CVPR 2024 论文列表

Diffusion models have recently achieved remarkable progress in generating realistic images. However challenges remain in accurately understanding and synthesizing the layout requirements in the textual prompts. To align the generated image with layout instructions we present a training-free layout calibration system SimM that intervenes in the generative process on the fly during inference time. Specifically following a "check-locate-rectify" pipeline the system first analyses the prompt to generate the target layout and compares it with the intermediate outputs to automatically detect errors. Then by moving the located activations and making intra- and inter-map adjustments the rectification process can be performed with negligible computational overhead. To evaluate SimM over a range of layout requirements we present a benchmark SimMBench that compensates for the lack of superlative spatial relations in existing datasets. And both quantitative and qualitative results demonstrate the effectiveness of the proposed SimM in calibrating the layout inconsistencies. Our project page is at https://simm-t2i.github.io/SimM.

We present ChatScene a Large Language Model (LLM)-based agent that leverages the capabilities of LLMs to generate safety-critical scenarios for autonomous vehicles. Given unstructured language instructions the agent first generates textually described traffic scenarios using LLMs. These scenario descriptions are subsequently broken down into several sub-descriptions for specified details such as behaviors and locations of vehicles. The agent then distinctively transforms the textually described sub-scenarios into domain-specific languages which then generate actual code for prediction and control in simulators facilitating the creation of diverse and complex scenarios within the CARLA simulation environment. A key part of our agent is a comprehensive knowledge retrieval component which efficiently translates specific textual descriptions into corresponding domain-specific code snippets by training a knowledge database containing the scenario description and code pairs. Extensive experimental results underscore the efficacy of ChatScene in improving the safety of autonomous vehicles. For instance the scenarios generated by ChatScene show a 15% increase in collision rates compared to state-of-the-art baselines when tested against different reinforcement learning-based ego vehicles. Furthermore we show that by using our generated safety-critical scenarios to fine-tune different RL-based autonomous driving models they can achieve a 9% reduction in collision rates surpassing current SOTA methods. ChatScene effectively bridges the gap between textual descriptions of traffic scenarios and practical CARLA simulations providing a unified way to conveniently generate safety-critical scenarios for safety testing and improvement for AVs.

We introduce ChatPose a framework employing Large Language Models (LLMs) to understand and reason about 3D human poses from images or textual descriptions. Our work is motivated by the human ability to intuitively understand postures from a single image or a brief description a process that intertwines image interpretation world knowledge and an understanding of body language. Traditional human pose estimation and generation methods often operate in isolation lacking semantic understanding and reasoning abilities. ChatPose addresses these limitations by embedding SMPL poses as distinct signal tokens within a multimodal LLM enabling the direct generation of 3D body poses from both textual and visual inputs. Leveraging the powerful capabilities of multimodal LLMs ChatPose unifies classical 3D human pose and generation tasks while offering user interactions. Additionally ChatPose empowers LLMs to apply their extensive world knowledge in reasoning about human poses leading to two advanced tasks: speculative pose generation and reasoning about pose estimation. These tasks involve reasoning about humans to generate 3D poses from subtle text queries possibly accompanied by images. We establish benchmarks for these tasks moving beyond traditional 3D pose generation and estimation methods. Our results show that ChatPose out-performs existing multimodal LLMs and task-specific methods on these newly proposed tasks. Furthermore ChatPose's ability to understand and generate 3D human poses based on complex reasoning opens new directions in human pose analysis. Code and data are available for research at https://yfeng95.github.io/ChatPose.

Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversations. However existing methods encounter challenges in effectively handling both image and video understanding particularly with limited visual tokens. In this work we introduce Chat-UniVi a Unified Vision-language model capable of comprehending and engaging in conversations involving images and videos through a unified visual representation. Specifically we employ a set of dynamic visual tokens to uniformly represent images and videos. This representation framework empowers the model to efficiently utilize a limited number of visual tokens to simultaneously capture the spatial details necessary for images and the comprehensive temporal relationship required for videos. Moreover we leverage a multi-scale representation enabling the model to perceive both high-level semantic concepts and low-level visual details. Notably Chat-UniVi is trained on a mixed dataset containing both images and videos allowing direct application to tasks involving both mediums without requiring any modifications. Extensive experimental results demonstrate that Chat-UniVi consistently outperforms even existing methods exclusively designed for either images or videos. Code is available at https://github.com/PKU-YuanGroup/Chat-UniVi.

