance of zero-shot VLMs we propose REtrieval-Augmented Learning (REAL). First instead of prompting VLMs using the original class names REAL uses their most frequent synonyms found in pretraining texts. This simple change already outperforms costly human-engineered and LLM-enriched prompts over nine benchmark datasets. Second REAL trains a linear classifier on a small yet balanced set of pretraining data retrieved using concept synonyms. REAL surpasses the previous zero-shot SOTA using 400x less storage and 10000x less training time!
Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection significantly generalizing the powerful capabilities of the detector to identify more unknown object categories. However these methods face significant challenges in background interpretation and model overfitting and thus often result in the loss of crucial background knowledge giving rise to sub-optimal inference performance of the detector. To mitigate these issues we present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge thus enhancing the detection performance w.r.t. base and novel categories. Specifically we devise three modules: Background Category-specific Prompt Background Object Discovery and Inference Probability Rectification to empower the detector to discover represent and leverage implicit object knowledge explored from background proposals. Evaluation on two benchmark datasets OV-COCO and OV-LVIS demonstrates the superiority of our proposed method over existing state-of-the-art approaches in handling the OVD tasks.
We present HumanNeRF-SE a simple yet effective method that synthesizes diverse novel pose images with simple input. Previous HumanNeRF works require a large number of optimizable parameters to fit the human images. Instead we reload these approaches by combining explicit and implicit human representations to design both generalized rigid deformation and specific non-rigid deformation. Our key insight is that explicit shape can reduce the sampling points used to fit implicit representation and frozen blending weights from SMPL constructing a generalized rigid deformation can effectively avoid overfitting and improve pose generalization performance. Our architecture involving both explicit and implicit representation is simple yet effective. Experiments demonstrate our model can synthesize images under arbitrary poses with few-shot input and increase the speed of synthesizing images by 15 times through a reduction in computational complexity without using any existing acceleration modules. Compared to the state-of-the-art HumanNeRF studies HumanNeRF-SE achieves better performance with fewer learnable parameters and less training time.
Since humans interact with diverse objects every day the holistic 3D capture of these interactions is important to understand and model human behaviour. However most existing methods for hand-object reconstruction from RGB either assume pre-scanned object templates or heavily rely on limited 3D hand-object data restricting their ability to scale and generalize to more unconstrained interaction settings. To address this we introduce HOLD -- the first category-agnostic method that reconstructs an articulated hand and an object jointly from a monocular interaction video. We develop a compositional articulated implicit model that can reconstruct disentangled 3D hands and objects from 2D images. We also further incorporate hand-object constraints to improve hand-object poses and consequently the reconstruction quality. Our method does not rely on any 3D hand-object annotations while significantly outperforming fully-supervised baselines in both in-the-lab and challenging in-the-wild settings. Moreover we qualitatively show its robustness in reconstructing from in-the-wild videos. See https://github.com/zc-alexfan/hold for code data models and updates.
Most continual segmentation methods tackle the problem as a per-pixel classification task. However such a paradigm is very challenging and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones as objectness has strong transfer ability and forgetting resistance. Based on these findings we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification. CoMasTRe uses a two-stage segmenter learning class-agnostic mask proposals at the first stage and leaving recognition to the second stage. During continual learning a simple but effective distillation is adopted to strengthen objectness. To further mitigate the forgetting of old classes we design a multi-label class distillation strategy suited for segmentation. We assess the effectiveness of CoMasTRe on PASCAL VOC and ADE20K. Extensive experiments show that our method outperforms per-pixel and query-based methods on both datasets. Code will be available at https://github.com/jordangong/CoMasTRe.
In this paper we propose an accurate post-training quantization framework of diffusion models (APQ-DM) for efficient image generation. Conventional quantization frameworks learn shared quantization functions for tensor discretization regardless of the generation timesteps in diffusion models while the activation distribution differs significantly across various timesteps. Meanwhile the calibration images are acquired in random timesteps which fail to provide sufficient information for generalizable quantization function learning. Both issues cause sizable quantization errors with obvious image generation performance degradation. On the contrary we design distribution-aware quantization functions for activation discretization in different timesteps and search the optimal timesteps for informative calibration image generation so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead. Specifically we partition various timestep quantization functions into different groups according to the importance weights which are optimized by differentiable search algorithms. We also extend structural risk minimization principle for informative calibration image generation to enhance the generalization ability in the deployment of quantized diffusion model. Extensive experimental results show that our method outperforms the state-of-the-art post-training quantization of diffusion model by a sizable margin with similar computational cost.
