CVPR 2024 Papers

From image-text pairs large-scale vision-language models (VLMs) learn to implicitly associate image regions with words which prove effective for tasks like visual question answering. However leveraging the learned association for open-vocabulary semantic segmentation remains a challenge. In this paper we propose a simple yet extremely effective training-free technique Plug-and-Play Open-Vocabulary Semantic Segmentation (PnP-OVSS) for this task. PnP-OVSS leverages a VLM with direct text-to-image cross-attention and an image-text matching loss. To balance between over-segmentation and under-segmentation we introduce Salience Dropout; by iteratively dropping patches that the model is most attentive to we are able to better resolve the entire extent of the segmentation mask. PnP-OVSS does not require any neural network training and performs hyperparameter tuning without the need for any segmentation annotations even for a validation set. PnP-OVSS demonstrates substantial improvements over comparable baselines (+29.4% mIoU on Pascal VOC +13.2% mIoU on Pascal Context +14.0% mIoU on MS COCO +2.4% mIoU on COCO Stuff) and even outperforms most baselines that conduct additional network training on top of pretrained VLMs. Our codebase is at https://github.com/letitiabanana/PnP-OVSS.

Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world environments. Activation-based methods are a key approach in OOD detection working to mitigate overconfident predictions of OOD data. These techniques rectifying anomalous activations enhancing the distinguishability between in-distribution (ID) data and OOD data. However they assume by default that every channel is necessary for OOD detection and rectify anomalous activations in each channel. Empirical evidence has shown that there is a significant difference among various channels in OOD detection and discarding some channels can greatly enhance the performance of OOD detection. Based on this insight we propose \underline D iscriminability-\underline D riven \underline C hannel \underline S election (DDCS) which leverages an adaptive channel selection by estimating the discriminative score of each channel to boost OOD detection. The discriminative score takes inter-class similarity and inter-class variance of training data into account. However the estimation of discriminative score itself is susceptible to anomalous activations. To better estimate score we pre-rectify anomalous activations for each channel mildly. The experimental results show that DDCS achieves state-of-the-art performance on CIFAR and ImageNet-1K benchmarks. Moreover DDCS can generalize to different backbones and OOD scores.

Existing open-vocabulary object detectors typically require a predefined set of categories from users significantly confining their application scenarios. In this paper we introduce DetCLIPv3 a high-performing detector that excels not only at both open-vocabulary object detection but also generating hierarchical labels for detected objects. DetCLIPv3 is characterized by three core designs: 1. Versatile model architecture: we derive a robust open-set detection framework which is further empowered with generation ability via the integration of a caption head. 2. High information density data: we develop an auto-annotation pipeline leveraging visual large language model to refine captions for large-scale image-text pairs providing rich multi-granular object labels to enhance the training. 3. Efficient training strategy: we employ a pre-training stage with low-resolution inputs that enables the object captioner to efficiently learn a broad spectrum of visual concepts from extensive image-text paired data. This is followed by a fine-tuning stage that leverages a small number of high-resolution samples to further enhance detection performance. With these effective designs DetCLIPv3 demonstrates superior open-vocabulary detection performance e.g. our Swin-T backbone model achieves a notable 47.0 zero-shot fixed AP on the LVIS minival benchmark outperforming GLIPv2 GroundingDINO and DetCLIPv2 by 18.0/19.6/6.6 AP respectively. DetCLIPv3 also achieves a state-of-the-art 19.7 AP in dense captioning task on VG dataset showcasing its strong generative capability.

We train a set of open text-to-image (T2I) diffusion models on a dataset of curated Creative-Commons-licensed (CC) images which yields models that are competitive with Stable Diffusion 2 (SD2). This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train T2I models; (2) CC images are relatively scarce. To address these challenges we use an intuitive transfer learning technique to produce a set of high-quality synthetic captions paired with our assembled CC images. We then develop a data- and compute-efficient training recipe that requires as little as 3% of the LAION data (i.e. roughly 70 million examples) needed to train existing SD2 models but obtains the same quality. These results indicate that we have a sufficient number of CC images (also roughly 70 million) for training high-quality models. Our recipe also implements a variety of optimizations that achieve 2.71x training speed-ups enabling rapid model iteration. We leverage this recipe to train several high-quality T2I mod- els which we dub the CommonCanvas family. Our largest model achieves comparable performance to SD2 on human evaluation even though we use a synthetically captioned CC-image dataset that is only <3% the size of LAION for training. We release our models data and code on GitHub.

