Monitoring wildlife behavior and population changes is critical for conservation efforts. However, specialized analysis of large volumes of wildlife images is extremely chal lenging, necessitating the use of artificial intelligence techniques to automatically detect, segment, and classify species captured by trap cameras. Despite the increasing use of AI in wildlife monitoring, challenges with data quality and availability persist. The Snapshot Serengeti (SS) dataset only has image-level labels and very few bounding box labels, and there's no dataset with pixel-level labels due to the significant annotation costs. To this end, we create and release the large-scale Semantic Segmentation for Snapshots of the Serengeti (S4) dataset, consisting of 24K high-quality images across 47 species with precise masks, for both common and rare species. This dataset serves as a resource for developing semantic segmentation algorithms in wildlife studies. Additionally, we introduce HitBack, a novel method leveraging Hierarchical-Semantic Cross Attention (HCA) and Background Contrast (BC) for weakly supervised semantic segmentation (WSSS). The HCA module is used to capture both the shared and distinct features across species, and the BC module is designed to enhance foreground activation by ensuring consistency in the backgrounds. Extensive experiments on the newly proposed S4benchmark show that, our HitBack presents competitive performance when compared with the state-of-the-art models. The mIoU of HitBack is +10.4%, +14.7%, and +18.4% higher than that of ToCo, SIPE, and MCTformer, respectively. In addition, our HitBack even obtains performance that surpasses the fully supervised and semi-supervised methods when annotation data is limited. Code and datasets will be available at Github.

While backpropagation effectively trains models, it presents challenges related to bio-plausibility, resulting in high memory demands and limited parallelism. Recently, Hinton (2022) proposed the Forward-Forward (FF) algorithm for high-parallel local updates. FF leverages squared sums as the local update target, termed goodness, and decouples goodness by normalizing the vector length to extract new features. However, this design encounters issues with feature scaling and deactivated neurons, limiting its application mainly to shallow networks. This paper proposes a novel goodness design utilizing **layer normalization** and **mean goodness** to overcome these challenges, demonstrating performance improvements even in 17-layer CNNs. Experiments on CIFAR-10, MNIST, and Fashion-MNIST show significant advantages over existing FF-based algorithms, highlighting the potential of FF in deep models. Furthermore, the model parallel strategy is proposed to achieve highly efficient training based on the property of local updates.

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively.

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.