The Segment Anything Model (SAM) excels at general image segmentation but has limited ability to understand natural language, which restricts its direct application in Referring Expression Segmentation (RES). Toward this end, we propose SSP-SAM, a framework that fully utilizes SAM’s segmentation capabilities by integrating a Semantic-Spatial Prompt (SSP) encoder. Specifically, we incorporate both visual and linguistic attention adapters into the SSP encoder, which highlight salient objects within the visual features and discriminative phrases within the linguistic features. This design enhances the referent representation for the prompt generator, resulting in high-quality SSPs that enable SAM to generate precise masks guided by language. Although not specifically designed for Generalized RES (GRES), where the referent may correspond to zero, one, or multiple objects, SSP-SAM naturally supports this more flexible setting without additional modifications. Extensive experiments on widely used RES and GRES benchmarks confirm the superiority of our method. Notably, our approach generates segmentation masks of high quality, achieving strong precision even at strict thresholds such as [email protected]. Further evaluation on the PhraseCut dataset demonstrates improved performance in open-vocabulary scenarios compared to existing state-of-the-art RES methods. The code and checkpoints are available at: https://github.com/WayneTomas/SSP-SAM.

Although Multimodal Large Language Models (MLLMs) excel at various image-related tasks, they encounter challenges in precisely aligning coordinates with spatial information within images, particularly in position-aware tasks such as visual grounding. This limitation arises from two key factors. First, MLLMs lack explicit spatial references, making it difficult to associate textual descriptions with precise image locations. Second, their feature extraction processes prioritize global context over fine-grained spatial details, leading to weak localization capability. To address these issues, we introduce VPP-LLaVA, an MLLM enhanced with Visual Position Prompt (VPP) to improve its grounding capability. VPP-LLaVA integrates two complementary mechanisms: the global VPP overlays a learnable, axis-like tensor onto the input image to provide structured spatial cues, while the local VPP incorporates position-aware queries to support fine-grained localization. To effectively train our model with spatial guidance, we further introduce VPP-SFT, a curated dataset of 0.6 M high-quality visual grounding samples. Designed in a compact format, it enables efficient training and is significantly smaller than datasets used by other MLLMs (e.g., 21 M samples in MiniGPT-v2), yet still provides a strong performance boost. The resulting model, VPP-LLaVA, not only achieves state-of-the-art results on standard visual grounding benchmarks but also demonstrates strong zero-shot generalization to challenging unseen datasets.

Visual grounding (VG) aims to locate a specific target in an image based on a given language query. The discriminative information from context is important for distinguishing the target from other objects, particularly for the targets that have the same category as others. However, most previous methods underestimate such information. Moreover, they are usually designed for the standard scene (without any novel object), which limits their generalization to the open-vocabulary scene. In this paper, we propose a novel framework with context disentangling and prototype inheriting for robust visual grounding to handle both scenes. Specifically, the context disentangling disentangles the referent and context features, which achieves better discrimination between them. The prototype inheriting inherits the prototypes discovered from the disentangled visual features by a prototype bank to fully utilize the seen data, especially for the open-vocabulary scene. The fused features, obtained by leveraging Hadamard product on disentangled linguistic and visual features of prototypes to avoid sharp adjusting the importance between the two types of features, are then attached with a special token and feed to a vision Transformer encoder for bounding box regression. Extensive experiments are conducted on both standard and open-vocabulary scenes. The performance comparisons indicate that our method outperforms the state-of-the-art methods in both scenarios.

The main challenge of fine-grained image hashing is how to learn highly discriminative hash codes to distinguish the within and between class variations. On the one hand, most of the existing methods treat sample pairs as equivalent in hash learning, ignoring the more discriminative information contained in hard sample pairs. On the other hand, in the testing phase, these methods ignore the influence of outliers on retrieval performance. In order to solve the above issues, this paper proposes a novel Self-Adaptive Hashing method, which learns discriminative hash codes by mining hard sample pairs, and improves retrieval performance by correcting outliers in the testing phase. In particular, to improve the discriminability of hash codes, a pair-weighted based loss function is proposed to enhance the learning of hash functions of hard sample pairs. Furthermore, in the testing phase, a self-adaptive module is proposed to discover and correct outliers by generating self-adaptive boundaries, thereby improving the retrieval performance. Experimental results on two widely-used fine-grained datasets demonstrate the effectiveness of the proposed method.