The rapidly growing scale of data in practice poses demands on the efficiency of retrieval models. However for fine-grained image retrieval task there are inherent contradictions in the design of hashing based efficient models. Firstly the limited information embedding capacity of low-dimensional binary hash codes coupled with the detailed information required to describe fine-grained categories results in a contradiction in feature learning. Secondly there is also a contradiction between the complexity of fine-grained feature extraction models and retrieval efficiency. To address these issues in this paper we propose the characteristics matching based hash codes generation method. Coupled with the cross-layer semantic information transfer module and the multi-region feature embedding module the proposed method can generate hash codes that effectively capture fine-grained differences among samples while ensuring efficient inference. Extensive experiments on widely used datasets demonstrate that our method can significantly outperform state-of-the-art methods.

Unlike color photography images which are consistently encoded into RGB channels biological images encompass various modalities where the type of microscopy and the meaning of each channel varies with each experiment. Importantly the number of channels can range from one to a dozen and their correlation is often comparatively much lower than RGB as each of them brings specific information content. This aspect is largely overlooked by methods designed out of the bioimage field and current solutions mostly focus on intra-channel spatial attention often ignoring the relationship between channels yet crucial in most biological applications. Importantly the variable channel type and count prevent the projection of several experiments to a unified representation for large scale pre-training. In this study we propose ChAda-ViT a novel Channel Adaptive Vision Transformer architecture employing an Inter-Channel Attention mechanism on images with an arbitrary number order and type of channels. We also introduce IDRCell100k a bioimage dataset with a rich set of 79 experiments covering 7 microscope modalities with a multitude of channel types and channel counts varying from 1 to 10 per experiment. 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.

Deep neural networks have demonstrated remarkable performance in point cloud classification. However previous works show they are vulnerable to adversarial perturbations that can manipulate their predictions. Given the distinctive modality of point clouds various attack strategies have emerged posing challenges for existing defenses to achieve effective generalization. In this study we for the first time introduce causal modeling to enhance the robustness of point cloud classification models. Our insight is from the observation that adversarial examples closely resemble benign point clouds from the human perspective. In our causal modeling we incorporate two critical variables the structural information (standing for the key feature leading to the classification) and the hidden confounders (standing for the noise interfering with the classification). The resulting overall framework CausalPC consists of three sub-modules to identify the causal effect for robust classification. The framework is model-agnostic and adaptable for integration with various point cloud classifiers. Our approach significantly improves the adversarial robustness of three mainstream point cloud classification models on two benchmark datasets. For instance the classification accuracy for DGCNN on ModelNet40 increases from 29.2% to 72.0% with CausalPC whereas the best-performing baseline achieves only 42.4%.

While Multi-modal Language Models (MLMs) demon strate impressive multimodal ability they still struggle on providing factual and precise responses for tasks like vi sual question answering (VQA). In this paper we address this challenge from the perspective of contextual informa tion. We propose Causal Context Generation Causal-CoG which is a prompting strategy that engages contextual infor mation to enhance precise VQA during inference. Specifi cally we prompt MLMs to generate contexts i.e text de scription of an image and engage the generated contexts for question answering. Moreover we investigate the ad vantage of contexts on VQA from a causality perspective introducing causality filtering to select samples for which contextual information is helpful. To show the effective ness of Causal-CoG we run extensive experiments on 10 multimodal benchmarks and showconsistent improvements e.g. +6.30% on POPE +13.69% on Vizwiz and +6.43% on VQAv2 compared to direct decoding surpassing exist ing methods. We hope Casual-CoG inspires explorations of context knowledge in multimodal models and serves as a plug-and-play strategy for MLM decoding.

RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically datasets in multispectral pedestrian detection mainly distribute between ROTO (day) and RXTO (night) data; the majority of the pedestrian labels statistically co-occur with their thermal features. As a result multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation such as ROTX data. To address this problem we propose a novel Causal Mode Multiplexer (CMM) framework that effectively learns the causalities between multispectral inputs and predictions. Moreover we construct a new dataset (ROTX-MP) to evaluate modality bias in multispectral pedestrian detection. ROTX-MP mainly includes ROTX examples not presented in previous datasets. Extensive experiments demonstrate that our proposed CMM framework generalizes well on existing datasets (KAIST CVC-14 FLIR) and the new ROTX-MP. Our code and dataset are available open-source.

Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focus on geometry reasoning and neglect the actual physical assembly process of matching and fitting joints which are the contact surfaces connecting different parts. In this paper we consider contacting joints for the task of multi-part assembly. A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment. We propose a hierarchical graph learning approach composed of two levels of graph representation learning. The part graph takes part geometries as input to build the desired shape structure. The joint-level graph uses part joints information and focuses on matching and aligning joints. The two kinds of information are combined to achieve the bilateral objectives. Extensive experiments demonstrate that our method outperforms previous methods achieving better shape structure and higher joint alignment accuracy.