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.

Deep neural networks (DNNs) often display overconfidence when encountering out-of-distribution (OOD) samples posing significant challenges in real-world applications. Capitalizing on the observation that responses on convolutional kernels are generally more pronounced for in-distribution (ID) samples than for OOD ones this paper proposes the COnvolutional REsponse-based Score (CORES) to exploit these discrepancies for OOD detection. Initially CORES delves into the extremities of convolutional responses by considering both their magnitude and the frequency of significant values. Moreover through backtracking from the most prominent predictions CORES effectively pinpoints sample-relevant kernels across different layers. These kernels which exhibit a strong correlation to input samples are integral to CORES's OOD detection capability. Comprehensive experiments across various ID and OOD settings demonstrate CORES's effectiveness in OOD detection and its superiority to the state-of-the-art methods.

The rebroadcasting of screen-recaptured document images introduces a significant risk to the confidential documents processed in government departments and commercial companies. However detecting recaptured document images subjected to distortions from online social networks (OSNs) is challenging since the common forensics cues such as moir ?e pattern are weakened during transmission. In this work we first devise a pixel-level distortion model of the screen-recaptured document image to identify the robust features of color artifacts. Then we extract a chromaticity map from the recaptured image to highlight the presence of color artifacts even under low-quality samples. Based on the prior understanding we design a chromaticity map adapter (CMA) to efficiently extract the chromaticity map and feed it into the transformer backbone as multi-modal prompt tokens. To evaluate the performance of the proposed method we collect a recaptured office document image dataset with over 10K diverse samples. Experimental results demonstrate that the proposed CMA method outperforms a SOTA approach (with RGB modality only) reducing the average EER from 26.82% to 16.78%. Robustness evaluation shows that our method achieves 0.8688 and 0.7554 AUCs under samples with JPEG compression (QF=70) and resolution as low as 534x503 pixels.

3D Scene Graph Generation (3DSGG) aims to classify objects and their predicates within 3D point cloud scenes. However current 3DSGG methods struggle with two main challenges. 1) The dependency on labor-intensive ground-truth annotations. 2) Closed-set classes training hampers the recognition of novel objects and predicates. Addressing these issues our idea is to extract cross-modality features by CLIP from text and image data naturally related to 3D point clouds. Cross-modality features are used to train a robust 3D scene graph (3DSG) feature extractor. Specifically we propose a novel Cross-Modality Contrastive Learning 3DSGG (CCL-3DSGG) method. Firstly to align the text with 3DSG the text is parsed into word level that are consistent with the 3DSG annotation. To enhance robustness during the alignment adjectives are exchanged for different objects as negative samples. Then to align the image with 3DSG the camera view is treated as a positive sample and other views as negatives. Lastly the recognition of novel object and predicate classes is achieved by calculating the cosine similarity between prompts and 3DSG features. Our rigorous experiments confirm the superior open-vocabulary capability and applicability of CCL-3DSGG in real-world contexts.

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions. In this work we introduce a novel cost-based approach to adapt vision-language foundation models notably CLIP for the intricate task of semantic segmentation. Through aggregating the cosine similarity score i.e. the cost volume between image and text embeddings our method potently adapts CLIP for segmenting seen and unseen classes by fine-tuning its encoders addressing the challenges faced by existing methods in handling unseen classes. Building upon this we explore methods to effectively aggregate the cost volume considering its multi-modal nature of being established between image and text embeddings. Furthermore we examine various methods for efficiently fine-tuning CLIP.