In the evolving landscape of computer vision foundation models have emerged as pivotal tools exhibiting exceptional adaptability to a myriad of tasks. Among these the Segment Anything Model (SAM) by Meta AI has distinguished itself in image segmentation. However SAM like its counterparts encounters limitations in specific niche applications prompting a quest for enhancement strategies that do not compromise its inherent capabilities. This paper introduces ASAM a novel methodology that amplifies SAM's performance through adversarial tuning. We harness the potential of natural adversarial examples inspired by their successful implementation in natural language processing. By utilizing a stable diffusion model we augment a subset (1%) of the SA-1B dataset generating adversarial instances that are more representative of natural variations rather than conventional imperceptible perturbations. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations thereby preserving the integrity of the segmentation task. The fine-tuned ASAM demonstrates significant improvements across a diverse range of segmentation tasks without necessitating additional data or architectural modifications. The results of our extensive evaluations confirm that ASAM establishes new benchmarks in segmentation tasks thereby contributing to the advancement of foundational models in computer vision. Our project page is in https://asam2024.github.io/.
We present UniBind a flexible and efficient approach that learns a unified representation space for seven diverse modalities-- images text audio point cloud thermal video and event data. Existing works eg. ImageBind treat the image as the central modality and build an image-centered representation space; however the space may be sub-optimal as it leads to an unbalanced representation space among all modalities. Moreover the category names are directly used to extract text embeddings for the downstream tasks making it hardly possible to represent the semantics of multi-modal data. The 'out-of-the-box' insight of our UniBind is to make the alignment center modality-agnostic and further learn a unified and balanced representation space empowered by the large language models (LLMs). UniBind is superior in its flexible application to all CLIP-style models and delivers remarkable performance boosts. To make this possible we 1) construct a knowledge base of text embeddings with the help of LLMs and multi-modal LLMs; 2) adaptively build LLM-augmented class-wise embedding center on top of the knowledge base and encoded visual embeddings; 3) align all the embeddings to the LLM-augmented embedding center via contrastive learning to achieve a unified and balanced representation space.
UniBind shows strong zero-shot recognition performance gains over prior arts by an average of 6.36%. Finally we achieve new state-of-the-art performance eg. a 6.75% gain on ImageNet on the multi-modal fine-tuning setting while reducing 90% of the learnable parameters.
Category-agnostic pose estimation (CAPE) aims to predict the pose of a query image based on few support images with pose annotations. Existing methods achieve the localization of arbitrary keypoints through similarity matching between support keypoint features and query image features. However these methods primarily focus on mining information from the query images neglecting the fact that support samples with keypoint annotations contain rich category-specific fine-grained semantic information and prior structural information. In this paper we propose a Support-based Dynamic Perception Network (SDPNet) for the robust and accurate CAPE. On the one hand SDPNet models complex dependencies between support keypoints constructing category-specific prior structure to guide the interaction of query keypoints. On the other hand SDPNet extracts fine-grained semantic information from support samples dynamically modulating the refinement process of query. Our method outperforms existing methods on MP-100 dataset by a large margin.
It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods for bridging this gap such as domain adaptation (DA) may require the source data on which the model was trained (often not available) while others i.e. source-free DA require many passes through the testing data. We propose an online test-time adaptation method for depth completion the task of inferring a dense depth map from a single image and associated sparse depth map that closes the performance gap in a single pass. We first present a study on how the domain shift in each data modality affects model performance. Based on our observations that the sparse depth modality exhibits a much smaller covariate shift than the image we design an embedding module trained in the source domain that preserves a mapping from features encoding only sparse depth to those encoding image and sparse depth. During test time sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i.e. adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain.
During test time sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i.e. adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain. We evaluate our method on indoor and outdoor scenarios and show that it improves over baselines by an average of 21.1%. Code available at https://github.com/seobbro/TTA-depth-completion.