Multi-view diffusion models obtained by applying Supervised Finetuning (SFT) to text-to-image diffusion models have driven recent breakthroughs in text-to-3D research. However due to the limited size and quality of existing 3D datasets they still suffer from multi-view inconsistencies and Neural Radiance Field (NeRF) reconstruction artifacts. We argue that multi-view diffusion models can benefit from further Reinforcement Learning Finetuning (RLFT) which allows models to learn from the data generated by themselves and improve beyond their dataset limitations during SFT. To this end we introduce Carve3D an improved RLFT algorithm coupled with a novel Multi-view Reconstruction Consistency (MRC) metric to enhance the consistency of multi-view diffusion models. To measure the MRC metric on a set of multi-view images we compare them with their corresponding NeRF renderings at the same camera viewpoints. The resulting model which we denote as Carve3DM demonstrates superior multi-view consistency and NeRF reconstruction quality than existing models. Our results suggest that pairing SFT with Carve3D's RLFT is essential for developing multi-view-consistent diffusion models mirroring the standard Large Language Model (LLM) alignment pipeline. Our code training and testing data and video results are available at: https://desaixie.github.io/carve-3d.

In this paper we focus on capturing closely interacted two-person motions from monocular videos an important yet understudied topic. Unlike less-interacted motions closely interacted motions contain frequently occurring inter-human occlusions which pose significant challenges to existing capturing algorithms. To address this problem our key observation is that close physical interactions between two subjects typically happen under very specific situations (e.g. handshake hug etc.) and such situational contexts contain strong prior semantics to help infer the poses of occluded joints. In this spirit we introduce reaction priors which are invertible neural networks that bi-directionally model the pose probability distributions of one person given the pose of the other. The learned reaction priors are then incorporated into a query-based pose estimator which is a decoder-only Transformer with self-attentions on both intra-joint and inter-joint relationships. We demonstrate that our design achieves considerably higher performance than previous methods on multiple benchmarks. What's more as existing datasets lack sufficient cases of close human-human interactions we also build a new dataset called Dual-Human to better evaluate different methods. Dual-Human contains around 2k sequences of closely interacted two-person motions each with synthetic multi-view renderings contact annotations and text descriptions. We believe that this new public dataset can significantly promote further research in this area.

Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success but suffer from excessive noise. Recent studies use alternative captions synthesized by captioning models and have achieved notable benchmark performance. However our experiments reveal significant Scalability Deficiency and World Knowledge Loss issues in models trained with synthetic captions which have been largely obscured by their initial benchmark success. Upon closer examination we identify the root cause as the overly-simplified language structure and lack of knowledge details in existing synthetic captions. To provide higher-quality and more scalable multimodal pretraining data we propose CapsFusion an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions. Extensive experiments show that CapsFusion captions exhibit remarkable all-round superiority over existing captions in terms of model performance (e.g. 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps) sample efficiency (requiring 11-16 times less computation than baselines) world knowledge depth and scalability. These effectiveness efficiency and scalability advantages position CapsFusion as a promising candidate for future scaling of LMM training.

We concentrate on a novel human-centric image synthesis task that is given only one reference facial photograph it is expected to generate specific individual images with diverse head positions poses facial expressions and illuminations in different contexts. To accomplish this goal we argue that our generative model should be capable of the following favorable characteristics: (1) a strong visual and semantic understanding of our world and human society for basic object and human image generation. (2) generalizable identity preservation ability. (3) flexible and fine-grained head control. Recently large pre-trained text-to-image diffusion models have shown remarkable results serving as a powerful generative foundation. As a basis we aim to unleash the above two capabilities of the pre-trained model. In this work we present a new framework named CapHuman. We embrace the "encode then learn to align" paradigm which enables generalizable identity preservation for new individuals without cumbersome tuning at inference. CapHuman encodes identity features and then learns to align them into the latent space. Moreover we introduce the 3D facial prior to equip our model with control over the human head in a flexible and 3D-consistent manner. Extensive qualitative and quantitative analyses demonstrate our CapHuman can produce well-identity-preserved photo-realistic and high-fidelity portraits with content-rich representations and various head renditions superior to established baselines. Code and checkpoint will be released at https://github.com/VamosC/CapHuman.

We introduce PlausiVL a large video-language model for anticipating action sequences that are plausible in the real-world. While significant efforts have been made towards anticipating future actions prior approaches do not take into account the aspect of plausibility in an action sequence. To address this limitation we explore the generative capability of a large video-language model in our work and further develop the understanding of plausibility in an action sequence by introducing two objective functions a counterfactual-based plausible action sequence learning loss and a long-horizon action repetition loss. We utilize temporal logical constraints as well as verb-noun action pair logical constraints to create implausible/counterfactual action sequences and use them to train the model with plausible action sequence learning loss. This loss helps the model to differentiate between plausible and not plausible action sequences and also helps the model to learn implicit temporal cues crucial for the task of action anticipation. The long-horizon action repetition loss puts a higher penalty on the actions that are more prone to repetition over a longer temporal window. With this penalization the model is able to generate diverse plausible action sequences. We evaluate our approach on two large-scale datasets Ego4D and EPIC-Kitchens-100 and show improvements on the task of action anticipation.