When deploying segmentation models in practice it is critical to evaluate their behaviors in varied and complex scenes. Different from the previous evaluation paradigms only in consideration of global attribute variations (e.g. adverse weather) we investigate both local and global attribute variations for robustness evaluation. To achieve this we construct a mask-preserved attribute editing pipeline to edit visual attributes of real images with precise control of structural information. Therefore the original segmentation labels can be reused for the edited images. Using our pipeline we construct a benchmark covering both object and image attributes (e.g. color material pattern style). We evaluate a broad variety of semantic segmentation models spanning from conventional close-set models to recent open-vocabulary large models on their robustness to different types of variations. We find that both local and global attribute variations affect segmentation performances and the sensitivity of models diverges across different variation types. We argue that local attributes have the same importance as global attributes and should be considered in the robustness evaluation of segmentation models. Code: https://github.com/PRIS-CV/Pascal-EA.

With the immense growth of dataset sizes and computing resources in recent years so-called foundation models have become popular in NLP and vision tasks. In this work we propose to explore foundation models for the task of keypoint detection on 3D shapes. A unique characteristic of keypoint detection is that it requires semantic and geometric awareness while demanding high localization accuracy. To address this problem we propose first to back-project features from large pre-trained 2D vision models onto 3D shapes and employ them for this task. We show that we obtain robust 3D features that contain rich semantic information and analyze multiple candidate features stemming from different 2D foundation models. Second we employ a keypoint candidate optimization module which aims to match the average observed distribution of keypoints on the shape and is guided by the back-projected features. The resulting approach achieves a new state of the art for few-shot keypoint detection on the KeyPointNet dataset almost doubling the performance of the previous best methods.

Traditional approaches in physics-based motion generation centered around imitation learning and reward shaping often struggle to adapt to new scenarios. To tackle this limitation we propose AnySkill a novel hierarchical method that learns physically plausible interactions following open-vocabulary instructions. Our approach begins by developing a set of atomic actions via a low-level controller trained via imitation learning. Upon receiving an open-vocabulary textual instruction AnySkill employs a high-level policy that selects and integrates these atomic actions to maximize the CLIP similarity between the agent's rendered images and the text. An important feature of our method is the use of image-based rewards for the high-level policy which allows the agent to learn interactions with objects without manual reward engineering. We demonstrate AnySkill's capability to generate realistic and natural motion sequences in response to unseen instructions of varying lengths marking it the first method capable of open-vocabulary physical skill learning for interactive humanoid agents.

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e. samples from open-set anomaly classes) while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies current OSAD methods can often largely reduce false positive errors. However these methods are trained in a closed-set setting and treat the anomaly examples as from a homogeneous distribution rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. This paper proposes to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end we introduce a novel approach namely Anomaly Heterogeneity Learning (AHL) that simulates a diverse set of heterogeneous anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model in surrogate open-set environments. Further AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies and 2) effectively generalize to unseen anomalies in new domains. Code is available at https://github.com/mala-lab/AHL.

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.

With recent text-to-image models anyone can generate deceptively realistic images with arbitrary contents fueling the growing threat of visual disinformation. A key enabler for generating high-resolution images with low computational cost has been the development of latent diffusion models (LDMs). In contrast to conventional diffusion models LDMs perform the denoising process in the low-dimensional latent space of a pre-trained autoencoder (AE) instead of the high-dimensional image space. Despite their relevance the forensic analysis of LDMs is still in its infancy. In this work we propose AEROBLADE a novel detection method which exploits an inherent component of LDMs: the AE used to transform images between image and latent space. We find that generated images can be more accurately reconstructed by the AE than real images allowing for a simple detection approach based on the reconstruction error. Most importantly our method is easy to implement and does not require any training yet nearly matches the performance of detectors that rely on extensive training. We empirically demonstrate that AEROBLADE is effective against state-of-the-art LDMs including Stable Diffusion and Midjourney. Beyond detection our approach allows for the qualitative analysis of images which can be leveraged for identifying inpainted regions. We release our code and data at https://github.com/jonasricker/aeroblade.