Stable Diffusion has established itself as a foundation model in generative AI artistic applications receiving widespread research and application. Some recent fine-tuning methods have made it feasible for individuals to implant personalized concepts onto the basic Stable Diffusion model with minimal computational costs on small datasets. However these innovations have also given rise to issues like facial privacy forgery and artistic copyright infringement. In recent studies researchers have explored the addition of imperceptible adversarial perturbations to images to prevent potential unauthorized exploitation and infringements when personal data is used for fine-tuning Stable Diffusion. Although these studies have demonstrated the ability to protect images it is essential to consider that these methods may not be entirely applicable in real-world scenarios. In this paper we systematically evaluate the use of perturbations to protect images within a practical threat model. The results suggest that these approaches may not be sufficient to safeguard image privacy and copyright effectively. Furthermore we introduce a purification method capable of removing protected perturbations while preserving the original image structure to the greatest extent possible. Experiments reveal that Stable Diffusion can effectively learn from purified images over all protective methods.

Language models have demonstrated impressive ability in context understanding and generative performance. Inspired by the recent success of language foundation models in this paper we propose LMTraj (Language-based Multimodal Trajectory predictor) which recasts the trajectory prediction task into a sort of question-answering problem. Departing from traditional numerical regression models which treat the trajectory coordinate sequence as continuous signals we consider them as discrete signals like text prompts. Specially we first transform an input space for the trajectory coordinate into the natural language space. Here the entire time-series trajectories of pedestrians are converted into a text prompt and scene images are described as text information through image captioning. The transformed numerical and image data are then wrapped into the question-answering template for use in a language model. Next to guide the language model in understanding and reasoning high-level knowledge such as scene context and social relationships between pedestrians we introduce an auxiliary multi-task question and answering. We then train a numerical tokenizer with the prompt data. We encourage the tokenizer to separate the integer and decimal parts well and leverage it to capture correlations between the consecutive numbers in the language model. Lastly we train the language model using the numerical tokenizer and all of the question-answer prompts. Here we propose a beam-search-based most-likely prediction and a temperature-based multimodal prediction to implement both deterministic and stochastic inferences. Applying our LMTraj we show that the language-based model can be a powerful pedestrian trajectory predictor and outperforms existing numerical-based predictor methods. Extensive experiments show that our LMTraj can successfully understand social relationships and accurately extrapolate the multimodal futures on the public pedestrian trajectory prediction benchmark. Code is publicly available at https://github.com/inhwanbae/LMTrajectory.

We study visually grounded VideoQA in response to the emerging trends of utilizing pretraining techniques for video- language understanding. Specifically by forcing vision- language models (VLMs) to answer questions and simultane- ously provide visual evidence we seek to ascertain the extent to which the predictions of such techniques are genuinely anchored in relevant video content versus spurious corre- lations from language or irrelevant visual context. Towards this we construct NExT-GQA - an extension of NExT-QA with 10.5K temporal grounding (or location) labels tied to the original QA pairs. With NExT-GQA we scrutinize a series of state-of-the-art VLMs. Through post-hoc atten- tion analysis we find that these models are extremely weak in substantiating the answers despite their strong QA per- formance. This exposes the limitation of current VLMs in making reliable predictions. As a remedy we further explore and propose a grounded-QA method via Gaussian mask optimization and cross-modal learning. Experiments with different backbones demonstrate that this grounding mechanism improves both grounding and QA. With these efforts we aim to push towards trustworthy VLMs in VQA systems. Our dataset and code are available at https://github.com/doc-doc/NExT-GQA.

The robust generalization of models to rare in-distribution (ID) samples drawn from the long tail of the training distribution and to out-of-training-distribution (OOD) samples is one of the major challenges of current deep learning methods. For image classification this manifests in the existence of adversarial attacks the performance drops on distorted images and a lack of generalization to concepts such as sketches. The current understanding of generalization in neural networks is very limited but some biases that differentiate models from human vision have been identified and might be causing these limitations. Consequently several attempts with varying success have been made to reduce these biases during training to improve generalization. We take a step back and sanity-check these attempts. Fixing the architecture to the well-established ResNet-50 we perform a large-scale study on 48 ImageNet models obtained via different training methods to understand how and if these biases - including shape bias spectral biases and critical bands - interact with generalization. Our extensive study results reveal that contrary to previous findings these biases are insufficient to accurately predict the generalization of a model holistically. We provide access to all checkpoints and evaluation code at https://github.com/paulgavrikov/biases_vs_generalization/